MLTB:增强密度功能紧密结合理论的可转移性和可扩展性,使用多体相互作用纠正
Daniel J Burrill1, Chang Liu1, Michael G Taylor1
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Journal of chemical theory and computation
|January 29, 2025
概括
我们开发了一种机器学习紧固结合 (MLTB) 模型,用于材料科学,提高密度功能紧固结合 (DFTB) 的准确性. 这种混合方法增强了对-氧纳米集群等系统的计算.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习在物理学中的应用
背景情况:
- 标准自相一致的电荷密度功能紧固结合 (SCC-DFTB) 为电子结构计算提供了一种计算效率高的方法.
- 现有的DFTB模型通常缺乏对复杂系统的准确性,这是由于描述原子间相互作用的局限性.
- 像HIP-NN这样的机器学习潜力显示出改善交互描述的潜力,但可能是计算上昂贵或缺乏可转移性.
研究的目的:
- 开发一种混合计算模型,将DFTB的效率与机器学习潜力的精度相结合.
- 为准确的电子结构计算创建一个更可转移和可扩展的模型.
- 提供一个实用的框架,以提高DFTB的多体校正.
主要方法:
- 通过将机器学习神经网络潜力 (HIP-NN) 集成到标准自相一致的电荷密度功能紧固结合 (SCC-DFTB) 形式主义中,开发了一种混合机器学习紧固结合 (MLTB) 模型.
- 在SCC-DFTB框架内,HIP-NN潜力被用作SCC-DFTB框架内的排斥性术语的多体校正.
- 开发的MLTB模型被应用于研究氧系统 (ThO2) 的纳米集群结构.
主要成果:
- 与独立的SCC-DFTB和HIP-NN模型相比,MLTB模型显著改善了可转移性和可扩展性.
- 混合方法成功提高了DFTB计算的准确性,使其更接近更计算密集型DFT方法的准确性.
- 为氧系统开发了一个准确的MLTB模型,并用于纳米集群结构调查.
结论:
- MLTB模型为材料科学研究提供了一个实用且准确的计算框架.
- 这种混合方法有效地将多体校正纳入DFTB,增强其预测能力.
- 开发的方法为更可靠,更准确的复杂材料系统模拟铺平了道路.
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