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| 1 | Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)显示文摘Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level. | Artem Maksov Ondrej Dyck Kai Wang Kai Xiao David B.Geohegan Bobby G.Sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov | 2019 | npj Computational Materials2019，，1： | 11 |
| 2 | The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design显示文摘The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment. | Kamal Choudhary Kevin F.Garrity Andrew C.E.Reid Brian DeCost Adam J.Biacchi Angela R.Hight Walker Zachary Trautt Jason Hattrick-Simpers A.Gilad Kusne Andrea Centrone Albert Davydov Jie Jiang Ruth Pachter Gowoon Cheon Evan Reed Ankit Agrawal Xiaofeng Qian Vinit Sharma Houlong Zhuang Sergei V.Kalinin Bobby G.Sumpter Ghanshyam Pilania Pinar Acar Subhasish Mandal Kristjan Haule David Vanderbilt Karin Rabe Francesca Tavazza | 2020 | npj Computational Materials2020，，1： | 10 |
| 3 | Benchmarking graph neural networks for materials chemistry显示文摘Graph neural networks(GNNs)have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications.To date,a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis.However,a consistent benchmark of these models remains lacking,hindering the development and consistent evaluation of new models in the materials field.Here,we present a workflow and testing platform,MatDeepLearn,for quickly and reproducibly assessing and comparing GNNs and other machine learning models.We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry.From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized.We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs,due to learned rather than defined representations.Meanwhile several weaknesses of GNNs are also observed including high data requirements,and suggestions for further improvement for applications in materials chemistry are discussed. | Victor Fung Jiaxin Zhang Eric Juarez Bobby G.Sumpter | 2021 | npj Computational Materials2021，，1： | 5 |
| 4 | Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy显示文摘Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest.However,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different ones.This limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies.Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection.This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble.This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging. | Ayana Ghosh Bobby G.Sumpter Ondrej Dyck Sergei V.Kalinin Maxim Ziatdinov | 2021 | npj Computational Materials2021，，1： | 1 |
| 5 | Use of Drug Discovery Tools in Rational Organometallic Catalyst Design显示文摘 | Michael L.Drummond Bobby G.Sumpter | | 0，，21： | 1 |
| 6 | Ab initio investigation of the cyclodehydrogenation process for polyanthrylene transformation to graphene nanoribbons显示文摘Graphene nanoribbons(GNRs)can be synthesized from molecular precursors with atomic precision.A prominent case is the 7-atom-wide armchair GNR made from 10,10′-dibromo-9,9′-bianthryl(DBBA)precursors on metal substrates through dehalogenation/polymerization followed by cyclodehydrogenation.We investigate the key aspects of the cyclodehydrogenation process by evaluating the energy profiles of various reaction pathways using density functional theory and the nudged elastic band method.The metal substrate plays a critical catalytic role by providing stronger adsorption for products and facilitating H desorption.For polyanthrylene on an extra layer of GNR on Au,the underlying GNR insulates it from the Au substrate and increases the reaction barriers,rendering the polyanthrylene“quasi-freestanding”.However,positive charge injection can induce localized cyclodehydrogenation.We find that this is due to the stabilization of an intermediate state through an arenium ion mechanism and favorable orbital symmetries.These results provide mechanistic insight into the effects of the metal substrate and charge injection on cyclodehydrogenation during GNR synthesis and offer guidance for the design and growth of new graphitic structures. | Zhongcan Xiao Chuanxu Ma Wenchang Lu Jingsong Huang Liangbo Liang Kunlun Hong An-Ping Li Bobby G.Sumpter Jerzy Bernholc | 2019 | npj Computational Materials2019，，1： | 0 |
| 7 | Inverse design of two-dimensional materials with invertible neural networks显示文摘The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties. | Victor Fung Jiaxin Zhang Guoxiang Hu P.Ganesh Bobby G.Sumpter | 2021 | npj Computational Materials2021，，1： | 0 |
| 8 | Author Correction:Inverse design of two-dimensional materials with invertible neural networks显示文摘The original version of this Article contained errors in Fig.4,in which Fig.4a and Fig.4b were swapped. | Victor Fung Jiaxin Zhang Guoxiang Hu P.Ganesh Bobby G.Sumpter | 2021 | npj Computational Materials2021，，1： | 0 |
