Compared to common density functionals, ab initio wave function methods can provide greater reliability and accuracy, which could prove useful when modeling adsorbates or defects of otherwise periodic systems. However, the breaking of translational symmetry necessitates large supercells that are often prohibitive for correlated wave function methods. As an alternative, we introduce the regional embedding approach, which enables correlated wave function treatments of only a target fragment of interest through small, fragment-localized orbital spaces constructed using a simple overlap criterion. Applications to the adsorption of water on lithium hydride, hexagonal boron nitride, and graphene substrates show that regional embedding combined with focal point corrections can provide converged CCSD(T) (coupled cluster) adsorption energies with very small fragment sizes.
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）
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Massachusetts Institute of Technology（麻省理工学院）；Microsoft AI for Good Research Lab（微软AI向善研究实验室）；Rutherford Appleton Laboratory（卢瑟福·阿普尔顿实验室）；University of Reading（雷丁大学）
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）
Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains
University of South Dakota（南达科他大学）；South Dakota School of Mines and Technology（南达科他矿业理工学院）；Fayetteville State University（费耶特维尔州立大学）；PROMISE Lab, Sanford Research（桑福德研究所 PROMISE 实验室）；Lower Brule Schools（下布卢学校）；University of Nebraska at Kearney（内布拉斯加大学基尔尼分校）；University of Mississippi（密西西比大学）；University of Kansas（堪萨斯大学）
Fast and Scalable GPU-Accelerated Quantum Chemistry for Periodic Systems with Gaussian Orbitals: Implementation and Hybrid Density Functional Theory Calculations
The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）；NVIDIA Corporation（英伟达公司）；Northwestern University（西北大学）；Lila Sciences, Inc.（Lila Sciences公司）；PsiQuantum（PsiQuantum公司）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
ETH Zurich（苏黎世联邦理工学院）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）；McGill University（麦吉尔大学）；University of Houston（休斯顿大学）；Duke University（杜克大学）；University of California at Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；University of Southern California（南加州大学）；Humboldt University of Berlin（柏林洪堡大学）；Université catholique de Louvain（列日天主大学）；Matgenix SRL（Matgenix公司）；University of California, Los Angeles（加州大学洛杉矶分校）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希-施莱尔大学）；Federal Institute of Materials Research and Testing (BAM)（材料研究与测试联邦机构）；Argonne National Laboratory（阿贡国家实验室）；University of Toronto（多伦多大学）；EPFL（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Amsterdam（阿姆斯特丹大学）；Acceleration（加速机构）
