用图神经网络自动学习分子运动的低维表示，无需预设特征
Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
- 结合图神经网络与状态预测信息瓶颈，从原子坐标自动提取特征
- 在三个基准系统上准确预测结构、热力学和动力学信息
- 适合复杂体系的增强采样，无需人工定义反应坐标
分子动力学模拟可提供原子运动的详细信息，但受限于时间尺度。尽管增强采样方法已部分解决此问题，当前基于机器学习的方法仍常依赖专家预选特征。本文提出图神经网络-状态预测信息瓶颈（GNN-SPIB）框架，结合图神经网络与状态预测信息瓶颈，直接从原子坐标中自动学习低维表示。在三个基准系统上的测试表明，该方法能有效预测慢过程中的关键结构、热力学与动力学信息，展现出跨不同系统的鲁棒性。该方法为复杂体系的增强采样提供了新路径，无需预定义反应坐标或输入特征。
原文摘要 · Abstract (English)
Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. In this work, we present the Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the State Predictive Information Bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.
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。