| 9 | Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline显示文摘Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials,naturally offering the challenge of using these as starting inputs for atomistic simulations.In this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic configurations.However,this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy,as well as latencies of microscopy and simulations per se.Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment,to enable the selection of regions of interest and exploring them using physical simulations.Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors.The pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow.This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects.However,it is universal and can be used for other material systems. | Ayana Ghosh Maxim Ziatdinov Ondrej Dyck Bobby G.Sumpter Sergei V.Kalinin | 2022 | npj Computational Materials2022，，1： | 0 |
| 10 | A Novel Dynamic Polymer Synthesis via Chlorinated Solvent Quenched Depolymerization显示文摘Dynamic polymers with both physical interactions and dynamic covalent bonds exhibit superior performance,but achieving such dry polymers in an effi-cient manner remains a challenge.Herein,we report a novel organic solvent quenched polymer synthesis using the natural molecule thioctic acid(TA),which has both a dynamic disulfide bond and carboxylic acid.The effects of the solvent type and concentration along with reaction times on the proposed reaction were thoroughly explored for polymer synthesis.Solid-state proton nuclear magnetic resonance(1 H NMR)and first-principles simulations were carried out to investigate the reaction mechanism.They show that the chlorinated solvent can efficiently stabilize and mediate the depolymerization of poly(TA),which is more kinetically favorable upon lowering the temperature.Attributed to the numerous dynamic covalent disulfide bonds and noncovalent hydrogen bonds,the obtained poly(TA)shows high extensibility,self-healing,and reprocessable properties.It can also be employed as an efficient adhesive even on a Teflon surface and 3D printed using the fused deposition modeling technique.This new polymer synthesis approach of using organic solvents as catalysts along with the unique reaction mechanism provides a new pathway for efficient polymer synthesis,especially for multifunctional dynamic polymers. | Jiadeng Zhu Sheng Zhao Jiancheng Luo Wei Niu Joshua T.Damron Zhen Zhang Md Anisur Rahman Mark A.Arnould Tomonori Saito Rigoberto Advincula Alexei P.Sokolov Bobby G.Sumpter Peng-Fei Cao | 2023 | CCS Chemistry2023，5，8： | 0 |
| 11 | Author Correction:Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)显示文摘The original version of the published Article omitted a statement from the Acknowledgements section.The Acknowledgements have been updated to include the following:The work on microscopy and synthesis was supported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Materials Sciences and Engineering Division(R.K.V.,S.V.K.,K.W.,K.X.,D.G.).The HTML and PDF versions of the Article have been corrected. | Artem Maksov Ondrej Dyck Kai Wang Kai Xiao David B.Geohegan Bobby G.Sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov | 2020 | npj Computational Materials2020，，1： | 0 |
| 12 | Single-atom catalysts with anionic metal centers: Promising electrocatalysts for the oxygen reduction reaction and beyond显示文摘Ongoing efforts to develop single-atom catalysts(SACs) for the oxygen reduction reaction(ORR) typically focus on SACs with cationic metal centers,while SACs with anionic metal centers(anionic SACs) have been generally neglected.However,anionic SACs may offer excellent active sites for ORR,since anionic metal centers could facilitate the activation of O_(2) by back donating electrons to the antibonding orbitals of O_(2).In this work,we propose a simple guideline for designing anionic SACs:the metal centers should have larger electronegativity than the surrounding atoms in the substrate on which the metal atoms are supported.By means of density functional theory(DFT) simulations,we identified 13 anionic metal centers(Co,Ni,Cu,Ru,Rh,Pd,Ag,Re,Os,Ir,Pt,Au,and Hg) dispersed on pristine or defective antimonene substrates as new anionic SACs,among which anionic Au and Co metal centers exhibit limiting potentials comparable to,or even better than,conventional Pt-based catalysts towards ORR.We also found that anionic Os and Re metal centers on the defective antimonene can electrochemically catalyze the nitrogen reduction reaction(NRR) with a limiting potential close to that of stepped Ru(0001).Overall,our work shows promise towards the rational design of anionic SACs and their utility for applications as electrocatalysts for ORR and other important electrochemical reactions. | Jinxing Gu Yinghe Zhao Shiru Lin Jingsong Huang Carlos R.Cabrera Bobby G.Sumpter Zhongfang Chen | 2021 | Journal of Energy Chemistry2021，30，12： | 0 |
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