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Exploring the necessary complexity of interatomic potentials
Computational Materials Science ( IF 3.3 ) Pub Date : 2021-08-19 , DOI: 10.1016/j.commatsci.2021.110752
Joshua A. Vita 1 , Dallas R. Trinkle 1
Affiliation  

The application of machine learning models and algorithms towards describing atomic interactions has been a major area of interest in materials simulations in recent years, as machine learning interatomic potentials (MLIPs) are seen as being more flexible and accurate than their classical potential counterparts. This increase in accuracy of MLIPs over classical potentials has come at the cost of significantly increased complexity, leading to higher computational costs and lower physical interpretability and spurring research into improving the speeds and interpretability of MLIPs. As an alternative, in this work we leverage “machine learning” fitting databases and advanced optimization algorithms to fit a class of spline-based classical potentials, showing that they can be systematically improved in order to achieve accuracies comparable to those of low-complexity MLIPs. These results demonstrate that high model complexities may not be strictly necessary in order to achieve near-DFT accuracy in interatomic potentials and suggest an alternative route towards sampling the high accuracy, low complexity region of model space by starting with forms that promote simpler and more interpretable interatomic potentials.



中文翻译:

探索原子间势的必要复杂性

近年来,机器学习模型和算法在描述原子相互作用方面的应用一直是材料模拟中的一个主要领域,因为机器学习原子间势 (MLIP) 被认为比经典势对应物更灵活和准确。MLIP 相对于经典势的准确性的提高是以显着增加复杂性为代价的,导致更高的计算成本和更低的物理可解释性,并刺激了提高 MLIP 的速度和可解释性的研究。作为替代方案,在这项工作中,我们利用“机器学习”拟合数据库和高级优化算法来拟合一类基于样条的经典势,表明它们可以系统地改进,以达到与低复杂度 MLIP 相当的精度。这些结果表明,为了在原子间势能中实现接近 DFT 的准确度,高模型复杂性可能不是绝对必要的,并提出了一种替代途径,通过从促进更简单和更可解释的形式开始,对模型空间的高精度、低复杂性区域进行采样原子间势。

更新日期:2021-08-20
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