Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simulations with almost ab initio accuracy. However, the simultaneous training of NNs on energies and forces, which are a prerequisite for, e.g., molecular dynamics simulations, can be demanding. In this work, we present an improved NN architecture based on the previous GM-NN model [V. Zaverkin and J. Kästner, J. Chem. Theory Comput. 16, 5410-5421 (2020)], which shows an improved prediction accuracy and considerably reduced training times. Moreover, we extend the applicability of Gaussian moment-based interatomic potentials to periodic systems and demonstrate the overall excellent transferability and robustness of the respective models. The fast training by the improved methodology is a pre-requisite for training-heavy workflows such as active learning or learning-on-the-fly.
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
Universidad Autónoma de Madrid（马德里自治大学）；Aalto University（阿尔托大学）；Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid（马德里自治大学凝聚态物理中心（IFIMAC））；Nano Life Science Institute (WPI-NanoLSI), Kanazawa University（金泽大学纳米生命科学研究院（WPI-NanoLSI））
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Harvard University（哈佛大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Robert Bosch Research and Technology Center（罗伯特·博世研究与技术中心）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Massachusetts Institute of Technology（麻省理工学院）
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
Department of Chemistry, UC Berkeley, California 94720, United States（化学系，伯克利大学，加利福尼亚州，94720，美国）；Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States（地球数字材料研究所，伯克利大学，加利福尼亚州，94720，美国）；The Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria（奥地利科学与技术研究所）
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
School of Mathematics and Natural Sciences, Bergische Universität Wuppertal（贝尔吉施-乌珀塔尔大学数学与自然科学学院）；School of Science, Constructor University（康斯特鲁克托大学理学院）
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Peking University（北京大学）；AI for Science Institute（人工智能科学研究院）；Tsinghua University（清华大学）；HEDPS, CAPT, College of Engineering, Peking University（北京大学工学院理论力学与计算力学系、高超声速空气动力学国家重点实验室）；DP Technology（深势科技）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）；National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics（中国科学院应用物理与计算数学研究所）
