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在化学空间中数据高效的D-ML的哈梅特启发产品基准

V Diana Rakotonirina1, Marco Bragato2, Guido Falk von Rudorff3,4

  • 1Department of Materials Science and Engineering, University of Toronto, 184 College Street, Toronto, Ontario M5S 3E4, Canada.

Journal of chemical theory and computation
|October 1, 2025
PubMed
概括

一个新的哈梅特灵感产品 (HIP) Ansatz为化学发现中的机器学习提供了一个数据效率高的基线模型. 这种方法减少了分子和材料设计中的数据需求和成本.

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