Graph neural networks for materials science and chemistry
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Université de Strasbourg（斯特拉斯堡大学）；Eindhoven University of Technology（埃因霍温理工大学）
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Amsterdam（阿姆斯特丹大学）；Nanyang Technological University（南洋理工大学）；Acceleration Consortium（加速联盟）；University of Wuppertal（伍珀塔尔大学）
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；McGill University（麦吉尔大学）；Reincarnate Inc.（Reincarnate公司）；University of Cambridge（剑桥大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）；Duke University（杜克大学）
Science Done on a Machine by a Machine: AI Agents in Computational Chemistry
State Key Laboratory of Physical Chemistry of Solid Surfaces（固体表面物理化学国家重点实验室）；College of Chemistry（化学学院）；Xiamen University（厦门大学）；School of Physics, Anhui University（安徽大学物理学院）
RECOB: Reliable Benchmarking of Experimental Optimization in Chemistry and Materials Science
State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（中国科学技术大学精密与智能化学国家重点实验室）；Center for Scientific Intelligence Innovation（科学智能创新中心）；Department of Computer Science, Fudan University（复旦大学计算机科学与技术学院）；School of Chemistry, School of Computer Science, University of Birmingham（伯明翰大学化学学院、计算机科学学院）
Fast and Scalable GPU-Accelerated Quantum Chemistry for Periodic Systems with Gaussian Orbitals: Implementation and Hybrid Density Functional Theory Calculations
The MolecularWeb Universe: Web-Based, Immersive, Multiuser Molecular Graphics And Modeling, for Education and Work in Chemistry, Structural Biology, and Materials Sciences
Taming Multi-Domain, -Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Center for Quantum Information and Quantum Biology, The University of Osaka（大阪大学量子信息与量子生物学中心）
Advances in Prebiotic Chemistry: the potential of Analog Computing and Navier-Stokes Nernst-Planck (NPNS) Modeling in Organic Electronics Technologies (OECTs)
Rapid Computational Optimization of Molecular Properties using Genetic Algorithms: Searching Across Millions of Compounds for Organic Photovoltaic Materials
