在本文中，我们将处理到数据驱动的性能建模的锂基尖晶石电池材料。当考虑具有Me作为一种或多种金属的LiMe 2 O 4形式的尖晶石化合物时，假设最多三种金属元素，存在125，000种可能的组合。在这项工作中，我们专注于预测属性的容量，并通过将处理纳入建模中，将可能的组合数量增加到200万。由于工艺和性能之间的非线性关系，以及确保适当的灵敏度，一种新的方法，跟踪变化的工艺。这项工作为指导下一代电池实验提供了宝贵的工具，大大缩小了无限的设计空间。
英文摘要：
In this paper, we incorporate processing into the data driven property modeling of Li-based spinel battery materials. When considering spinel compounds of the form LiMe2O4 with Me as a metal or metals, there are 125,000 possible combinations assuming a maximum of three metallic elements. In this work, we focus on capacity for our predicted property, and increase the number of possible combinations to two million by incorporating processing into the modeling. Due to the non-linear relationship between processing and property, as well as ensuring proper sensitivity, a new approach which tracks the change with changing processing is introduced. This work provides an invaluable tool for guiding the next generation of battery experiments, providing a significant narrowing of the infinite design space.
Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-ion Battery Chemistry by Mass Spectrometry and Electro-Chemical Reaction Networks
Bakar Institute of Digital Materials for the Planet, University of California at Berkeley（加州大学伯克利分校巴克数字材料星球研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Department of Materials Science and Engineering, University of California at Berkeley（加州大学伯克利分校材料科学与工程系）；Université de Rouen Normandie, INSA Rouen Normandie, Université de Caen Normandie, ENSICAEN, CNRS, Institut CARMeN UMR 6064（鲁昂诺曼底大学、鲁昂国立应用科学学院、卡昂诺曼底大学、ENSICAEN、法国国家科学研究中心CARMeN联合研究所）；TotalEnergies OneTech, TotalEnergies Research & Technology Gonfreville（道达尔能源OneTech、道达尔能源贡夫勒维尔研究与技术部）；Saft, Corporate Research（萨博公司企业研究部）；International Joint Laboratory-Complex Matrices Molecular Characterization (iC2MC)（国际联合实验室-复杂基体分子表征）；TotalEnergies OneTech Belgium（道达尔能源OneTech比利时）；TotalEnergies EP Research & Technology（道达尔能源勘探与生产研究与技术部）；Universite de Pau et des Pays de l’Adour, CNRS, IPREM（波城与阿杜尔地区大学、法国国家科学研究中心IPREM）
Redox Chemistry of LiCoO$_2$, LiNiO$_2$, and LiNi$_{1/3}$Mn$_{1/3}$Co$_{1/3}$O$_2$ Cathodes: Deduced via XPS, DFT+DMFT, and Charge Transfer Multiplet Simulations
A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
基于Ni-Ti-Al系统中玻璃形成的一组机器学习预测，我们对该系统进行了高通量实验研究。我们利用快速合成，然后进行高通量的结构和电化学表征。使用这种双模态方法，我们能够更好地分类库的无定形部分，我们发现其是对于第一尖锐X射线衍射峰具有0.42 A $^{-1}$的半高全宽（FWHM）的部分。我们表明，半高宽和耐腐蚀性是相关的，但化学仍然发挥作用，一个大的半高宽是必要的最佳耐腐蚀性。
High-Throughput Photovoltaic Screening and Spacer-Dependent Shift Current in Two-Dimensional Hybrid Perovskites
Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute of Materials Science, Technische Universität Darmstadt（达姆施塔特工业大学材料科学研究所）；North University of China（中北大学）；State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument（极端环境光电动态测量技术与仪器国家重点实验室）；Materials Genome Institute, Shanghai University（上海大学材料基因组研究院）；Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China（福建省光电信息科技创新实验室）
Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations
Materials Sciences Division, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室材料科学部）；Department of Materials Science and Engineering, University of California, Berkeley（加州大学伯克利分校材料科学与工程系）
Decoding Oxygen K-edge Fingerprints of NCM-811 Degradation via Ab Initio Many-Body Theory and High-Throughput Screening of Crystal Proxies
Carl von Ossietzky Universit\"at Oldenburg, Institute of Physics, 26129 Oldenburg, Germany；Department Interface Design, Helmholtz-Zentrum Berlin f\"ur Materialien und Energie GmbH, Berlin, Germany；Friedrich-Schiller-Universit\"at Jena, Institute of Condensed Matter Theory
A Potential Cathode Material for Rechargeable Potassium-Ion Batteries Inducing Manganese Cation and Oxygen Anion Redox Chemistry: Potassium-Deficient $\rm K_{0.4}Fe_{0.5}Mn_{0.5}O_2$
Harnessing X-ray Absorption Spectroscopy Data through Multimodal Mining of Battery Literature
Argonne National Laboratory（阿贡国家实验室）；NVIDIA（英伟达）；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）；The University of Chicago（芝加哥大学）
Orbital-interaction-aware deep learning model for efficient surface chemistry simulations
State Key Laboratory of Green Chemical Engineering and Industrial Catalysis, center for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology（华东理工大学）；State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University（上海交通大学）
Extracting a nitrile-centered, ether-assisted motif hierarchy for lithium-battery electrolyte design from billion-scale molecular space
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Suzhou Big Data & AI Research and Engineering Center（苏州大数据与人工智能研究与工程中心）；Contemporary Amperex Technology Co. Limited（宁德时代新能源科技股份有限公司）；AI for Science Institute（人工智能科学研究所）
