“数据驱动的计算与理论材料设计国际研讨会”(DCTMD2024)是一个由上海大学举办,上海大学和德国弗里茨·哈伯(FHI)研究所共同组织,旨在聚集材料科学领域领军人物,探索由数据、计算、理论和实验驱动的最新领域前沿进展和创新成果的活动。DCTMD会议定于2024年10月9-13日在上海召开。
◐ Rampi Ramprasad, Georgia Tech, USA
- Talk: Polymer Informatics: Algorithmic Advances & Materials Design(不直播)
◐ T. Daniel Crawford, Virginia Tech, USA
-Talk: The Molecular Sciences Software Institute
◐ Xin Xu, Department of Chemistry, Fudan University
- Talk: AI-powered DFT methods
◐ Alexandre Tkatchenko, Luxembourg University, Luxembourg
-Talk: Towards AI-enabled Fully Quantum (Bio)Molecular Simulations
◐ Jun Jiang, University of Science and Technology of China, China
-Talk: A data driven robotic AI-chemist
◐ Lucas Foppa, Fritz Haber Institute (FHI) of the Max Planck Society, Germany
-Talk: Describing Materials Properties and Functions via the “Materials Genes” Concept
◐ Xiaonan Wang, Tsinghua University, China
-Talk: AI Foundation models and Active Learning for Materials Discovery and Process Design
Invited speakers(随机排序):
◐ Linfeng Zhang, DP Technology, China
-Talk: AI-Empowered Materials Design: Transforming Collaboration Paradigms and Overcoming Incentive Barriers
◐ Han Wang, Institute of Applied Physics and Computational Mathematics, Beijing, China
- Talk: Simulating the Microscopic World: From Schrödinger Equation to Large Atomic Models
◐ Zhipan Liu, Fudan University, China
-Talk: LASP 3.7 for Large-scale Atomic Simulation and the Application to Ethene Epoxidation on Silver
◐ Yong Xu, Tsinghua University, China
-Talk: First-principles artificial intelligence
◐ Carla Verdi, The University of Queensland, Australia
-Talk: Accurate materials modeling by machine learning and beyond DFT methods
◐ Lixue Cheng, Microsoft Research AI for Science Lab
- Talk: Recent advances in Deep QMC developments and its molecular property calculations
◐ Hongxia Hao, Microsoft Research AI for Science
- Talk: AI4Materials: From Simulation to Generation
◐ Timon Rabczuk, The Bauhaus-Universität Weimar, Germany
-Talk: Deep Energy Methods for solving PDEs
◐ Xiaoying Zhuang, Leibniz University Hannover, Germany
-Talk: Machine learning based multiscale exploration and characterization of 2D materials
◐ Jiong Yang, Shanghai University, Shanghai, China
- Talk: HH130: A Standardized Dataset for Universal Machine Learning Force Field and the Applications in the Thermal Transport of Half-Heusler Thermoelectrics
◐ Yangshuai Wang, Department of Mathematics, National University of Singapore, Singapore
- Talk: Advancing Molecular Simulations with Machine-Learned Interatomic Potentials
◐ Dan Han, Jilin University, Changcun, China
- Talk: Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites
◐ Turab Lookman, AiMaterials Research LLC, USA
-Talk: Guiding the next experiment: Bayesian Global Optimization versus Reinforcement Learning
◐ Annette Trunschke, Fritz Haber Institute (FHI) of the Max Planck Society, Germany
-Talk: Creating Synergies between Experimental and Computational Approaches in Advanced Materials Design: Importance and Challenges of Clean Data
◐ Jungho Shin, Korea Research Institute of Chemical Technology, Korea
- Talk: Optimization of Process Conditions in the Synthesis of Perovskite Solar Cells and Methane Conversion Catalysts through Intelligent Robotic Laboratories
◐ Yousung Jung, Seoul National University, Korea
- Talk: Data-Enabled Synthesis Predictions for Molecules and Materials
◐ Runhai Ouyang, Shanghai University, China
-Talk: Symbolic Regression in Materials Informatics: Applications and Challenges
