This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and electronic structure simulation is integrated. Using Wannier functions as the basis, we categorize Wannier Hamiltonian elements based on physical principles to incorporate diverse information from a deep-learning force field model. This information-sharing mechanism streamlines the architecture of our multifunctional model, enhancing its efficiency and effectiveness. Utilizing Wannier functions as the basis lays the groundwork for predicting more physical quantities. This approach serves as a powerful tool to explore both the structural and electronic properties of large-scale systems characterized by low periodicities. By endowing an existing well-developed machine-learning force field with electronic structure simulation capabilities, the study marks a significant advancement in developing multimodal machine-learning-based computational methods that can achieve multiple functionalities traditionally exclusive to first-principles calculations.
Li$_x$CoO$_2$ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories
University of Michigan（密歇根大学）；Toyota Research Institute（丰田研究所）
$_xTM$ O $_2$（TM={Ni，Co，Mn}）是锂离子电池的有前途的阴极，其电化学循环性能强烈地受晶体结构和相稳定性的控制，所述相稳定性是原子尺度下Li含量的函数。在这里，我们使用Li $_x$ CoO $_2$（LCO）作为一个模型系统的基准尺度桥接框架，结合密度泛函理论（DFT）计算在原子尺度上的连续尺度相场建模，以了解微观结构演变的影响相稳定性。这种尺度桥接是通过将传统的统计力学方法与可集成的深度神经网络相结合来实现的，这使得特定原子构型的形成能能够被粗粒度化并被纳入材料自由能的神经网络描述中。由此产生的现实的自由能函数，使原子通知相场模拟。这些计算结果使我们能够连接到实验工作的LCO阴极降解作为温度，形态和粒度的函数。
Note: Bridging Information Science: AB Initio Calculation Vortex of 2D Materials of Bismuthene(Bismuth Molecule) Graphene-Shaped through Kohn-Sham Equations
Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors
Center for High Pressure Science and Technology Advanced Research（高压科学与技术前沿研究中心）；Renmin University of China（中国人民大学）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；Field Quantum Research Institute（场量子研究所）；Ningbo University（宁波大学）
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）
Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters
Federal University of Pelotas（佩洛塔斯联邦大学）；Universidade de Franca（弗兰卡大学）；Federal University of Paraná（巴拉那联邦大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；University of Brasília（巴西利亚大学）；Federal Institute of Amazonas（亚马孙联邦学院）
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization
Physics-Informed Tree Search for High-Dimensional Computational Design
Department of Mechanical and Industrial Engineering, University of Illinois, Chicago（机械与工业工程系，伊利诺伊大学芝加哥分校）；Center for Nanoscale Materials, Argonne National Laboratory（纳米材料中心，阿贡国家实验室）；Mathematics and Computer Science Division, Argonne National Laboratory（数学与计算机科学部，阿贡国家实验室）
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所凝聚态物理国家实验室）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；Songshan Lake Materials Laboratory（松山湖材料实验室）
