Publications by authors named "Subramanian K R S Sankaranarayanan"

72 Publications

Learning with Delayed Rewards-A Case Study on Inverse Defect Design in 2D Materials.

ACS Appl Mater Interfaces 2021 Aug 21;13(30):36455-36464. Epub 2021 Jul 21.

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS, we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS are discussed. Overall, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.
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http://dx.doi.org/10.1021/acsami.1c07545DOI Listing
August 2021

One-Dimensional Lateral Force Anisotropy at the Atomic Scale in Sliding Single Molecules on a Surface.

Nano Lett 2021 Aug 20;21(15):6391-6397. Epub 2021 Jul 20.

Center for Nanoscale Materials, Nanoscience and Technology Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.

Using a q+ atomic force microscopy at low temperature, a sexiphenyl molecule is slid across an atomically flat Ag(111) surface along the direction parallel to its molecular axis and sideways to the axis. Despite identical contact area and underlying surface geometry, the lateral force required to move the molecule in the direction parallel to its molecular axis is found to be about half of that required to move it sideways. The origin of the lateral force anisotropy observed here is traced to the one-dimensional shape of the molecule, which is further confirmed by molecular dynamics simulations. We also demonstrate that scanning tunneling microscopy can be used to determine the comparative lateral force qualitatively. The observed one-dimensional lateral force anisotropy may have important implications in atomic scale frictional phenomena on materials surfaces.
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http://dx.doi.org/10.1021/acs.nanolett.0c04974DOI Listing
August 2021

High-Entropy 2D Carbide MXenes: TiVNbMoC and TiVCrMoC.

ACS Nano 2021 Jun 15. Epub 2021 Jun 15.

Department of Mechanical and Energy Engineering, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, are a fast-growing family of 2D materials. MXenes 2D flakes have + 1 ( = 1-4) atomic layers of transition metals interleaved by carbon/nitrogen layers, but to-date remain limited in composition to one or two transition metals. In this study, by implementing four transition metals, we report the synthesis of multi-principal-element high-entropy MCT MXenes. Specifically, we introduce two high-entropy MXenes, TiVNbMoCT and TiVCrMoCT, as well as their precursor TiVNbMoAlC and TiVCrMoAlC high-entropy MAX phases. We used a combination of real and reciprocal space characterization (X-ray diffraction, X-ray photoelectron spectroscopy, energy dispersive X-ray spectroscopy, and scanning transmission electron microscopy) to establish the structure, phase purity, and equimolar distribution of the four transition metals in high-entropy MAX and MXene phases. We use first-principles calculations to compute the formation energies and explore synthesizability of these high-entropy MAX phases. We also show that when three transition metals are used instead of four, under similar synthesis conditions to those of the four-transition-metal MAX phase, two different MAX phases can be formed (.., no pure single-phase forms). This finding indicates the importance of configurational entropy in stabilizing the desired single-phase high-entropy MAX over multiphases of MAX, which is essential for the synthesis of phase-pure high-entropy MXenes. The synthesis of high-entropy MXenes significantly expands the compositional variety of the MXene family to further tune their properties, including electronic, magnetic, electrochemical, catalytic, high temperature stability, and mechanical behavior.
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http://dx.doi.org/10.1021/acsnano.1c02775DOI Listing
June 2021

Artificial Intelligence-Guided Molecular Design Targeting COVID-19.

ACS Omega 2021 May 4;6(19):12557-12566. Epub 2021 May 4.

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. While computational docking simulations remain a popular method of choice for the ligand design and high-throughput screening of therapeutic agents, it is severely limited in the discovery of new candidate ligands owing to the high computational cost and vast chemical space. Here, we present a molecular design strategy that leverages artificial intelligence (AI) to discover new therapeutic agents against SARS-CoV-2. A Monte Carlo tree search algorithm combined with a multitask neural network surrogate model for expensive docking simulations, and recurrent neural networks for rollouts, is used in an iterative search and retrain strategy. Using Vina scores as the target objective to measure binding to either the isolated spike protein (S-protein) at its host receptor region or to the S-protein/angiotensin converting enzyme 2 receptor interface, we generate several (∼100's) new therapeutic agents that outperform Food and Drug Administration (FDA) (∼1000's) and non-FDA molecules (∼million). Our AI strategy is broadly applicable for accelerated design and discovery of chemical molecules with any user-desired functionality.
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http://dx.doi.org/10.1021/acsomega.1c00477DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154149PMC
May 2021

Bragg Coherent Diffraction Imaging for Studies in Electrocatalysis.

