Data as the next challenge in atomistic machine learning

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chiheb Ben Mahmoud, John L. A. Gardner, Volker L. Deringer
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引用次数: 0

Abstract

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.

Abstract Image

数据是原子机器学习的下一个挑战。
随着机器学习模型逐渐成为分子和材料研究的主流工具,迫切需要改进原子数据的性质、质量和可获取性。反过来,新一代普遍适用的数据集和可提炼模型也有了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.70
自引率
0.00%
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