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、预测精度与普适适用性之间的平衡始终是一项持续挑战。本文介绍了TorchMD-Net软件的重大进展，标志着从传统力场向基于神经网络的势场转变的关键一步。重点阐述了TorchMD-Net演化为更全面、更通用框架的过程，整合了TensorNet等前沿架构。这一转型通过模块化设计方法实现，鼓励科学社区的定制化应用。最显著的改进是计算效率的大幅提升，针对TensorNet模型的能量与力计算实现了从2倍到10倍的显著加速。其他增强功能包括支持周期性边界条件的高度优化近邻搜索算法，以及与现有分子动力学框架的无缝集成。此外，更新版本引入了整合物理先验知识的能力，进一步丰富了其研究应用谱系与实用性。该软件可在https://github.com/torchmd/torchmd-net获取。