ACS Nano 2021 Apr 1;15(4):6129-6146. Epub 2021 Apr 1.

Chemistry Institute, State University of Campinas, 13083-970 Campinas, São Paulo, Brazil.

Electrocatalysis is at the heart of a broad range of physicochemical applications that play an important role in the present and future of a sustainable economy. Among the myriad of different electrocatalysts used in this field, nanomaterials are of ubiquitous importance. An increased surface area/volume ratio compared to bulk makes nanoscale catalysts the preferred choice to perform electrocatalytic reactions. Bragg coherent diffraction imaging (BCDI) was introduced in 2006 and since has been applied to obtain 3D images of crystalline nanomaterials. BCDI provides information about the displacement field, which is directly related to strain. Lattice strain in the catalysts impacts their electronic configuration and, consequently, their binding energy with reaction intermediates. Even though there have been significant improvements since its birth, the fact that the experiments can only be performed at synchrotron facilities and its relatively low resolution to date (∼10 nm spatial resolution) have prevented the popularization of this technique. Herein, we will briefly describe the fundamentals of the technique, including the electrocatalysis relevant information that we can extract from it. Subsequently, we review some of the computational experiments that complement the BCDI data for enhanced information extraction and improved understanding of the underlying nanoscale electrocatalytic processes. We next highlight success stories of BCDI applied to different electrochemical systems and in heterogeneous catalysis to show how the technique can contribute to future studies in electrocatalysis. Finally, we outline current challenges in spatiotemporal resolution limits of BCDI and provide our perspectives on recent developments in synchrotron facilities as well as the role of machine learning and artificial intelligence in addressing them.
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http://dx.doi.org/10.1021/acsnano.1c01080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155327PMC
April 2021

Accelerating copolymer inverse design using monte carlo tree search.

Nanoscale 2020 Dec;12(46):23653-23662

Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.

There exists a broad class of sequencing problems in soft materials such as proteins and polymers that can be formulated as a heuristic search that involves decision making akin to a computer game. AI gaming algorithms such as Monte Carlo tree search (MCTS) gained prominence after their exemplary performance in the computer Go game and are decision trees aimed at identifying the path (moves) that should be taken by the policy to reach the final winning or optimal solution. Major challenges in inverse sequencing problems are that the materials search space is extremely vast and property evaluation for each sequence is computationally demanding. Reaching an optimal solution by minimizing the total number of evaluations in a given design cycle is therefore highly desirable. We demonstrate that one can adopt this approach for solving the sequencing problem by developing and growing a decision tree, where each node in the tree is a candidate sequence whose fitness is directly evaluated by molecular simulations. We interface MCTS with MD simulations and use a representative example of designing a copolymer compatibilizer, where the goal is to identify sequence specific copolymers that lead to zero interfacial energy between two immiscible homopolymers. We apply the MCTS algorithm to polymer chain lengths varying from 10-mer to 30-mer, wherein the overall search space varies from 210 (1024) to 230 (∼1 billion). In each case, we identify a target sequence that leads to zero interfacial energy within a few hundred evaluations demonstrating the scalability and efficiency of MCTS in exploring practical materials design problems with exceedingly vast chemical/material search space. Our MCTS-MD framework can be easily extended to several other polymer and protein inverse design problems, in particular, for cases where sequence-property data is either unavailable and/or is resource intensive.
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http://dx.doi.org/10.1039/d0nr06091gDOI Listing
December 2020

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies.

J Phys Chem Lett 2020 Sep 14;11(17):7058-7065. Epub 2020 Aug 14.

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors (χ, topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.
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http://dx.doi.org/10.1021/acs.jpclett.0c02278DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430156PMC
September 2020

Creation of Single-Photon Emitters in WSe Monolayers Using Nanometer-Sized Gold Tips.

Nano Lett 2020 Aug 16;20(8):5866-5872. Epub 2020 Jul 16.

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.

