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Updated: Jun 18, 2026

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Published on: September 17, 2021

为通用机器学习优化跨领域转移原子间潜能

Jaesun Kim1, Jinmu You1, Yutack Park1

  • 1Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Nature communications
|March 3, 2026
PubMed
概括

我们开发了一种新的机器学习原子间潜力的培训策略,提高模型的准确性和跨不同化学领域的可转移性. 这种方法通过使分子,晶体和表面的可靠预测,加速了材料的发现.

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Hybridization of Atomic Orbitals I03:24

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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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