王衍明 - 上海交通大学

个人简介

Education Ph.D. Materials Science and Engineering, Stanford University, USA (2016) Ph.D. (Minor) Electrical Engineering, Stanford University, USA (2016) Ph.D. (Minor) Mechanical Engineering, Stanford University, USA (2016) M.S. Materials Science and Engineering, Stanford University, USA (2012) B.E. Materials Science and Engineering, Shanghai Jiao Tong University, China (2010) 2nd B.E. Intelligence Science and Technology, Shanghai Jiao Tong University, China (2010) Working Experience 2023-pre. Tenure-Track Associate Professor, UM-SJTU Joint Institute, Shanghai 2020-2022 Tenure-Track Assistant Professor, UM-SJTU Joint Institute, Shanghai 2017-2020 Postdoctoral Research Associate, Massachusetts Institute of Technology, USA 2016-2017 Postdoctoral Research Fellow, Stanford University, USA

研究领域

Research Goal The main goal of our research is to establish an integrated, intelligent and insightful approach for accelerating new materials design and optimization, by advancing in the areas of multi-scale modeling, multi-physics modeling and machine learning. Computational Methods Our research involves a very wide range of materials modeling and simulation methods, from atomistic to continuum scale, including density functional theory, molecular dynamics, coarse-grained molecular dynamics, phase field model, finite element method and etc. The cooperation of computational methods at different scales enables accurate and systematic predictions on a variety of materials properties (such as electronic, mechanical, transport, interfacial and structural properties). In addition, this allows investigation of material synthesizability and stability, to promote the realization of promising new materials. Material Systems We list several representative material systems we have studied or we are currently working on, which can generally be categorized into the classes of low-dimensional materials, energy materials and carbonaceous materials. Examples Here we would like to briefly show a few examples from our recent work, and many of them were done in close collaboration with other research groups. Certainly they did not cover the entire spectrum of our research. If you would like to know more, please check our latest publication list or simply send us an email. Polymer electrolyte design Here we aimed to accelerate the design of highly conductive polymer electrolyte materials for all-solid-state battery applications. Specifically, we first converted the conventional chemical species space to a continuous design space via the coarse-graining approach. Then we adopted the Bayesian optimization method for an efficient screening, to identify the collective effects of molecular level material properties on the ionic conductivity, based on which we would be able to achieve the global optimization of the system conductivity. Nanowire structural change Here we developed a 3D multi-phase field model, which is capable of capturing the dynamical process of nanowire vapor-liquid-solid growth as well as reproducing the experimental wire geometries. With this model, we clarified the formation mechanisms of several abnormal semiconductor nanowire structures, and predicted the conditions at the onset of these structural changes. Crystallization mechanisms Here we demonstrated our ability of developing an empirical interatomic potential well fitted to the binary phase diagram. With an accurate potential, we can perform molecular dynamics simulations to reveal the atomistic mechanisms of many interesting phenomena. For example, from the simulation trajectories, we can identify the interface morphology of gold-catalyzed silicon epitaxial growth; or we can capture the grain coarsening process inside a polycrystalline nanoparticle.