Due to their tunable bandgaps and strong spin-valley locking, transition metal dichalcogenides constitute a unique platform for hosting single-photon emitters. Here, we present a versatile approach for creating bright single-photon emitters in WSe monolayers by the deposition of gold nanostars. Our molecular dynamics simulations reveal that the formation of the quantum emitters is likely caused by the highly localized strain fields created by the sharp tips of the gold nanostars. The surface plasmon modes supported by the gold nanostars can change the local electromagnetic fields in the vicinity of the quantum emitters, leading to their enhanced emission intensities. Moreover, by correlating the emission energies and intensities of the quantum emitters, we are able to associate them with two types of strain fields and derive the existence of a low-lying dark state in their electronic structures. Our findings are highly relevant for the development and understanding of single-photon emitters in transition metal dichalcogenide materials.
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http://dx.doi.org/10.1021/acs.nanolett.0c01789DOI Listing
August 2020

Perovskite neural trees.

Nat Commun 2020 05 7;11(1):2245. Epub 2020 May 7.

School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA.

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.
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http://dx.doi.org/10.1038/s41467-020-16105-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206050PMC
May 2020

Slippery and Wear-Resistant Surfaces Enabled by Interface Engineered Graphene.

Nano Lett 2020 Feb 9;20(2):905-917. Epub 2020 Jan 9.

Department of Electrical and Computer Engineering , National University of Singapore , Singapore 117583 , Republic of Singapore.

Friction and wear remain the primary cause of mechanical energy dissipation and system failure. Recent studies reveal graphene as a powerful solid lubricant to combat friction and wear. Most of these studies have focused on nanoscale tribology and have been limited to a few specific surfaces. Here, we uncover many unknown aspects of graphene's contact-sliding at micro- and macroscopic tribo-scales over a broader range of surfaces. We discover that graphene's performance reduces for surfaces with increasing roughness. To overcome this, we introduce a new type of graphene/silicon nitride (SiN, 3 nm) bilayer overcoats that exhibit superior performance compared to native graphene sheets (mono and bilayer), that is, display the lowest microscale friction and wear on a range of tribologically poor flat surfaces. More importantly, two-layer graphene/SiN bilayer lubricant (<4 nm in total thickness) shows the highest macroscale wear durability on tape-head (topologically variant surface) that exceeds most previous thicker (∼7-100 nm) overcoats. Detailed nanoscale characterization and atomistic simulations explain the origin of the reduced friction and wear arising from these nanoscale coatings. Overall, this study demonstrates that engineered graphene-based coatings can outperform conventional coatings in a number of technologies.
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http://dx.doi.org/10.1021/acs.nanolett.9b03650DOI Listing
February 2020

Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection.

Sci Rep 2019 Dec 27;9(1):20140. Epub 2019 Dec 27.

Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, United States.

Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities-layered, isolated, and interconnected-that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.
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http://dx.doi.org/10.1038/s41598-019-56649-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934719PMC
December 2019

Promoting Noncovalent Intermolecular Interactions Using a C Core Particle in Aqueous PC60s-Covered Colloids for Ultraefficient Photoinduced Particle Activity.

ACS Appl Mater Interfaces 2019 Oct 11;11(42):38798-38807. Epub 2019 Oct 11.

Center for Nanoscale Materials , Argonne National Laboratory , Lemont , Illinois 60439 , United States.

Noncovalent intermolecular interactions in nanomaterials, such as van der Waals effects, allow adjustment of the nanoscopic size of compounds and their conformation in molecular crystal regimes. These strong interactions permit small particle sizes to be maintained as the crystals grow. In particular, these effects can be leveraged in the confined/reinforcing phase of molecules. With this in mind, we used C molecules as a core particle in single-PC60 surfactant-covered colloid in a water-processable system. Compared with our previous results based on a PCBM core-PC60 shell particle, the PC60-C colloid had a considerably smaller spherical structure due to the increased intermolecular interactions between C (fullerene) molecules. Interestingly, the conformation of C aggregates was altered depending on the mixed solvents and their volume fraction in the organic phase, which strongly affected the structural properties of the PC60-C colloids. The particle facilitated strong interactions with a p-type core sphere when it was introduced as the shell part of a p-n heterojunction particle. This direct interaction provided effective electronic communication between p- and n-type particles, resulting in ultraefficient photonic properties, particularly in charge separation in aqueous heterostructured colloids. This enabled the development of an extremely efficient photovoltaic device with a 6.74% efficiency, which could provide the basis for creating high-performance water-processable solar cells based on p-n heterostructured NPs.
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http://dx.doi.org/10.1021/acsami.9b14240DOI Listing
October 2019

Machine learning a bond order potential model to study thermal transport in WSe nanostructures.

Nanoscale 2019 May;11(21):10381-10392

Center for Nanoscale Materials, Argonne National Laboratory, Argonne IL, USA.

