{"paper":{"title":"Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Bo-Ying Huang, Chang Liu, Cheng-Xuan Wang, Fan-Jie Xu, Fei-Teng Wang, Feng Wang, Fujie Tang, Fu-Qiang Gong, Jia-Bo Le, Jiang-Peng Qiu, Jian Gu, Jia-Xin Li, Jia-Xin Zhu, Jie-Qiong Li, Jie-Zhen Xia, Jing-Xiang Zou, Jin-Yuan Hu, Juan-Juan Sun, Jun Cheng, Jun-yi Wang, Lang Li, Lin Huang, Mei Jia, Meng-Lei Jia, Min Lin, Peng-Wei Xu, Qi You, Qi-Yuan Fan, Rui-Hao Bi, Sheng Bi, Si-Jie Chen, Siyuan Han, Su-Yang Zhong, Wei-Hong Xu, Xiang-Long Du, Xiao-Hui Yang, Xing-Yun Xie, Yan Sun, Yan-Yi Su, Yi-Ming Chen, Yi-Ze Wang, Yong-Bin Zhuang, Yue Liu, Yu-Hang Tang, Yu-Lei Gong, Yun-Pei Liu, Yu-Xin Guo, Zi-Heng Gong, Zi-Qiang Chen, Zixuan Wei","submitted_at":"2026-07-01T08:36:28Z","abstract_excerpt":"Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic structure, finite-temperature fluctuations, and electric-field response. Such complexity is difficult to address with traditional ab initio calculations, which are limited by the time and length scales they can reach. AI-accelerated ab initio (AI2) methods use machine learning potentials trained on first-principles data to replace expensive electronic-structure calculations, extending ab initio accuracy to these regimes, b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00613","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.00613/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}