测试谷歌神经网络泛函DM21在分子几何优化中的表现
On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimization
- 用PySCF实现DM21泛函的几何优化，对比传统泛函
- 发现神经网络泛函存在振荡问题，影响收敛稳定性
- 提出改进方案提升实用性，适合新物质建模研究者
密度泛函理论（DFT）因其精度与速度的平衡，是量子化学计算中最具前景的方法之一。近年来，基于神经网络的交换-关联泛函被开发用于提高精度，其中谷歌DeepMind提出的DM21最为突出。本研究聚焦于评估DM21在预测分子几何结构方面的效率，特别关注神经网络泛函中出现的振荡行为对优化的影响。我们在PySCF中实现了DM21泛函的几何优化，并与传统泛函进行比较，测试了多种基准数据集。结果揭示了神经网络泛函在几何优化中的潜力与当前挑战。本文提出一种改进方法，扩展了此类泛函的实际应用范围，使它们可用于新物质的建模。
原文摘要 · Abstract (English)
Density functional theory (DFT) is probably the most promising approach for quantum chemistry calculations considering its good balance between calculations precision and speed. In recent years, several neural network-based functionals have been developed for exchange-correlation energy approximation in DFT, DM21 developed by Google Deepmind being the most notable between them. This study focuses on evaluating the efficiency of DM21 functional in predicting molecular geometries, with a focus on the influence of oscillatory behavior in neural network exchange-correlation functionals. We implemented geometry optimization in PySCF for the DM21 functional in geometry optimization problem, compared its performance with traditional functionals, and tested it on various benchmarks. Our findings reveal both the potential and the current challenges of using neural network functionals for geometry optimization in DFT. We propose a solution extending the practical applicability of such functionals and allowing to model new substances with their help.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。