We present ultra-fast quantum chemical methods for the calculation of infrared and ultraviolet-visible spectra designed to provide fingerprint information during autonomous and interactive explorations of molecular structures. Characteristic spectral signals can serve as diagnostic probes for the identification and characterization of molecular structures. These features often do not require ultimate accuracy with respect to peak position and intensity, which alleviates the accuracy-time dilemma in ultra-fast electronic structure methods. If approximate ultra-fast algorithms are supplemented with an uncertainty quantification scheme for the detection of potentially large prediction errors in signal position and intensity, an offline refinement will always be possible to confirm or discard the predictions of the ultra-fast approach. Here, we present ultra-fast electronic structure methods for such a protocol in order to obtain ground- and excited-state electronic energies, dipole moments, and their derivatives for real-time applications in vibrational spectroscopy and photophysics. As part of this endeavor, we devise an information-inheritance partial Hessian approach for vibrational spectroscopy, a tailored subspace diagonalization approach and a determinant-selection scheme for excited-state calculations.
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
University of Ottawa（渥太华大学）；University of Waterloo（滑铁卢大学）；National Research Council of Canada（加拿大国家研究委员会）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
本文提出ChemGymRL，一个基于Open AI Gym的开源强化学习交互式框架，通过一系列互联的虚拟化学实验台支持RL智能体训练，解决真实化学实验中数据密集且危险的问题，并验证了多种标准RL算法的性能。
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）
Universal Machine-learning Molecular Dynamics at the Speed of Empirical Potentials
Peking University（北京大学）；AI for Science Institute（人工智能科学研究所）；DP Technology（深势科技）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学交叉信息研究院）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）
迄今为止，量子硬件上的小分子量子化学计算仅在通用门量子计算机上得到证明，而不是量子退火机。后一种设备仅限于找到伊辛哈密顿量的最低本征态，而电子哈密顿量不能映射到伊辛形式，而伊辛哈密顿量不会随着系统的大小呈指数增长[J. Phys. Chem. B 122，3384（2018）]。在这里，我们提出了一种新的混合离散-连续优化算法，该算法使用量子退火器来求解问题的离散部分，从而找到量子比特耦合簇（QCC）方法的最低本征态。QCC方法是一种在量子比特空间中构造电子波函数的潜在精确方法。因此，我们的方法允许使用量子退火系统地改进量子化学计算。我们通过计算LiH、H $_2$ O和C $_6$ H $_6$分子的QCC基态电子态来说明我们的方法的能力。C $_6$ H $_6$计算涉及36个量子比特，是迄今为止在量子退火机（D-Wave 2000 Q系统）上进行的最大量子化学计算。我们的发现为量子退火机在高通量材料发现中的应用开辟了新的视角。
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）
Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；IQuest Research（IQuest研究）；University of Science and Technology of China（中国科学技术大学）
The MolecularWeb Universe: Web-Based, Immersive, Multiuser Molecular Graphics And Modeling, for Education and Work in Chemistry, Structural Biology, and Materials Sciences
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
University of Toronto（多伦多大学）；Acceleration Consortium（加速联盟）；National Research Council Canada（加拿大国家研究委员会）；Clean Energy Innovation Research Center（清洁能源创新研究中心）
V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction
State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（精密与智能化学国家重点实验室，中国科学技术大学）；Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China（微尺度物质科学国家研究中心，中国科学技术大学）；Hefei National Laboratory, University of Science and Technology of China（合肥国家实验室，中国科学技术大学）
