AI 中文总结
研究针对DFTB计算收敛慢的问题，提出用机器学习预测最佳初始原子电荷来加速模拟的方法，通过特定模型训练电荷模型，并证明该方法能在多种化学系统中显著改善SCC收敛。
AI 中文摘要
半经验电子结构方法如密度泛函紧束缚（DFTB）为分子和材料模拟提供了一种计算高效的方法，弥合了第一性原理精度和经典力场速度之间的差距，同时能充分获取电子性质。然而，基于自洽电荷（SCC）方案的DFTB计算仍可能收敛缓慢，特别是对于复杂分子和材料系统，这使得迭代过程成为大规模模拟和高通量工作流程中的一个重大瓶颈。我们提出一种机器学习方法，通过预测最佳初始原子电荷来加速DFTB模拟。使用基于原子位置平滑重叠描述符和核岭回归的特定元素模型，我们在参考计算上训练电荷模型，并证明机器学习预测的初始电荷在包括有机分子、生物分子、水团簇、过渡金属氧化物和固体电解质在内的各种化学系统中持续且显著地改善了SCC收敛。
英文摘要
Semiempirical electronic structure methods such as Density-Functional Tight-Binding (DFTB) offer a computationally efficient approach to molecular and materials simulations, bridging the gap between first-principles accuracy and classical force field speed while retaining full access to electronic properties. However, DFTB calculations based on self-consistent charge (SCC) schemes can still suffer from slow convergence, particularly for complex molecular and materials systems, making the iterative procedure a significant bottleneck in large-scale simulations and high-throughput workflows. We present a machine learning approach that accelerates DFTB simulations by predicting optimal initial atomic charges. Using element-specific models based on the Smooth Overlap of Atomic Positions descriptor and kernel ridge regression, we train charge models on reference calculations and demonstrate that ML-predicted initial charges consistently and significantly improve SCC convergence across diverse chemical systems including organic molecules, biomolecules, water clusters, transition metal oxides and solid electrolytes.
Comments10 pages, 2 figures