Nanostructures of transition metal di-chalcogenides (TMDCs) exhibit exotic thermal, chemical and electronic properties, enabling diverse applications from thermoelectrics and catalysis to nanoelectronics. The thermal properties of these nanoscale TMDCs are of particular interest for thermoelectric applications. Thermal transport studies on nanotubes and nanoribbons remain intractable to first principles calculations whereas existing classical molecular models treat the two chalcogen layers in a monolayer with different atom types; this imposes serious limitations in studying multi-layered TMDCs and dynamical phenomena such as nucleation and growth. Here, we overcome these limitations using machine learning (ML) and introduce a bond order potential (BOP) trained against first principles training data to capture the structure, dynamics, and thermal transport properties of a model TMDC such as WSe2. The training is performed using a hierarchical objective genetic algorithm workflow to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet. As a representative case study, we perform molecular dynamics simulations using the ML-BOP model to study the structure and temperature-dependent thermal conductivity of WSe2 tubes and ribbons of different chiralities. We observe slightly higher thermal conductivities along the armchair direction than zigzag for WSe2 monolayers but the opposite effect for nanotubes, especially of smaller diameters. We trace the origin of these differences to the anisotropy in thermal transport and the restricted momentum selection rules for phonon-phonon Umpklapp scattering. The developed ML-BOP model is of broad interest and will facilitate studies on nucleation and growth of low dimensional WSe2 structures as well as their transport properties for thermoelectric and thermal management applications.
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http://dx.doi.org/10.1039/c9nr02873kDOI Listing
May 2019

Perovskite nickelates as bio-electronic interfaces.

Nat Commun 2019 04 10;10(1):1651. Epub 2019 Apr 10.

School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA.

Functional interfaces between electronics and biological matter are essential to diverse fields including health sciences and bio-engineering. Here, we report the discovery of spontaneous (no external energy input) hydrogen transfer from biological glucose reactions into SmNiO, an archetypal perovskite quantum material. The enzymatic oxidation of glucose is monitored down to ~5 × 10 M concentration via hydrogen transfer to the nickelate lattice. The hydrogen atoms donate electrons to the Ni d orbital and induce electron localization through strong electron correlations. By enzyme specific modification, spontaneous transfer of hydrogen from the neurotransmitter dopamine can be monitored in physiological media. We then directly interface an acute mouse brain slice onto the nickelate devices and demonstrate measurement of neurotransmitter release upon electrical stimulation of the striatum region. These results open up avenues for use of emergent physics present in quantum materials in trace detection and conveyance of bio-matter, bio-chemical sciences, and brain-machine interfaces.
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http://dx.doi.org/10.1038/s41467-019-09660-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458181PMC
April 2019

"Teamwork Makes the Dream Work": Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions.

J Phys Chem A 2019 May 17;123(17):3903-3910. Epub 2019 Apr 17.

Center for Nanoscale Materials , Argonne National Lab , Lemont , Illinois 60439 , United States.

Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature ( T), pressure ( P), and composition ( X) require a free-energy-based search that is often computationally expensive and tedious. Here, we introduce a new machine-learning workflow for structure prediction that is based on a concept inspired by the evolution of human tribes in primitive society. Our tribal genetic algorithm (GA) combines configurational sampling with evolutionary optimization to accurately predict entropically stabilized phases at finite ( T, P, X), at a computational cost that is an order of magnitude smaller than that required for a free-energy-based search. In a departure from standard GA techniques, the populations of individuals are divided into multiple tribes based on a bond-order fingerprint, and genetic operations are modified to ensure that cluster configurations are sampled adequately to capture entropic contributions. Team competition introduced into the evolutionary process allows winning teams (representing a better set of individuals) to expand their sizes; this translates into a more expanded search of the phase space allowing us to explore solutions near possible global minimum. Each team explores a specific section of the structural phase space and avoids bias on solutions arising from the use of individual populations in a purely energy-based search. We demonstrate the efficacy of our approach by performing the structural prediction of a representative two-dimensional two-body system as well as Lennard-Jones clusters over a range of temperatures up to its melting point. Our approach outperforms the standard GA approaches and enables structural search under "real nonambient conditions" on both bulk systems and finite-sized clusters.
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http://dx.doi.org/10.1021/acs.jpca.9b00914DOI Listing
May 2019

Ligand dynamics control structure, elasticity, and high-pressure behavior of nanoparticle superlattices.

