AI for Science期刊第二卷第一期文章出版
得益于与中国科学院东莞材料科学与技术研究所的合作支持，期刊目前实行钻石开放获取模式，所有发表费用均已覆盖。
- 读者：可即时、免费获取全部前沿研究成果；
- 作者：可免费发表研究文章。
本期内容包括：
- 利用机器学习加速拉曼计算，用于研究固态电解质中的快速离子传导；
- 基于MOSES框架的本体驱动多智能体化学知识推理；
- 数据驱动的金属玻璃设计，实现实验与理论的更紧密结合；
- 以及更多精彩内容……
亮点文章
Letter
Manuel Grumet, Takeru Miyagawa, Olivier Pittet, Paolo Pegolo, Karin S Thalmann, Waldemar Kaiser and David A Egger
Focus Issue on Machine Learning Potentials and Mapping of Atomic Structures
Topical Review
Huanrong Liu, Shan Zhang, Qingan Li, Bin Xu, Jian Li and Pengfei Guan
Perspective
Learning atomic representations for data-driven materials design
Zhenyao Fang, Ting-Wei Hsu and Qimin Yan
Focus Issue on Machine Learning Potentials and Mapping of Atomic Structures
Papers
Yingkai Sun, Feiyang Xu, Huadong Liang, Xianghui Fan, Guozhu Wan, Wenwan Zhong, Jun Jiang, Xin Li and Linjiang Chen
Artificial intelligence driven workflow for accelerating design of novel photosensitizers
Hongyi Wang, Xiuli Zheng, Weimin Liu, Zitian Tang and Sheng Gong
Da Long, Yabo Wang, Tian Li and Lifen Sun
Beyond Adam: disentangling optimizer effects in the fine-tuning of atomistic foundation models
Xiaoqing Liu, Yangshuai Wang and Teng Zhao
Focus Issue on Machine Learning Potentials and Mapping of Atomic Structures
Hongyu Wu, Ruoyu Wang, Xin Chen and Zhicheng Zhong
Focus Issue on Machine Learning Potentials and Mapping of Atomic Structures
期刊介绍
- AI for Science是一本跨学科、国际同行评审的钻石开放获取期刊，致力于发表具有重大影响力的原创研究、综述和观点文章，聚焦人工智能（AI）在推动科学创新方面的变革性应用。本刊由IOP出版社和中国科学院东莞材料科学与技术研究所合办。