◐ Sergey V. Levchenko, Skolkovo Institute of Science and Technology (Skotech), Russia
-Talk: Finding Descriptors of Catalytic Properties from Data for Catalyst Design with the Help of Artificial Intelligence
◐ Taylor Sparks, The University of Utah, USA
-Talk: What do we mean by new? Quantifying structural uniqueness in the era of generative crystal structure prediction
◐ Yuanyuan Zhou, Leibniz institute for crystal growth, Berlin, Germany
-Talk: AI-accelerated grand-canonical method for surface processes(不直播)
◐ Lei Zhang, Nanjing University of Information Science and Technology, Nanjing, China
- Talk: Language Data-Driven Machine Learning Design of New Materials
◐ Junfeng Qiao, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland
-Talk: The Electronic-Structure Genome of Inorganic Crystals
◐ Xin Chen, Suzhou Laboratory, Suzhou, China
- Talk: A Large Multi-Modality Model for Chemistry and Materials Science
◐ Kangming Li, Acceleration Consortium, University of Toronto, Canada
- Talk: Unexpected Failure and Success in Data-Driven Materials Science
◐ Lei Shen, National University of Singapore, Singapore
- Talk: Scalable Crystal Structure Relaxation Using an Iteration-free Deep Generative Model with Uncertainty Quantification
Contributed speakers(随机排序):
◐ Zhenpeng Yao, Shanghai Jiaotong University, China
- Talk: From computational screening to the synthesis of a promising OER catalyst
◐ Bastien F. Grosso, University of Birmingham, United Kingdom
- Talk: From imaginary phonons to a universal interatomic potential: the case of BiFeO3
◐ Jun Liu, Beijing University of Chemical Technology, Beijing, China
- Talk: Computational modeling and simulation of molecular design and property prediction of novel elastomer materials
◐ Guangcun Shan, Beihang University, Beijing, China
- Talk: Progress in Machine Learning Studies for High-Entropy Alloys
◐ Hui Zhou, DP Technology, Beijing
- Talk: New-Generation Materials Design Platform Powered by AI and Physical Modeling
◐ Xiankang Tang, TU Darmstadt, Germany
- Talk: Bayesian Optimization for High-Resolution Transmission Electron Microscopy
◐ Chunxia Chi, Nankai University, Tianjin, China
- Talk: Anisotropic materials with abnormal Poisson’s ratios and acoustic velocities
◐ Wenkai Ning, Shanghai University, Shanghai, China
- Talk: Extraction of data from publications empowered by Kolmogorov-Arnold Networks
◐ Akhil S. Nair, Fritz Haber Institute of the Max-Planck-Gesellschaft, Germany
- Talk: Materials-Discovery Workflows Guided by Symbolic Regression:Identifying Acid-Stable Oxides for Electrocatalysis
◐ Yunwei Zhang, Sun Yat-sen University, Guangzhou, China
- Talk: Battery prognosis from impedance spectroscopy using machine learning
◐ Jiaqi Zhou, Université catholique de Louvain (UCLouvain), Belgium
- Talk: High-throughput calculation of spin Hall conductivity in 2D material
◐ Huazhang Zhang, University of Liège, Belgium
- Talk: Effective lattice potentials of perovskite oxides derived from elaborately designed training dataset(不直播)
◐ Mohammad Khatamirad, Technical University of Berlin, Germany
- Talk: Leveraging Open-Access Libraries for Feature Engineering in Material Discovery
◐ Ivan S. Novikov, Skolkovo Institute of Science and Technology (Skotech), Russia
- Talk: Machine-learned interatomic potentials for screening multi-component alloys
◐ Zhibin Gao, Xi’an Jiaotong University, Xi’an, China
- Talk: An interpretable formula for lattice thermal conductivity of crystals
更多嘉宾敬请期待
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