MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package faciliates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. The package is integrated currently with the mesocale and molecular dynamics simulation package LAMMPS and PyTorch. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.

翻译：MLMOD是一款用于将机器学习方法及模型融入LAMMPS微观力学与分子动力学模拟中的软件包。近期机器学习方法为从实验数据及高保真模拟中学习系统行为表征提供了有前景的数据驱动途径。该软件包支持学习并应用数据驱动模型，用于：(i)更大时空尺度下的系统动力学，(ii)系统组件间的相互作用，(iii)产生粗粒化自由度的特征，以及(iv)表征系统行为的新关注量的特征。MLMOD在LAMMPS中提供了以下接口：(i)动力学建模与时间步积分，(ii)相互作用建模，以及(iii)计算表征系统状态的关注量。该软件包支持使用包含神经网络、高斯过程回归、核模型及其他方法在内的通用模型类进行机器学习。本文讨论了我们的原型C++/Python软件包、研究目标及用例。该软件包目前已与介观/分子动力学模拟软件LAMMPS及PyTorch实现集成。相关论文、示例、更新及更多信息请参见https://github.com/atzberg/mlmod 及 http://atzberger.org/。