Lithium-based disordered rocksalts (LDRs), which are an important class of cathodes for advanced Li-ion batteries, represent a complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly-disordered systems, such as LDRs, where the performance of such MLIPs have not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five MLIPs in modelling LDRs, including artificial neural network potential developed by the atomic energy network (AENET), Gaussian approximation potential (GAP), spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and moment tensor potential (MTP). Specifically, we generate a DFT-calculated dataset of 10842 disordered LiTMO$_2$ and TMO$_2$ configurations, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. Importantly, we find AENET to be the best in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train at low number of epochs, the training time increases significantly as epochs increase, with a corresponding reduction in training errors. Note that AENET and GAP tend to overfit in small datasets, with the extent of overfitting reducing with larger datasets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO$_2$ frameworks, compared to DFT ($\sim$10% error on average). Our study should pave the way both for discovering novel LDR electrodes and for modelling other configurationally complex systems, such as high-entropy ceramics and alloys.
The Intercalation Chemistry of the Disordered RockSalt Li3V2O5 Anode from Cluster Expansions and Machine Learning Interatomic Potentials
无序岩盐Li 3V 2 O 5由于其理想的低电压、高倍率性能和优异的循环稳定性而成为锂离子二次电池负极材料的理想选择。在此，我们提出了一个全面的研究DRX-Li 3V 2 O 5阳极的嵌入化学使用密度泛函理论计算结合机器学习集群扩展和原子间势。基于蒙特卡罗模拟和拟合的团簇膨胀模型，预测了室温下无序Li 3V 2 O 5阳极的电压分布，与实验结果吻合良好。与之前的DFT结果相比，我们发现在充电过程中Li离子主要嵌入四面体位置，而八面体位置的Li和V离子大多数保持稳定。此外，用拟合的矩张量势进行的MD模拟将DRX-Li 3V 2 O 5的快速充电能力归因于Li+通过四面体-八面体-四面体途径的容易扩散性。我们进一步建议调整Li：V比作为在该系统中权衡增加的锂化容量和降低的阳极电压的手段。这项工作为高性能DRX-Li 3V 2 O 5阳极提供了深入的见解，并为发现其他无序阳极材料铺平了道路。
Visualizing Crystallization Dynamics and Transformation Pathways of Disordered Rocksalt Oxides During Thermally Activated Sol-Gel Synthesis
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM)（巴西同步辐射光源实验室（LNLS），巴西能源与材料研究中心（CNPEM））；University of California, Berkeley（加州大学伯克利分校）
Coupling Lattice Distortion and Cation Disorder to Control Li-ion Transport in Cation-Disordered Rocksalt Oxides
School of Materials Science and Engineering, Peking University（北京大学材料科学与工程学院）；Zhejiang Key Laboratory of All-Solid-State Battery, Eastern Institute of Technology（宁波东方理工大学全固态电池浙江省重点实验室）；Beijing Key Laboratory of Theory and Technology for Advanced Batteries Materials, Peking University（北京大学先进电池材料理论与技术北京市重点实验室）
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）
Driving force of atomic ordering in Fe$_{1-x}$Pt$_{x}$, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations
Large Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide Based on a Machine-Learned Interatomic Potential: Novel Crystalline Phases and Oxygen-Vacancy Ordering
利用快速准确的神经网络势，我们能够用最小跳跃方法系统地探索大块氧化镁的大晶胞的能量景观。该潜力的训练重点是近化学计量组成，特别是低氧化物，即，Mg $_x$ O $_{1-x}$与$0.50<x<0.60$。我们广泛的探索表明，对于散装化学计量的化合物，有几个新的低能量岩盐状结构，其中Mg原子是八面体六配位的，并形成具有不同堆叠顺序的三角棱柱图案。此外，对于$x$的每种组成，我们发现了Mg $_x$ O $_{1-x}$的新型非化学计量晶相的密集光谱。这些结构大多类似于具有八面体配位和五配位Mg原子的岩盐结构。由于去除了一个氧原子，能量景观变得更像玻璃，具有氧空位型结构，它们在能量上彼此非常接近。对于相同数量的镁和氧原子，如果空位沿着线或平面排列，则我们的缺氧结构的能量低于具有随机分布的氧空位的岩盐结构。我们还发现了假定的全球最低配置的非化学计量的低氧化物结构的每个组合物。这些结构主要由岩盐结构的（111）板片组成，其在顶部和底部用Mg原子终止，并且沿着$z$方向以不同的顺序堆叠。与其他Magnéli型相一样，这些结构具有与其化学计量对应物显著不同的性质，例如低晶格热导率和高电导率。
Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所）；Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所材料设计与模拟实验室）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院新材料计算设计与发现国家中心）
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）
Elucidation of the atomic-scale mechanism of the anisotropic oxidation rate of 4H-SiC between the ($0001$) Si-face and ($000\overline{1}$) C-face by using a new Si-O-C interatomic potential
A cylindrical sintering method for more realistic grain boundaries in nanocrystalline thin films
Institute of Physics of Materials, The Czech Academy of Sciences（材料物理研究所，捷克科学院）；Central European Institute of Technology, Brno University of Technology（中欧技术研究所，布尔诺理工大学）；Charles University, Faculty of Mathematics and Physics（查理大学，数学与物理学院）
Evaluating approaches for on-the-fly machine learning interatomic potential for activated mechanisms sampling with the activation-relaxation technique nouveau
Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients
School of Integrated Circuit Science and Engineering, Beihang University（北京航空航天大学集成电路科学与工程学院）；Guangdong University of Technology（广东工业大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；School of Materials Science and Engineering, Beihang University（北京航空航天大学材料科学与工程学院）
kALDo 2.0: Scalable Thermal Transport from First Principles and Machine Learning Potentials
University of California Davis（加州大学戴维斯分校）；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）；Massachusetts Institute of Technology（麻省理工学院）；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））
Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
Department of Material Design and Innovation, University at Buffalo（材料设计与创新系，布法罗大学）；NVIDIA Corp.（NVIDIA公司）；X-ray Science Division, Advanced Photon Source, Argonne National Laboratory（先进光子源X射线科学部，阿贡国家实验室）
Assessing the Reliability of Foundational Machine Learning Potentials for Evaluating the Veracity of the Crystallography Open Database
Center for Physical Sciences and Technology (FTMC)（立陶宛物理与技术中心）；Institute of Biotechnology, Life Sciences Center, Vilnius University（维尔纽斯大学生命科学中心生物技术研究所）
