We present an approach to generate machine-learned force fields (MLFF) with beyond density functional theory (DFT) accuracy. Our approach combines on-the-fly active learning and $Δ$-machine learning in order to generate an MLFF for zirconia based on the random phase approximation (RPA). Specifically, an MLFF trained on-the-fly during DFT based molecular dynamics simulations is corrected by another MLFF that is trained on the differences between RPA and DFT calculated energies, forces and stress tensors. Thanks to the relatively smooth nature of the differences, the expensive RPA calculations are performed only on a small number of representative structures of small unit cells. These structures are determined by a singular value decomposition rank compression of the kernel matrix with low spatial resolution. This dramatically reduces the computational cost and allows us to generate an MLFF fully capable of reproducing high-level quantum-mechanical calculations beyond DFT. We carefully validate our approach and demonstrate its success in studying the phase transitions of zirconia.
Hillock formation of Pt thin films on Yttria stabilized Zirconia single crystals
介电基片上金属薄膜的稳定性取决于界面相互作用力的大小。在非反应性界面和弱粘附的情况下，自由表面能的最小化引起薄膜的不稳定性。为了研究这些影响，Pt薄膜的厚度为50 nm的沉积通过离子束溅射氧化钇稳定的氧化锆单晶。所有的Pt膜进行热处理高达973 K 2小时。利用扫描电子显微镜（SEM）、原子力显微镜（AFM）和标准图像分析技术研究了Pt薄膜的形貌演化。已经进行了三个主要观察：i）沉积方法对退火期间膜的形态演变具有直接影响。代替通常作为对热处理的响应而观察到的空穴形成，在膜的顶部上形成各向异性金字塔形的小丘。ii）通过比较小丘的长宽比和有限元模拟结果，表明小丘的形成是由于薄膜内部的应力松弛过程。iii）通过测量所形成的Pt丘的平衡形状和形状起伏，除了丘边缘的各向异性扭结能之外，还导出了台阶自由能及其刚度的各向异性。
Band Structure Modulation of ZrO2 Nanoparticles for Control of CO Adsorption Properties: A Combined Density Functional Theory - Density Functional Tight Binding Study
School of Materials and Chemical Technology, Institute of Science Tokyo（东京科学大学材料化学技术学院）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada（加查马达大学数学与自然科学学院化学系）
Observing Nucleation and Crystallization of Rocksalt LiF from Molten State through Molecular Dynamics Simulations with Refined Machine-Learned Force Field
Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
Orbital-Free DFT-Assisted Machine-Learned Molecular Dynamics for Electric-Field-Driven Ionic Transport
The University of Tokyo（东京大学）；Quemix Inc.（Quemix公司）；Idemitsu Kosan Co., Ltd.（出光兴产株式会社）；National Institutes for Quantum Science and Technology (QST)（量子科学技术研究机构）
Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
Institute of Theoretical Physics, Technical University of Vienna（维也纳技术大学理论物理研究所）；Faculty of Physics and Center for Computational Materials Science, University of Vienna（维也纳大学物理系和计算材料科学中心）
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）
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
Institute of Informatics and Telecommunications（信息学与电信研究所）；Institute of Nanoscience and Nanotechnology（纳米科学与纳米技术研究所）；National Centre for Scientific Research “Demokritos”（“德谟克利特”国家科学研究中心）；SciFY P.N.P.C.（SciFY公共非营利公司）；School of Chemical Engineering（化学工程学院）；National Technical University of Athens（雅典国立技术大学）
Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric
Fritz-Haber-Institut der Max-Planck-Gesellschaft（马克斯·普朗克学会弗里茨·哈伯研究所）；Department of Chemistry and Biochemistry, University of South Carolina（南卡罗来纳大学化学与生物化学系）；Department of Chemical Engineering, University of California, Davis（加州大学戴维斯分校化学工程系）；Department of Materials, Imperial College London（帝国理工学院材料系）；Department of Chemistry, Imperial College London（帝国理工学院化学系）；Department of Materials Science and Engineering, University of Florida（佛罗里达大学材料科学与工程系）；Department of Chemistry, Korea University（高丽大学化学系）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）
Scalable photoexcitation-induced molecular dynamics with machine-learned Hamiltonians
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）；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）
Molecular Implementation of the Machine-Learned Skala Exchange-Correlation Functional in CP2K through GauXC
Center for Advanced Systems Understanding (CASUS)（高级系统理解中心）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗斯多夫亥姆霍兹中心）；Microsoft Research AI for Science（微软研究院科学人工智能）；Department of Chemistry, University of Zurich（苏黎世大学化学系）；Institute of Artificial Intelligence, Technische Universität Dresden（德累斯顿工业大学人工智能研究所）
