从稀疏量子计算数据到基于通用机器学习原子间势的原子模拟

中文摘要 AI 辅助

我们提出了一个框架，用于将基于量子计算的电子结构计算纳入通用机器学习原子间势（uMLIPs）。我们不是从头构建原子间势，而是利用从量子计算获得的一小组精确参考能量来微调预训练的基于密度泛函理论（DFT）的uMLIP。我们针对三个化学上不同的应用展示了该方法：Menshutkin反应、金属有机框架HKUST-1中的水吸附，以及高熵合金纳米颗粒上的CO跳跃。对于Menshutkin反应，在气相构型上的微调改善了碳纳米管内过渡态的能量，但未改善产物能量。对于HKUST-1中的水吸附，仅使用14个参考构型进行微调，使得通过Widom插入采样的数百万个构型获得的吸附热力学与参考值更加一致。对于IrPdPtRhRu纳米颗粒上的CO跳跃，尽管参考数据仅包含能量，但在增强采样分子动力学获得的有限温度自由能分布中，恢复了顶位吸附相对于桥位吸附的偏好。这些结果表明，所提出的框架为将量子计算纳入实际原子模拟提供了一条实用途径，并且量子计算参考数据可以改进预训练的uMLIPs。

英文摘要

We propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than constructing an interatomic potential from scratch, we refine a pretrained DFT-based uMLIP using a small set of accurate reference energies obtained from quantum computing. We demonstrate the approach for three chemically distinct applications: the Menshutkin reaction, water adsorption in the metal-organic framework HKUST-1, and CO hopping on a high-entropy-alloy nanoparticle. For the Menshutkin reaction, fine-tuning on gas-phase configurations improves the transition-state energy inside a carbon nanotube but not the product energy. For water adsorption in HKUST-1, fine-tuning with only 14 reference configurations brings adsorption thermodynamics obtained from millions of configurations sampled by Widom insertion into closer agreement with reference values. For CO hopping on an IrPdPtRhRu nanoparticle, the preference for on-top over bridge adsorption is recovered in the finite-temperature free-energy profile obtained from enhanced-sampling molecular dynamics, even though the reference data contain only energies. These results demonstrate that the proposed framework provides a practical route for incorporating quantum-computing calculations into realistic atomistic simulations and that quantum-computing reference data can improve pretrained uMLIPs.

发表机构

• The University of Osaka（大阪大学）

机构由 AI 辅助整理，请以论文原文为准。