近期论文

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Z. Shen, W. Bao, Z. Dang, He Li, R. Shang, M. Hu, Y. Wang*, J. Xie*, B. Qiao*, An Easy-to-Execute Data-Driven Method to Predict and Understand the Properties of Polymer Electrolytes based on Multivariate Linear Free Energy Relationship, submitted. Z. Zhang, Z. Xu*, C. Shen, H. Zhao*, K. Wang, L. Cheng,Y. Wang, A computational model incorporating realistic microstructures for predicting effective thermal conductivity of polyimide nanofiber aerogel, submitted. S. He, F. Xiao*, R. Hou, S. Zuo, Y. Zhou, X. Cai, Z. Li, Y. Wang, A. A. Catal, E. I. Galindo-Nava, X. Jin*, Accelerated learning and co-optimization of elastocaloric effect and stress hysteresis of elastocaloric alloys, submitted. J. Huang, C. Li, H. Luo, L. Cheng, J. Gao, D. Jiang, G. Li, Z. Xu, D. Shin, Y. Wang, Y. Kim*, Solid-State Electrolytes for Lithium Metal Batteries: State‐of‐the‐Art and Perspectives, submitted. R. Wang, J. Li, Y. Wang*, Atomistic Mechanisms of Ring Formation During Catalyzed Carbon Nanotube Growth, submitted. J. Huang, L. Cheng, Z. Zhang, C. Li, K.-T. Bang, A. Liem, H. Luo, C. Hu, Y. M. Lee, Y. Wang*, Y. Kim*, Ionic Covalent Organic Framework and Poly(Ionic Liquid) Composite Electrolytes for All-Solid-State Lithium Metal Batteries, submitted. Y. Yuan, D. Wang, Z. Zhang, K.-T. Bang, H. Chen, R. Wang, Y. Wang, Y. Kim*, Charge-Delocalized Triptycene-Based Ionic Porous Organic Polymers as Solid Electrolytes for Lithium-Metal Batteries, submitted. Z. Zhang, H. Zhu, R. Yuan, S. Wang, Y. Wang, T. Fan, Y. Rezgui, D. Zhang, The Elastic Properties of Composites Reinforced by a 3D Voronoi Fibre Network with or without Missing Fibres, submitted. Y. Yuan, Z. Zhang, Z. Zhang, K.-T. Bang, Y. Tian, Z. Dang, M. Gu, R. Wang, R. Tao, Y. Lu, Y. Wang, Y. Kim*, Highly Conductive Imidazolate Covalent Organic Frameworks with Ether Chains as Solid Electrolytes for Lithium Metal Batteries, Angew. Chem. Int. Ed., in press. Z. Zhang#, H. Fang#, Z. Xu, J. Lv, Y. Shen, Y. Wang*, Multi-objective Generative Design of Three-Dimensional Composite Materials, APL Mach. Learn., 1(4), 046120 (2023). C. Li, D. Wang, G. S. H. P. Ho, Z. Zhang, J. Huang, K. Bang, C. Y. Lau, S. Leu, Y. Wang, Y. Kim*, Anthraquinone-Based Silicate Covalent Organic Frameworks as Solid Electrolyte Interphase for High-Performance Lithium-Metal Batteries, J. Am. Chem. Soc., 145(45), 24603-24614 (2023). Z. Zhang, P. Bai, Y. Xiao, Y. Guo*, Y. Wang*, Conformation-Induced Stiffening Effect of Crosslinked Polymer Thin Films, Commun. Physics, 6(1), 332 (2023). Y. Deng#, Y. Wang#, K. Xu, Y. Wang*, A Lightweight Extendable Stacking Framework for Structure Classification in Atomistic Simulations, J. Chem. Theory Comput., 19(22), 8332-8339 (2023). S. Peng, Y. Wang, M. Braun, Y. Yin, A. C. Meng, W. Tan, B. Saini, K. Severson, A. F. Marshall, K. Sytwu, J. Baniecki, J. Dionne, W. Cai and P. C. McIntyre*, Kinetics and Mechanism of Light-Induced Phase Separation in a Mixed-Halide Perovskite, Matter, 6(6), 2052-2065 (2023). S. He, Y. Wang, Z. Zhang, F. Xiao*, S. Zuo, Y. Zhou, X. Cai, X. Jin*, Interpretable Machine Learning Workflow for Evaluation of the Transformation Temperatures of TiZrHfNiCoCu High Entropy Shape Memory Alloys, Mater. Design, 225, 111513 (2023). Z. Wang, J. Wu, Y. Wang*, Revealing Atomistic Mechanisms of Gold-Catalyzed Germanium Growth Using Molecular