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Inverse design of two-dimensional materials with invertible neural networks

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npj Computational Materials 2021年第1期7卷 1822-1830页

作者： Victor Fung Jiaxin Zhang guoxiang Hu P.ganesh bobby g.sumpter Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Computer Science and Mathematics Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Chemistry and Biochemistry Queens College of the City University of New YorkQueensNY 11367USA

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials ***,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging *** 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 *** approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse *** 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 *** extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among *** approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

关键词： properties inverse satisfying

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

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npj Computational Materials 2021年第1期7卷 909-916页

作者： Ayana ghosh bobby g.sumpter Ondrej Dyck Sergei V.Kalinin Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

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 ***,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 *** 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 *** 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 *** 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 *** methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

关键词： learning experiment enable

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

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npj Computational Materials 2022年第1期8卷 705-715页

作者： Ayana ghosh Maxim Ziatdinov Ondrej Dyck bobby g.sumpter Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN37831USA

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 *** this fashion,the theory will address experimentally emerging structures,as opposed to the full range of theoretically possible atomic ***,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 *** 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 *** 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 *** pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the *** 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 ***,it is universal and can be used for other material systems.

关键词： dynamics bridges offering

Single-atom catalysts with anionic metal centers: Promising electrocatalysts for the oxygen reduction reaction and beyond

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Journal of Energy Chemistry 2021年第12期30卷 285-293,I0006页

作者： Jinxing gu Yinghe Zhao Shiru Lin Jingsong Huang Carlos R.Cabrera bobby g.sumpter Zhongfang Chen Department of Chemistry University of Puerto Rico.Rio Piedras Campus.San Juan.Puerto Rico 00931USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTennessee 37831USA Department of Chemistry and Biochemistry The University of Texas at El PasoEl PasoTexas 79968USA

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 ***,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 *** 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 *** 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.

关键词： Single-atom catalysts Anionic metal centers Antimonene Oxygen reduction reaction Nitrogen reduction reaction

Ab initio investigation of the cyclodehydrogenation process for polyanthrylene transformation to graphene nanoribbons

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npj Computational Materials 2019年第1期5卷 339-344页

作者： Zhongcan Xiao Chuanxu Ma Wenchang Lu Jingsong Huang Liangbo Liang Kunlun Hong An-Ping Li bobby g.sumpter Jerzy Bernholc Department of Physics North Carolina State UniversityRaleighNC 27695USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA

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 *** 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 *** metal substrate plays a critical catalytic role by providing stronger adsorption for products and facilitating H *** 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 *** find that this is due to the stabilization of an intermediate state through an arenium ion mechanism and favorable orbital *** 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.

关键词： method rendering stabilization

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)

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npj Computational Materials 2019年第1期5卷 1039-1046页

作者： Artem Maksov Ondrej Dyck Kai Wang Kai Xiao David B.geohegan bobby g.sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Institute for Functional Imaging of Materials Oak Ridge National LaboratoryOak RidgeTN 37831USA Bredesen Center for Interdisciplinary Research University of TennesseeKnoxvilleTN 37996USA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA

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 ***,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 *** is due to the inherent limitations of manual ex situ analysis of the collected volumes of *** 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 *** 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 *** approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level.

关键词： defect beam sulfur

A Novel Dynamic Polymer Synthesis via Chlorinated Solvent Quenched Depolymerization

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CCS Chemistry 2023年第8期5卷 1841-1853页

作者： 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 Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTennessee 37831 Departmnet of Chemistry University of TennesseeKnoxvilleTennessee 37996 Department of Chemical and Biomolecular Engineering University of TennesseeKnoxvilleTennessee 37996 Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTennessee 37831 State Key Lab of Organic-Inorganic Composites Beijing University of Chemical TechnologyBeijing 100029

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 ***,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 *** effects of the solvent type and concentration along with reaction times on the proposed reaction were thoroughly explored for polymer ***-state proton nuclear magnetic resonance(1 H NMR)and first-principles simulations were carried out to investigate the reaction *** show that the chlorinated solvent can efficiently stabilize and mediate the depolymerization of poly(TA),which is more kinetically favorable upon lowering the *** to the numerous dynamic covalent disulfide bonds and noncovalent hydrogen bonds,the obtained poly(TA)shows high extensibility,self-healing,and reprocessable *** can also be employed as an efficient adhesive even on a Teflon surface and 3D printed using the fused deposition modeling *** 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.

关键词： self-healing polymer dynamic sulfide bond 3D printing solvent quenched synthesis dynamic polymer

Author Correction:Inverse design of two-dimensional materials with invertible neural networks

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npj Computational Materials 2021年第1期7卷 1907-1907页

作者： Victor Fung Jiaxin Zhang guoxiang Hu P.ganesh bobby g.sumpter Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN 37831 USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA Department of Chemistry and Biochemistry Queens College of the City University of New York Queens NY 11367 USA

The original version of this Article contained errors in Fig.4,in which Fig.4a and Fig.4b were swapped.

关键词： Inverse dimensional networks

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

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npj Computational Materials 2020年第1期6卷 234-246页

作者： 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 Materials Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Theiss Research La JollaCA 92037USA Department of Chemistry and Biochemistry University of MarylandCollege ParkMD 20742USA Physical Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Materials and Manufacturing Directorate Air Force Research LaboratoryWright–Patterson Air Force BaseDaytonOH 45433USA Department of Materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Texas A&M UniversityTexasTX 77843USA Joint Institute for Computational Sciences University of TennesseeKnoxvilleTN 37996USA National Institute for Computational Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA School for Engineering of Matter Transport and Energy Arizona State UniversityTempeAZ 85287USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Materials Science and Technology Division Los Alamos National LabLos AlamosNM 87545USA Department of Mechanical Engineering Virginia TechBlacksburgVA 24061USA Department of Physics and Astronomy Rutgers UniversityPiscatawayNJ 08901USA

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)*** 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.

关键词： automated JAR databases