Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine-learning interatomic potential vs.\ reverse Monte Carlo fitting to experimental data
The short and intermediate-range order in GeO$_2$ glass are investigated by molecular dynamics using machine-learning interatomic potential trained on ab initio calculation data and compared with reverse Monte Carlo fitting of neutron diffraction data. To characterize the structural differences in each model, the total/partial structure factors, coordination number, ring size and shape distributions, and persistent homology analysis were performed. These results show that although the two approaches yield similar two-body correlations, they can lead to three-dimensional models with very different short and intermediate-range ordering. A clear difference was observed especially in the ring distributions; RMC models exhibit a broad distribution in the ring size distribution, while neural network potential molecular dynamics yield much narrower ring distributions. This confirms that the density functional approximation in the ab initio calculations determines the preferred network assembly more strictly than RMC with simple coordination constraints and neutron diffraction data with isotope substitution.
Modeling the amorphous structure of a mechanically alloyed Ti50Ni25Cu25 alloy using anomalous wide-angle x-ray scattering and reverse Monte Carlo simulations
通过19 h的机械合金化制备了Ti 50 Ni 25 Cu 25非晶合金。异常广角X射线散射数据收集在六个能量和六个总散射因子。通过考虑Ni和Cu K边附近两个能量下的数据，得到了Ni和Cu原子周围的两个微分反常散射因子，表明这两个原子周围的化学环境是不同的。这8个因子被用作反向蒙特卡罗方法的输入数据，用于计算部分结构因子STi-Ti（K）、STi-Cu（K）、STi-Ni（K）、SCu-Cu（K）、SCu-Ni（K）和SNi-Ni（K）。通过傅立叶变换，得到了部分对分布函数GTi-Ti（r），GTi-Cu（r），GTi-Ni（r），GCu-Cu（r），GCu-Ni（r）和GNi-Ni（r），并确定了第一邻体的配位数和原子间距。
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials
Dept. of Electrical & Computer Eng.（电气与计算机工程系）；Texas A&M Univ.（德克萨斯A&M大学）；Dept. of Prosthetics & Orthotics（假肢与矫形器系）；College of Science & Eng.（科学与工程学院）；Ankara Univ.（安卡拉大学）；Hamad Bin Khalifa Univ. and Texas A&M Univ. at Qatar（哈马德·本·卡西姆大学和德克萨斯A&M大学（多哈））
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）
Predicting Crystal Structures and Ionic Conductivities in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
Université de Bordeaux, CNRS, Bordeaux INP, ICMCB（波尔多大学，法国国家科学研究中心，波尔多国立高等电子、计算机、电信与无线电通信学院，材料化学与分子物理研究所）；Réseau sur le Stockage Electrochimique de l’Energie (RS2E), CNRS FR 3459（电化学储能网络（RS2E），法国国家科学研究中心）
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））
Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
NYU, Department of Chemistry（纽约大学化学系）；Initiative for Computational Catalysis, Flatiron Institute（计算催化计划，Flatiron研究所）；NYU, Department of Physics（纽约大学物理系）；Courant Institute of Mathematical Sciences, NYU（数学科学学院，纽约大学）；NYU-ECNU Center for Computational Chemistry（纽约大学-复旦大学计算化学中心）；Simons Center for Computational Physical Chemistry, NYU（Simons计算物理化学中心，纽约大学）
Number Series of Atoms, Interatomic Bonds and Interface Bonds Defining Zinc-Blende Nanocrystals as Function of Size, Shape and Surface Orientation: Analytic Tools to Interpret Solid State Spectroscopy Data
Semiconductor nanocrystals (NCs) experience stress and charge transfer by embedding materials or ligands and impurity atoms. In return, the environment of NCs experiences a NC stress response which may lead to matrix deformation and propagated strain. Up to now, there is no universal gauge to evaluate the stress impact on NCs and their response as a function of NC size $d_{\mathrm{NC}}$ . I deduce geometrical number series as analytical tools to obtain the number of NC atoms $N_{\mathrm{NC}}(d_{\mathrm{NC}}[i])$ , bonds between NC atoms $N\mathrm{_{bnd}}(d_{\mathrm{NC}}[i])$ and interface bond…
Evaluating approaches for on-the-fly machine learning interatomic potential for activated mechanisms sampling with the activation-relaxation technique nouveau
Facet: highly efficient E(3)-equivariant networks for interatomic potentials
Department of Computer Science and Engineering, University of South Carolina（南卡罗来纳大学计算机科学与工程系）；Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程系）
From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials
School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University（上海交通大学数学科学学院、自然科学研究院和教育部线性科学中心）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）
Beyond packing of hard spheres: The effects of core softness, non-additivity, intermediate-range repulsion, and many-body interactions on the glass-forming ability of bulk metallic glasses
