Large-scale atomistic simulations of materials heavily rely on interatomic potentials, which predict the system energy and atomic forces. One of the recent developments in the field is constructing interatomic potentials by machine-learning (ML) methods. ML potentials predict the energy and forces by numerical interpolation using a large reference database generated by quantum-mechanical calculations. While high accuracy of interpolation can be achieved, extrapolation to unknown atomic environments is unpredictable. The recently proposed physically-informed neural network (PINN) model significantly improves the transferability by combining a neural network regression with a physics-based bond-order interatomic potential. Here, we demonstrate that general-purpose PINN potentials can be developed for body-centered cubic (BCC) metals. The proposed PINN potential for tantalum reproduces the reference energies within 2.8 meV/atom. It accurately predicts a broad spectrum of physical properties of Ta, including (but not limited to) lattice dynamics, thermal expansion, energies of point and extended defects, the dislocation core structure and the Peierls barrier, the melting temperature, the structure of liquid Ta, and the liquid surface tension. The potential enables large-scale simulations of physical and mechanical behavior of Ta with nearly first-principles accuracy while being orders of magnitude faster. This approach can be readily extended to other BCC metals.
Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）；School of Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程学院）
Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces
钽、钽氧化物及其异质界面在电子、热管理、催化和生物化学等多个应用领域具有巨大的技术价值。例如，包括金属（Ta）和绝缘氧化物（Ta 2 O 5）的TaOx忆阻器中的局部氧化学计量变化已经被示出导致在十亿个周期内在亚纳秒时间尺度上的快速切换，这与神经形态计算相关。尽管其广泛的重要性，原子尺度的理解的氧化学计量变化跨Ta/TaOx异质界面，如在氧化和氧化物生长的早期阶段，没有得到很好的理解。这主要是由于缺乏一个可变的电荷原子间势模型的钽氧化物，可以准确地描述在金属（Ta）和氧化物（TaOx）的环境，以及在他们的界面的离子相互作用。为了解决这一挑战，我们引入了一个电荷转移离子势（CTIP）模型Ta/Ta氧化物系统的晶格参数，结合能，状态方程，和各种实验观察到的Ta 2 O 5多晶型物的弹性性能的训练。采用遗传算法驱动的单目标全局优化方案，其次是局部单纯形优化，确定最佳的一组CTIP参数。我们新开发的CTIP潜力准确地预测结构，热力学，充满活力的多晶型物，以及弹性和表面性质的Ta和Ta 2 O 5，在良好的协议与DFT计算和实验。我们采用我们的新参数化的CTIP潜力，研究在不同的温度和原子/分子性质的氧化物种的钽氧化的早期阶段。
Atomistic Insights into Cu/amorphous-Ta$_x$N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
Machine-learning octet $AB$-type binary compounds across chemical space with domain knowledge of the interatomic bond
Ruhr-Universität Bochum（波鸿鲁尔大学）；Federal Institute for Materials Research and Testing（联邦材料研究与测试研究所）；Friedrich-Schiller-Universität Jena（耶拿弗里德里希·席勒大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
Southern University of Science and Technology（南方科技大学）；Shanghai University（上海大学）；Materials Genome Institute（材料基因组研究院）；Institute of Innovative Materials（创新材料研究院）
Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
Institute of Materials Science, Technische Universitat Darmstadt（达姆施塔特工业大学材料科学研究所）
FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
Northeastern University（东北大学）；Department of Mathematics（数学系）；Khoury College of Computer Sciences（计算机科学学院）；Department of Mechanical and Industrial Engineering（机械与工业工程系）
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Cambridge（剑桥大学）；University of Minnesota（明尼苏达大学）
PSI Center for Scientific Computing, Theory and Data（瑞士保罗谢勒研究所科学计算、理论与数据中心）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL)（国家新材料计算设计与发现中心（MARVEL））；Universität Bremen（不来梅大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Cambridge（剑桥大学）