Nanoscale 2019 Jun;11(22):10655-10666

Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA.

Precise engineering of nanoparticle superlattices (NPSLs) for energy applications requires a molecular-level understanding of the physical factors governing their morphology, periodicity, mechanics, and response to external stimuli. Such knowledge, particularly the impact of ligand dynamics on physical behavior of NPSLs, is still in its infancy. Here, we combine coarse-grained molecular dynamics simulations, and small angle X-ray scattering experiments in a diamond anvil cell to demonstrate that coverage density of capping ligands (i.e., number of ligands per unit area of a nanoparticle's surface), strongly influences the structure, elasticity, and high-pressure behavior of NPSLs using face-centered cubic PbS-NPSLs as a representative example. We demonstrate that ligand coverage density dictates (a) the extent of diffusion of ligands over NP surfaces, (b) spatial distribution of the ligands in the interstitial spaces between neighboring NPs, and (c) the fraction of ligands that interdigitate across different nanoparticles. We find that below a critical coverage density (1.8 nm-2 for 7 nm PbS NPs capped with oleic acid), NPSLs collapse to form disordered aggregates via sintering, even under ambient conditions. Above the threshold ligand coverage density, NPSLs surprisingly preserve their crystalline order even under high applied pressures (∼40-55 GPa), and show a completely reversible pressure behavior. This opens the possibility of reversibly manipulating lattice spacing of NPSLs, and in turn, finely tuning their collective electronic, optical, thermo-mechanical, and magnetic properties.
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http://dx.doi.org/10.1039/c8nr09699fDOI Listing
June 2019

Boosting contact sliding and wear protection via atomic intermixing and tailoring of nanoscale interfaces.

Sci Adv 2019 Jan 18;5(1):eaau7886. Epub 2019 Jan 18.

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Republic of Singapore.

Friction and wear cause energy wastage and system failure. Usually, thicker overcoats serve to combat such tribological concerns, but in many contact sliding systems, their large thickness hinders active components of the systems, degrades functionality, and constitutes a major barrier for technological developments. While sub-10-nm overcoats are of key interest, traditional overcoats suffer from rapid wear and degradation at this thickness regime. Using an enhanced atomic intermixing approach, we develop a ~7- to 8-nm-thick carbon/silicon nitride (C/SiN ) multilayer overcoat demonstrating extremely high wear resistance and low friction at all tribological length scales, yielding ~2 to 10 times better macroscale wear durability than previously reported thicker (~20 to 100 nm) overcoats on tape drive heads. We report the discovery of many fundamental parameters that govern contact sliding and reveal how tuning atomic intermixing at interfaces and varying carbon and SiN thicknesses strongly affect friction and wear, which are crucial for advancing numerous technologies.
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http://dx.doi.org/10.1126/sciadv.aau7886DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357764PMC
January 2019

Machine learning coarse grained models for water.

Nat Commun 2019 01 22;10(1):379. Epub 2019 Jan 22.

Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA.

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP, and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10's of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model).
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http://dx.doi.org/10.1038/s41467-018-08222-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342926PMC
January 2019

Active site localization of methane oxidation on Pt nanocrystals.

Nat Commun 2018 08 24;9(1):3422. Epub 2018 Aug 24.

Department of Physics, Sogang University, Seoul, 04107, Korea.

High catalytic efficiency in metal nanocatalysts is attributed to large surface area to volume ratios and an abundance of under-coordinated atoms that can decrease kinetic barriers. Although overall shape or size changes of nanocatalysts have been observed as a result of catalytic processes, structural changes at low-coordination sites such as edges, remain poorly understood. Here, we report high-lattice distortion at edges of Pt nanocrystals during heterogeneous catalytic methane oxidation based on in situ 3D Bragg coherent X-ray diffraction imaging. We directly observe contraction at edges owing to adsorption of oxygen. This strain increases during methane oxidation and it returns to the original state after completing the reaction process. The results are in good agreement with finite element models that incorporate forces, as determined by reactive molecular dynamics simulations. Reaction mechanisms obtained from in situ strain imaging thus provide important insights for improving catalysts and designing future nanostructured catalytic materials.
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http://dx.doi.org/10.1038/s41467-018-05464-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109038PMC
August 2018

Crude-Oil-Repellent Membranes by Atomic Layer Deposition: Oxide Interface Engineering.

