Exploring the necessary complexity of interatomic potentials
Exploring the necessary complexity of interatomic potentials
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DOI：
10.1016/j.commatsci.2021.110752
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发表时间：
2021-12
影响因子：
3.3
通讯作者：
Joshua A Vita;D. Trinkle
中科院分区：
文献类型：
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作者：
Joshua A Vita;D. Trinkle
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相互作用势（MLIP）被认为比经典势更灵活和准确。MLIP相对于经典势的准确性的这种提高是以显著增加的复杂性为代价的，导致更高的计算成本和更低的物理可解释性，并刺激了对提高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”拟合数据库和先进的优化算法来拟合一类基于样条的经典电位，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，高模型的复杂性可能不是严格必要的，以实现近DFT的精度在原子间的潜力，并提出了一种替代的路线，对采样的高精度，低复杂性区域的模型空间开始的形式，促进更简单，更可解释的原子间的潜力。
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.