用机器学习将量子计算结果逐步简化，实现高精度反应自由能预测。
Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians
- 通过层级蒸馏，从少量高精度量子计算中提取知识构建粗粒化模型。
- 在不损失电子自由度的前提下，精确模拟长程静电与环境量子响应。
- 首次从头算预测弱酸解离常数和酶促反应速率，误差在化学精度内。
凝聚相化学反应的自由能计算对高阶量子力学方法而言仍极耗算力。本文提出一种分层机器学习框架，通过从少量高保真量子计算中蒸馏知识，构建逐级粗化的机器学习量子哈密顿量。保留显式电子自由度，该方法可精确嵌入量子与经典自由度，捕捉长程静电作用及环境对量子态的无限阶响应。验证表明，我们完全基于第一性原理计算了弱酸质子解离常数与酶促反应动力学速率，结果与实验值在化学精度或其不确定度范围内一致。本工作为实现最高精度下凝聚相反应自由能的收敛统计模拟提供了可行路径。
原文摘要 · Abstract (English)
Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling knowledge from a small number of high-fidelity quantum calculations into increasingly coarse-grained, machine-learned quantum Hamiltonians. By retaining explicit electronic degrees of freedom, our approach further enables a faithful embedding of quantum and classical degrees of freedom that captures long-range electrostatics and the quantum response to a classical environment to infinite order. As validation, we compute the proton dissociation constants of weak acids and the kinetic rate of an enzymatic reaction entirely from first principles, reproducing experimental measurements within chemical accuracy or their uncertainties. Our work demonstrates a path to condensed phase simulations of reaction free energies at the highest levels of accuracy with converged statistics.
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