ACS Nano 2018 Aug 17;12(8):8678-8685. Epub 2018 Aug 17.

Center for Nanoscale Materials , Argonne National Laboratory , Lemont , Illinois 60439 , United States.

Crude oil fouling on membrane surfaces is a persistent, crippling challenge in oil spill remediation and oilfield wastewater treatment. In this research, we present how a nanosized oxide coating can profoundly affect the anti-crude-oil property of membrane materials. Select oxide coatings with a thickness of ∼10 nm are deposited conformally on common polymer membrane surfaces by atomic layer deposition to significantly mitigate fouling during filtration processes. TiO- and SnO-coated membranes exhibited far greater anti-crude-oil performance than ZnO- and AlO-coated ones. Tightly bound hydration layers play a crucial role in protecting the surface from crude oil adhesion, as revealed by molecular dynamics simulations. This work provides a facile strategy to fabricate crude-oil-resistant membranes with negligible impact on membrane structure, and also demonstrates that, contrary to common belief, excellent crude oil resistance can be achieved easily without implementation of sophisticated, hierarchical structures.
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http://dx.doi.org/10.1021/acsnano.8b04632DOI Listing
August 2018

Strongly correlated perovskite lithium ion shuttles.

Proc Natl Acad Sci U S A 2018 09 13;115(39):9672-9677. Epub 2018 Aug 13.

School of Materials Engineering, Purdue University, West Lafayette, IN 47907.

Solid-state ion shuttles are of broad interest in electrochemical devices, nonvolatile memory, neuromorphic computing, and biomimicry utilizing synthetic membranes. Traditional design approaches are primarily based on substitutional doping of dissimilar valent cations in a solid lattice, which has inherent limits on dopant concentration and thereby ionic conductivity. Here, we demonstrate perovskite nickelates as Li-ion shuttles with simultaneous suppression of electronic transport via Mott transition. Electrochemically lithiated SmNiO (Li-SNO) contains a large amount of mobile Li located in interstitial sites of the perovskite approaching one dopant ion per unit cell. A significant lattice expansion associated with interstitial doping allows for fast Li conduction with reduced activation energy. We further present a generalization of this approach with results on other rare-earth perovskite nickelates as well as dopants such as Na The results highlight the potential of quantum materials and emergent physics in design of ion conductors.
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http://dx.doi.org/10.1073/pnas.1805029115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166818PMC
September 2018

Defect Dynamics in 2-D MoS Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy.

ACS Nano 2018 Aug 8;12(8):8006-8016. Epub 2018 Aug 8.

Computational Institute , University of Chicago , Chicago , Illinois 60637 , United States.

Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary time scales via standard atomistic simulations and (b) required spatiotemporal resolution in experiments. Here, we take advantage of supervised machine learning, in situ high-resolution transmission electron microscopy (HRTEM) and molecular dynamics (MD) simulations to overcome these limitations. We combine genetic algorithms (GA) with MD to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transition between the semiconducting (2H) and metallic (1T) phase in monolayer MoS. GA-based structural optimization is used to identify the long-range structure of randomly distributed point defects (sulfur vacancies) for various defect densities. Regardless of the density, we find that organization of sulfur vacancies into extended lines is the most energetically favorable. HRTEM validates these findings and suggests a phase transformation from the 2H-to-1T phase that is localized near these extended defects when exposed to high electron beam doses. MD simulations elucidate the molecular mechanism driving the onset of the 2H to 1T transformation and indicate that finite amounts of 1T phase can be retained by increasing the defect concentration and temperature. This work significantly advances the current understanding of defect structure/evolution and structural transitions in 2D TMDs, which is crucial for designing nanoscale devices with desired functionality.
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http://dx.doi.org/10.1021/acsnano.8b02844DOI Listing
August 2018

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models.

J Phys Chem B 2018 07 5;122(28):7102-7110. Epub 2018 Jul 5.

