发表机构
Technical University of Munich; Technical University of Denmark（慕尼黑工业大学; 丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FLOW-OTTER框架通过自动化分子动力学与哈密顿量预测，结合独立学习的核电子模型，仅用零压第一性原理数据重现卤化物钙钛矿的温度和压力依赖带隙，并揭示Pb-s/Br-p反键演化解释不对称压力响应，确立了哈密顿量学习作为光电机制研究的通用途径。
AI 中文摘要
在热力学状态空间中预测光电响应，需要将有限温度核动力学与电子结构耦合，而直接的第一性原理计算在此尺度上是不切实际的。机器学习力场和哈密顿量学习模型提供了可扩展的预测，但将其整合到可靠且可解释的工作流程中仍具挑战性。在此，我们引入FLOW-OTTER，一个模块化、模型无关的框架，可自动化分子动力学、哈密顿量预测、可观测量提取、可靠性评估和哈密顿量级解释。我们在卤化物钙钛矿中演示了FLOW-OTTER，这类软半导体的非谐涨落强烈调制电子响应。利用FLOW-OTTER，我们展示了独立学习的核与电子模型在端到端组合时仍保持预测能力，仅使用零压下第一性原理目标训练的模型，即可重现实验上温度依赖和压力依赖的带隙趋势。通过解析价带最大值处Pb-$s$/Br-$p$反键的非线性演化，它识别了不对称压力响应的微观起源。因此，FLOW-OTTER确立了哈密顿量学习作为从热力学轨迹到实验验证的光电机制的通用途径。
英文摘要
Predicting optoelectronic response across thermodynamic state space requires coupling finite-temperature nuclear dynamics to electronic structure at scales where direct first-principles calculations are impractical. Machine-learning force fields and Hamiltonian-learning models provide scalable predictions, but integrating them into reliable and interpretable workflows remains challenging. Here, we introduce FLOW-OTTER, a modular, model-agnostic framework that automates molecular dynamics, Hamiltonian prediction, observable extraction, reliability assessment, and Hamiltonian-level interpretation. We demonstrate FLOW-OTTER in halide perovskites, soft semiconductors whose anharmonic fluctuations strongly modulate electronic response. Using FLOW-OTTER, we show that independently learned nuclear and electronic models remain predictive when composed end-to-end, reproducing experimental temperature- and pressure-dependent band-gap trends using models trained only on first-principles targets at zero pressure. By resolving the nonlinear evolution of Pb-$s$/Br-$p$ antibonding at the valence-band maximum, it identifies the microscopic origin of the asymmetric pressure response. FLOW-OTTER thus establishes Hamiltonian learning as a general route from thermodynamic trajectories to experimentally grounded optoelectronic mechanisms.
