AI 中文总结
本文提出iso-EPIGS框架，基于质量可微神经网络重构自由能面，以经典计算成本实现同位素效应的准确模拟，且对未见过的部分氘代同位素体具有强迁移性，为相关基础模型构建奠定基础。
AI 中文摘要
同位素效应支配分馏过程并调控反应活性，其应用涵盖氢能、环境科学及催化等领域，但预测同位素效应需解析微小的同位素依赖自由能差，这对复杂体系中传统路径积分模拟而言仍极具挑战性。本文提出iso-EPIGS，一种基于质量可微神经网络构建的路径积分粗粒化框架，该框架可重构依赖质量与温度的路径积分质心自由能面。在学习得到的表面上进行经典分子动力学模拟，无需显式路径积分采样即可得到严格的同位素分辨热力学量。对气相、液相及晶相（包括液态水和草酸晶体）的基准测试显示，其能以接近经典计算成本重现参考路径积分的同位素自由能差、焓值及晶格参数。关键的是，仅用全氢与全氘同位素体训练的iso-EPIGS，对未见过的部分氘代同位素体仍保持高准确度，展现出跨核质量的强迁移性。iso-EPIGS使复杂体系的准确同位素效应模拟成为可能，并为构建覆盖化学空间的同位素效应基础模型奠定了基础。
英文摘要
Isotope effects govern fractionation and modulate reactivity, with applications from hydrogen energy to environmental science and catalysis, yet predicting them requires resolving small isotope-dependent free-energy differences that remain very challenging for conventional path-integral simulations in complex systems. Here we introduce iso-EPIGS, a path-integral coarse-graining framework built on a mass-differentiable neural network that reconstructs the mass- and temperature-dependent path integral centroid free-energy surface. Classical molecular dynamics on the learned surface yields rigorous isotope-resolved thermodynamics without explicit path-integral sampling. Benchmarks spanning gas, liquid, and crystalline phases, including liquid water and oxalic acid crystal, reproduce reference path-integral isotope free-energy differences, enthalpies, and lattice parameters at near classical computational cost. Crucially, iso-EPIGS trained solely on all-H and all-D isotopologues retains high accuracy for unseen partially deuterated isotopologues, demonstrating robust transferability across nuclear masses. Iso-EPIGS makes accurate isotope-effect simulations feasible for complex systems and lays the groundwork for foundation models of isotope effects across chemical space.
