Machine learning (ML)-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which is a highly labor-intensive activity. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.
Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning
dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic Potentials
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Princeton University（普林斯顿大学）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Big Data & AI Research and Engineering Center（苏州大数据与人工智能研究与工程中心）；AI for Science Institute（科学智能研究所）；DP Technology（深势科技）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；College of Engineering, Peking University（北京大学工学院）
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
由于计算成本高，使用{ \it ab initio}分子动力学（AIMD）在室温下精确模拟锂离子在电池材料中的扩散过程一直是一个挑战。近年来，由于基于机器学习的原子间势的进步，这种情况发生了巨大变化。在这里，我们实现了深电位发生器计划，以\textit{automatically}产生LiGePS型固态电解质材料的原子间相互作用势。这增加了我们模拟这些材料的能力，在不牺牲{ \it ab initio}精度的情况下，提高了几个数量级。重要的技术方面，如统计误差和尺寸效应进行了仔细研究。我们进一步建立了一个可靠的协议，在实验条件下准确计算锂离子扩散过程，通过调查重要的技术方面，如统计误差和尺寸效应。这样的协议和自动化工作流程使我们能够以更高的效率筛选材料的相关属性。通过使用这里开发的协议和自动化的工作流程，我们得到的扩散系数数据和锂离子扩散的活化能，以及同意与实验。本文的工作为进一步研究锂离子扩散机理和优化固态电解质材料的锂离子导电性能奠定了基础。
PySlice: Routine Vibrational Electron Energy Loss Spectroscopy Prediction with Universal Interatomic Potentials
Vanderbilt University（范德堡大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Virginia（弗吉尼亚大学）；University of Washington（华盛顿大学）；Uppsala University（乌普萨拉大学）
MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals
Department of Chemistry, New York University（纽约大学化学系）；New York University（纽约大学）；Simons Center for Computational Physical Chemistry, New York University（纽约大学计算物理化学中心）；Department of Physics, New York University（纽约大学物理系）；Department of Chemical and Biomolecular Engineering, Tandon School of Engineering, New York University（纽约大学工程学院化学与生物分子工程系）；New York University Shanghai（纽约大学上海分校）
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