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back mapping, of coarse-grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force fields and often rely on running a long-time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias Monte Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias Monte Carlo (C-CBMC) algorithm, which allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back mapping or reverse transformation for model systems ranging from the ice-liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and molecular dynamics-based back-mapping techniques. In all of the cases, the C-CBMC algorithm is able to find optimal hydrogen-bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures, such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground-state energies predicted by the other methods. Detailed analysis of the atomistic structures shows a significantly better global hydrogen positioning when contrasted with the existing simpler back-mapping methods. The errors in the radial distribution functions (RDFs) of back-mapped configuration relative to reference configuration for the C-CBMC, MD, and MC were found to be 6.9, 8.7, and 12.9, respectively, for the hexagonal system. For the cubic system, the relative errors of the RDFs for the C-CBMC, MD, and MC were found to be 18.2, 34.6, and 39.0, respectively. Our results demonstrate the efficiency and efficacy of our new back-mapping approach, especially for crystalline systems where simple force-field-based relaxations have a tendency to get trapped in local minima.
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http://dx.doi.org/10.1021/acs.jpcb.8b01791DOI Listing
July 2018

Quantitative Observation of Threshold Defect Behavior in Memristive Devices with Operando X-ray Microscopy.

ACS Nano 2018 05 4;12(5):4938-4945. Epub 2018 May 4.

Materials Science Division , Argonne National Laboratory , Argonne , Illinois 60439 , United States.

Memristive devices are an emerging technology that enables both rich interdisciplinary science and novel device functionalities, such as nonvolatile memories and nanoionics-based synaptic electronics. Recent work has shown that the reproducibility and variability of the devices depend sensitively on the defect structures created during electroforming as well as their continued evolution under dynamic electric fields. However, a fundamental principle guiding the material design of defect structures is still lacking due to the difficulty in understanding dynamic defect behavior under different resistance states. Here, we unravel the existence of threshold behavior by studying model, single-crystal devices: resistive switching requires that the pristine oxygen vacancy concentration reside near a critical value. Theoretical calculations show that the threshold oxygen vacancy concentration lies at the boundary for both electronic and atomic phase transitions. Through operando, multimodal X-ray imaging, we show that field tuning of the local oxygen vacancy concentration below or above the threshold value is responsible for switching between different electrical states. These results provide a general strategy for designing functional defect structures around threshold concentrations to create dynamic, field-controlled phases for memristive devices.
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http://dx.doi.org/10.1021/acsnano.8b02028DOI Listing
May 2018

Silicon compatible Sn-based resistive switching memory.

Nanoscale 2018 May;10(20):9441-9449

Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA.

Large banks of cheap, fast, non-volatile, energy efficient, scalable solid-state memories are an increasingly essential component for today's data intensive computing. Conductive-bridge random access memory (CBRAM) - which involves voltage driven formation and dissolution of Cu or Ag filaments in a Cu (or Ag) anode/dielectric (HfO2 or Al2O3)/inert cathode device - possesses the necessary attributes to fit the requirements. Cu and Ag are, however, fast diffusers and known contaminants in silicon microelectronics. Herein, employing a criterion for electrode metal selection applicable to cationic filamentary devices and using first principles calculations for estimating diffusion barriers in HfO2, we identify tin (Sn) as a rational, silicon CMOS compatible replacement for Cu and Ag anodes in CBRAM devices. We then experimentally fabricate Sn based CBRAM devices and demonstrate very fast, steep-slope memory switching as well as threshold switching, comparable to Cu or Ag based devices. Furthermore, time evolution of the cationic filament formation along with the switching mechanism is discussed based on time domain measurements (I vs. t) carried out under constant voltage stress. The time to threshold is shown to be a function of both the voltage stress (Vstress) as well as the initial leakage current (I0) through the device.
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http://dx.doi.org/10.1039/c8nr01540fDOI Listing
May 2018

Operando tribochemical formation of onion-like-carbon leads to macroscale superlubricity.

Nat Commun 2018 03 21;9(1):1164. Epub 2018 Mar 21.

Center for Nanoscale Materials, Argonne National Laboratory, 9700S. Cass Ave, Argonne, IL, 60439, USA.

Stress-induced reactions at the sliding interface during relative movement are known to cause structural or chemical modifications in contacting materials. The nature of these modifications at the atomic level and formation of byproducts in an oil-free environment, however, remain poorly understood and pose uncertainties in predicting the tribological performance of the complete tribosystem. Here, we demonstrate that tribochemical reactions occur even in dry conditions when hydrogenated diamond-like carbon (H-DLC) surface is slid against two-dimensional (2D) molybdenum disulfide along with nanodiamonds in dry nitrogen atmosphere. Detailed experimental studies coupled with reactive molecular dynamics simulations reveal that at high contact pressures, diffusion of sulfur from the dissociated molybdenum disulfide led to amorphization of nanodiamond and subsequent transformation to onion-like carbon structures (OLCs). The in situ formation of OLCs at the sliding interface provide reduced contact area as well as incommensurate contact with respect to the H-DLC surface, thus enabling successful demonstration of superlubricity.
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http://dx.doi.org/10.1038/s41467-018-03549-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862981PMC
March 2018

Three-Dimensional Integrated X-ray Diffraction Imaging of a Native Strain in Multi-Layered WSe.

