标题： 机器学习原子间势能库：用于高效训练、模型开发和分子系统模拟 显示英文标题

标题： Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems

摘要： 机器学习间原子势（MLIP）是一种新颖的计算机模拟方法，用于分子性质预测，为打破经验力场和密度泛函理论（DFT）之间的精度/速度权衡提供了替代方案。 在本白皮书中，我们介绍了我们的 MLIP 库，该库创建时有两个核心目标：(1) 为没有机器学习背景的行业专家提供一套易于使用且计算高效的工具，以试验 MLIP 模型；(2) 为机器学习开发者提供一个框架，以开发与分子动力学工具完全集成的新方法。 本次发布中，该库包括三种模型架构（MACE、NequIP 和 ViSNet），以及两种分子动力学（MD）包装器（ASE 和 JAX-MD），并附带一组预训练的有机物模型。 特别是与 JAX-MD 的无缝集成，极大地促进了高效分子动力学模拟，使 MLIP 模型更接近工业应用。 该库在 GitHub 和 PyPI 上以 Apache 许可证 2.0 版本提供。 显示英文摘要

摘要： Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT). In this white paper, we present our MLIP library which was created with two core aims: (1) provide to industry experts without machine learning background a user-friendly and computationally efficient set of tools to experiment with MLIP models, (2) provide machine learning developers a framework to develop novel approaches fully integrated with molecular dynamics tools. The library includes in this release three model architectures (MACE, NequIP, and ViSNet), and two molecular dynamics (MD) wrappers (ASE, and JAX-MD), along with a set of pre-trained organics models. The seamless integration with JAX-MD, in particular, facilitates highly efficient MD simulations, bringing MLIP models significantly closer to industrial application. The library is available on GitHub and on PyPI under the Apache license 2.0.