Wang Xinyan, Li Jichen, Yang Lan, Chen Feiyang, Wang Yingze, Chang Junhan, Chen Junmin, Feng Wei, Zhang Linfeng, Yu Kuang
DP Technology, Beijing 100080, P. R. China.
Tsinghua-Berkley Shenzhen Institute, Shenzhen, Guangdong 518055, P. R. China.
J Chem Theory Comput. 2023 Sep 12;19(17):5897-5909. doi: 10.1021/acs.jctc.2c01297. Epub 2023 Aug 17.
In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role in determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on human experience, which is not scalable. To address this issue, we introduce DMFF, an open-source molecular FF development platform based on an automatic differentiation technique. DMFF serves as a powerful tool for both top-down and bottom-up FF development. Using DMFF, both energies/forces and thermodynamic quantities such as ensemble averages and free energies can be evaluated in a differentiable way, realizing an automatic, yet highly flexible FF optimization workflow. DMFF also eases the evaluation of forces and virial tensors for complicated advanced FFs, helping the fast validation of new models in molecular dynamics simulation. DMFF has been released as an open-source package under the LGPL-3.0 license and is available at https://github.com/deepmodeling/DMFF.
在分子系统模拟中，基础力场（FF）模型在决定模拟的可靠性方面起着极其重要的作用。然而，在许多情况下，当前最先进的分子力场的质量仍不尽人意，并且FF参数化过程在很大程度上依赖于人类经验，缺乏可扩展性。为了解决这个问题，我们引入了DMFF，一个基于自动微分技术的开源分子FF开发平台。DMFF是一个用于自上而下和自下而上FF开发的强大工具。使用DMFF，能量/力以及诸如系综平均值和自由能等热力学量都可以以可微的方式进行评估，实现了一个自动但高度灵活的FF优化工作流程。DMFF还简化了对复杂先进FF的力和维里张量的评估，有助于在分子动力学模拟中快速验证新模型。DMFF已作为一个开源软件包根据LGPL - 3.0许可发布，可在https://github.com/deepmodeling/DMFF获取。
