Data as the next challenge in atomistic machine learning

doi:10.1038/s43588-024-00636-1

2024-06-12

PRE

Chiheb Ben Mahmoud

John L. A. Gardner

Volker L. Deringer *

DOI：10.1038/s43588-024-00636-1

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摘要

En 中文

As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.

期刊

Nature Computational Science

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18.3

论文数:

3.1K

被引数:

4.0K

机构

university of oxford

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9.8W

论文数: 8.6W

被引数: 137

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