Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

翻译：在分子模拟中实现计算速度、预测精度与普适性之间的平衡，一直是一个持续的挑战。本文介绍了TorchMD-Net软件的重大进展，这是从传统力场向基于神经网络的势能模型转变的关键一步。我们重点阐述了TorchMD-Net如何演变为一个更全面、更通用的框架，并集成了如TensorNet等尖端架构。这一转变通过模块化设计方法实现，鼓励科学界进行定制化应用。最显著的改进是计算效率的大幅提升，在TensorNet模型的能量和力计算上实现了非常显著的加速，性能相比之前版本提高了2倍到10倍。其他增强功能包括支持周期性边界条件的高度优化的近邻搜索算法，以及与现有分子动力学框架的平滑集成。此外，更新版本还引入了整合物理先验知识的能力，进一步丰富了其应用范围和科研实用性。该软件可在 https://github.com/torchmd/torchmd-net 获取。