Speakers

Plenary/Invited speakers and talks in DCTMD2024:

(The list is consistently updated and please click the name for the speaker’s bio and the title of talk for the abstract if available.)

Plenary speakers:

* Rampi Ramprasad, Georgia Tech, USA 

   - Talk: Polymer Informatics: Algorithmic Advances & Materials Design

* 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:

* 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

* 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

* 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:

* 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

* 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