将固定的电荷与库伦相互作用纳入瞬间张量潜力和等价张量网络潜力
Dmitry Korogod1,2,3, Olga Chalykh1, Max Hodapp4
1Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 143026, Russian Federation.
The Journal of chemical physics
|February 12, 2026
概括
这项研究将远程静电相互作用整合到机器学习原子间潜力 (MLIP) 中. 这大大减少了能量适配错误,并改善了带电有机分子的预测.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习在物理学中的应用
背景情况:
- 短距离机器学习原子间潜力 (MLIP) 往往难以准确地建模静电相互作用.
- 对充电分子的准确建模对于理解化学反应和材料特性至关重要.
研究的目的:
- 通过使用固定电荷的库伦模型结合远程静电相互作用来增强MLIP.
- 提高MLIP对带电有机分子及其结合性质的准确性.
主要方法:
- 将固定电荷的库伦模型纳入动量张量电位和等价张量网络电位的函数形式.
- 在有电荷分子有机二极体数据集上的增强MLIP的培训和验证.
- 将MLIP预测与密度函数理论 (DFT) 的结果进行比较.
主要成果:
- 对库伦相互作用的明确包括在内,在短距离的MLIP中,能量的拟合误差减少了四倍以上.
- 开发的远程MLIP在预测带电有机二元体的结合曲线方面取得了显著的改进.
- MLIP的结果与研究系统的DFT计算有很好的一致性.
结论:
- 整合远程静电相互作用是提高MLIP精度的高效策略,特别是在充电系统中.
- 改进的MLIP为研究带电有机分子提供了一个计算效率高,准确的替代DFT.
- 这项工作为涉及带电物种的更可靠的分子模拟铺平了道路.
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