Dynamics Simulations, J. Phys. Chem. C, 126(44), 18867-18875 (2022). P. Li, Z. Sun, Y. Wang, R. Razaq, Y. Gao, S. Bo*, Overpotential-Regulated Stable Cycling of a Thin Magnesium Metal Anode, ACS Appl. Mater. Interfaces, 14(27), 31435-31447 (2022). T. Xie, A. France-Lanord, Y. Wang, J. Lopez, Michael M. Stolberg, M. Hill, G. M. Leverick, R. Gomez-Bombarelli, J. A. Johnson, Y. Shao-Horn and Jeffrey C. Grossman*, Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties, Nat. Commun., 13(1), 1-10 (2022). A. Jana, T. Zhu, Y. Wang, J. J. Adams, L. Kearney, A. Naskar, J. C. Grossman and N. Ferralis*, Atoms to Fibers: identifying novel processing methods in the synthesis of pitch-based carbon fibers, Sci. Adv., 8(11), eabn1905 (2022). J. Chen, Y. Wang, W. Xu, Y. Wen, G. H. Ryu, J. C. Grossman, J. H. Warner*, Atomic Structure of Dislocations and Grain Boundaries in Two-Dimensional PtSe2, ACS Nano, 15(10), 16748-16759 (2021). A. C. Meng, Y. Wang, M. R. Braun, J. Z. Lentz, S. Peng, H. Cheng, A. F. Marshall, W. Cai, P. C. McIntyre*, Bending and Precipitate Formation Mechanisms in Epitaxial Ge-Core/GeSn-Shell Nanowires, Nanoscale, 13, 17547-17555 (2021). B. Huang*, R. R. Rao, S. You, K. H. Myint, Y. Song, Y. Wang, W. Ding, L. Giordano, Y. Zhang, T. Wang, S. Muy, Y. Katayama, J. C. Grossman, A. P. Willard, K. Xu, Y. Jiang and Yang Shao-Horn*, Cation- and pH- dependent hydrogen evolution and oxidation reaction kinetics, JACS Au, 1(10), 1674-1687 (2021). B. Jin#,*, Y. Wang#, C. Jin, R. Tang*, Revealing Au13 as elementary clusters during the early formation of Au nanocrystal, J. Phys. Chem. Lett., 12, 5938-5943 (2021). B. Huang*, K. H. Myint, Y. Wang, Y. Zhang, R, R Rao, J. Sun, S. Muy, Y. Katayama, J. C. Garcia, D. Fraggedakis, J. C. Grossman, M. Z. Bazant, K. Xu, A. P. Willard* and Y. Shao-Horn*, Cation-Dependent Interfacial Structures and Kinetics for Outer-Sphere Electron-Transfer Reactions, J. Phys. Chem. C, 125(8), 4397-4411 (2021). P. Wang, R. Lu, A. France-Lanord, Y. Wang, J. C. Grossman* and T. M. Swager*, Cyclobutene Based Macrocycles, Mater. Chem. Front., 4, 3529-3538 (2020). A. C. Meng, M. R. Braun, Y. Wang, S. Peng, W. Tan, J. Z. Lentz, M. Xue, A. Pakzad, A. F. Marshall, J. S. Harris, W. Cai, and P. C. McIntyre*, Growth Mode Control for Direct-Gap Core/Shell Ge/GeSn Nanowire Light Emission, Mater. Today, 40, 101-113 (2020). Y. Wang, Tomas Sikola and Miroslav Kolibal*, Collector Droplet Behavior during Formation of Nanowire Junctions, J. Phys. Chem. Lett., 11, 6498-6504 (2020). Y. Yin, N. R. Bertin, Y. Wang, Z. Bao and W. Cai*, Topological Origin of Strain Induced Damage of Elastomers by Bond Breaking, Extreme Mech. Lett., 40, 100883 (2020). T. W. Nam, M. Kim, Y. Wang, G. Y. Kim, W. Choi, H. Lim, K. M. Song, D. Y. Jeon, J. C. Grossman and Y. S. Jung*, Thermodynamic-Driven Full-Colour Quantum Dot Patterning for Light-Emitting Diodes beyond the Eye-Limiting Resolution, Nat. Commun., 11, 3040 (2020). Y. Wang#,*, T. Xie#, A. France-Lanord, A. Berkley, J. A. Johnson, Y. Shao-Horn and J. C. Grossman*, Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics, Chem. Mater., 32(10), 4144-4151 (2020). A. France-Lanord, Y. Wang, T. Xie, Y. Shao-Horn, and J. C. Grossman*, Modelling the Effect of Chemical Variations on Lithium Transport in Poly(ethylene oxide)-Based Polymer Electrolytes at High Salt Concentration, Chem. Mater., 32(1), 121-126 (2020).