使用机器学习的充电集群的DFTB模拟 电荷推理
Paul Guibourg1, Léo Dontot1, Pierre-Matthieu Anglade1
1Laboratoire Cimap, UMR6252─Université de Caen Normandie, École Nationale Supérieure d'Ingénieures de Caen, Commissariat à l'Énergie Atomique, Centre National de la Recherche Scientifique, 6 Boulevard Du Maréchal Juin, 14050 Caen Cedex, France.
Journal of chemical theory and computation
|May 1, 2024
概括
我们开发了一种机器学习方法来近似原子电荷,从而实现更快的基于自相一致的电荷密度函数的紧密结合 (SCC-DFTB) 计算. 这种方法显著降低了计算成本,同时保持了材料科学模拟的准确性.
科学领域:
- 计算材料科学科学 计算材料科学
- 量子化学 是一个量子化学.
- 机器学习在物理学中的应用
背景情况:
- 基于自相一致的电荷密度功能紧密结合 (SCC-DFTB) 是用于电子结构计算的强大方法.
- 传统的SCC-DFTB需要代的自我一致周期,这可能是计算上昂贵的,限制了研究的系统的大小.
- 精确的原子电荷对于许多化学和物理性质至关重要,但它们的精确计算可能很苛刻.
研究的目的:
- 引入SCC-DFTB的新型修改,绕过代自我一致的电荷计算.
- 开发一种机器学习 (ML) 模型,以快速准确地预测原子电荷.
- 为了使更大的原子组合和复杂的化学系统可以在减少计算开销的情况下进行调查.
主要方法:
- 结合库伦模型和神经网络的机器学习算法被开发来预测原子电荷.
- ML模型采用原子位置,由对称函数描述,作为输入.
- ML-DFTB方法执行单个对角化,近似密度矩阵,能量和力.
主要成果:
- ML预测的原子电荷与精确的SCC解决方案 (在10-2电荷单位内) 密切匹配.
- ML-DFTB方法准确地复制了带电碳化 (SiC) 集群的潜在能量表面 (PES).
- 离子排放的分裂障碍物得到了很好的复制,这表明该方法适合研究带电集群稳定性和离子场排放.
结论:
- 与标准的SCC-DFTB相比,ML-DFTB方法提供了显著的计算节省,而不会影响准确性.
- 这种方法有助于研究更大的原子系统,包括表面和固态材料.
- ML-DFTB方法为探索充电集群动态和相关现象提供了一个新的途径.
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