Δ-机器学习以提升DFT基础的潜力和力场到CCSD ((T) 以乙醇为例的水平
Apurba Nandi1, Priyanka Pandey2, Paul L Houston3,4
1Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.
Journal of chemical theory and computation
|October 3, 2024
概括
机器学习潜力,使用三角形机器学习,显著提高低级电子结构计算的准确性. 这种方法改善了密度函数理论 (DFT) 和分子力学力场,用于分子建模.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 通过开发精确的潜在能量表面 (PES) 来加速计算.
- 德尔塔机器学习 (Δ-ML) 已经成功地提高了使用B3LYP函数的各种分子的PES精度,达到接近合集群水平.
- 将 Δ-ML 扩展到其他密度函数理论 (DFT) 函数对于更广泛的适用性至关重要.
研究的目的:
- 调查 Δ-ML 对提高不同 DFT 函数的 PES 精度的一般适用性.
- 用以乙醇作为模型系统的PBE,M06,M06-2X和PBE0 + MBD函数来评估Δ-ML的性能.
- 评估 Δ-ML 在纠正分子力学力场方面的潜力.
主要方法:
- 使用线性回归与 permutationally不变的多项式,以适应低级和校正 PES.
- 在训练和测试数据集上使用根-平均-平方误差 (RMSE) 分析.
- 进行一般的忠实性测试,包括静止点能量,正常模式频率和扭矩潜力.
主要成果:
- 在所有测试的DFT函数 (PBE,M06,M06-2X,PBE0 + MBD) 中,Δ-ML显著提高了PES的准确性.
- 即使在没有使用合集群梯度进行校正时,也观察到实质性的改善.
- 证明了 Δ-ML 的成功应用,以纠正乙醇的分子力学力场.
结论:
- Δ-ML是一种通用和有效的方法,用于提高各种DFT函数和分子力学力场的精度.
- 这种方法提供了一种途径,以降低计算成本实现高精度的分子模拟.
- 需要进一步探索Δ-ML在各种化学系统和力场中的普遍性.
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