Nano Lett 2018 03 23;18(3):1993-2000. Epub 2018 Feb 23.

Emerging two-dimensional (2-D) materials such as transition-metal dichalcogenides show great promise as viable alternatives for semiconductor and optoelectronic devices that progress beyond silicon. Performance variability, reliability, and stochasticity in the measured transport properties represent some of the major challenges in such devices. Native strain arising from interfacial effects due to the presence of a substrate is believed to be a major contributing factor. A full three-dimensional (3-D) mapping of such native nanoscopic strain over micron length scales is highly desirable for gaining a fundamental understanding of interfacial effects but has largely remained elusive. Here, we employ coherent X-ray diffraction imaging to directly image and visualize in 3-D the native strain along the (002) direction in a typical multilayered (∼100-350 layers) 2-D dichalcogenide material (WSe) on silicon substrate. We observe significant localized strains of ∼0.2% along the out-of-plane direction. Experimentally informed continuum models built from X-ray reconstructions trace the origin of these strains to localized nonuniform contact with the substrate (accentuated by nanometer scale asperities, i.e., surface roughness or contaminants); the mechanically exfoliated stresses and strains are localized to the contact region with the maximum strain near surface asperities being more or less independent of the number of layers. Machine-learned multimillion atomistic models show that the strain effects gain in prominence as we approach a few- to single-monolayer limit. First-principles calculations show a significant band gap shift of up to 125 meV per percent of strain. Finally, we measure the performance of multiple WSe transistors fabricated on the same flake; a significant variability in threshold voltage and the "off" current setting is observed among the various devices, which is attributed in part to substrate-induced localized strain. Our integrated approach has broad implications for the direct imaging and quantification of interfacial effects in devices based on layered materials or heterostructures.
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http://dx.doi.org/10.1021/acs.nanolett.7b05441DOI Listing
March 2018

Perovskite nickelates as electric-field sensors in salt water.

Nature 2018 01 18;553(7686):68-72. Epub 2017 Dec 18.

School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, USA.

Designing materials to function in harsh environments, such as conductive aqueous media, is a problem of broad interest to a range of technologies, including energy, ocean monitoring and biological applications. The main challenge is to retain the stability and morphology of the material as it interacts dynamically with the surrounding environment. Materials that respond to mild stimuli through collective phase transitions and amplify signals could open up new avenues for sensing. Here we present the discovery of an electric-field-driven, water-mediated reversible phase change in a perovskite-structured nickelate, SmNiO. This prototypical strongly correlated quantum material is stable in salt water, does not corrode, and allows exchange of protons with the surrounding water at ambient temperature, with the concurrent modification in electrical resistance and optical properties being capable of multi-modal readout. Besides operating both as thermistors and pH sensors, devices made of this material can detect sub-volt electric potentials in salt water. We postulate that such devices could be used in oceanic environments for monitoring electrical signals from various maritime vessels and sea creatures.
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http://dx.doi.org/10.1038/nature25008DOI Listing
January 2018

Alloy-assisted deposition of three-dimensional arrays of atomic gold catalyst for crystal growth studies.

Nat Commun 2017 12 8;8(1):2014. Epub 2017 Dec 8.

Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA.

Large-scale assembly of individual atoms over smooth surfaces is difficult to achieve. A configuration of an atom reservoir, in which individual atoms can be readily extracted, may successfully address this challenge. In this work, we demonstrate that a liquid gold-silicon alloy established in classical vapor-liquid-solid growth can deposit ordered and three-dimensional rings of isolated gold atoms over silicon nanowire sidewalls. We perform ab initio molecular dynamics simulation and unveil a surprising single atomic gold-catalyzed chemical etching of silicon. Experimental verification of this catalytic process in silicon nanowires yields dopant-dependent, massive and ordered 3D grooves with spacing down to ~5 nm. Finally, we use these grooves as self-labeled and ex situ markers to resolve several complex silicon growths, including the formation of nodes, kinks, scale-like interfaces, and curved backbones.
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http://dx.doi.org/10.1038/s41467-017-02025-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722855PMC
December 2017
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