Reactive Force Field Design Guided by Energy Decomposition Analysis

在化学系化学理论、模型和计算方法计划的支持下，加州大学伯克利分校的Teresa Head-Gordon教授和Martin Head-Gordon教授将在能量分解分析(EDA)的新进展指导下开发下一代力场。力场是一种经验函数，其目的是准确而廉价地描述体系势能随原子和分子位置的变化。分子模拟通过提高力场的准确性来推进，这被认为是一个困难但本质的挑战，可以使用EDA部分解决。EDA对一组分子进行先进的量子力学计算，并将它们的相互作用能量提炼成一系列项，以捕捉相斥和吸引的物理驱动力。这些术语提供了宝贵的第一性原理数据，为新力场的设计和参数化提供了信息。这项研究应该在特定的化学体系上产生新的科学方法和结果。更广泛的影响将包括软件传播、机器学习模型、高质量数据生成以及教育、培训和推广。将为在较短任期内较难找到研究实验室的大三转学本科生提供研究机会。分子科学和软件工程(MSSE)专业硕士学位的发展将有助于培养一支为编程、数据建模和机器学习做好充分准备的多元化劳动力队伍。具体地说，加州大学伯克利分校的合作研究团队将制定一种新的吉布斯分解分析(GDA)，以揭示分子驱动力和热熵权衡之间的联系。GDA将适用于FF模拟，以探索控制界面化学的分子相互作用。对于非共价相互作用，将提出静电极化的EDA，以揭示每个碎片的能量降低和轨道重排。将进行新的分析，以协调电荷转移的实空间和希尔伯特空间的测量，并理解泡利弛豫。EDA的这些进展有可能回答有关非键合作用的基本问题，并有助于指导使用电荷平衡来定义极化和电荷转移的FF开发。最近出现的表示潜在表面的机器学习为化学键的反应力场开发提供了一种补充方法。NewtonNet机器学习模型将使用EDA数据进行训练，这些数据可以集成到力场中。总而言之，这些研究的主要目标是提供更强大的EDA工具以及机器学习模型，以将高级势能面的范围扩展到非键相互作用、反应化学，并更好地解释凝聚相和界面系统中的可观测数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professors Teresa Head-Gordon and Martin Head-Gordon of the University of California Berkeley will develop next-generation force fields guided by new advances in energy decomposition analysis (EDA). Force fields (FFs) are empirical functions that aim to describe how the potential energy of a system varies with position of the atoms and molecules both accurately and inexpensively. Molecular simulations advance by improving the accuracy of the force field, which is recognized as a difficult yet essential challenge, which can partly be addressed using EDA. An EDA takes advanced quantum mechanical calculations on groups of molecules, and distills their interaction energy into a sum of terms that capture repulsive and attractive physical driving forces. These terms provide valuable first principles data to inform the design and parameterization of new force fields. The research should yield new scientific methods and results on specific chemical systems. Broader impacts will encompass software dissemination, machine learning models, high quality data generation, and education, training, and outreach. Research opportunities will be provided for junior transfer undergraduates who have more difficulty finding research labs during their shorter tenure. Development of a Professional Masters in Molecular Sciences and Software Engineering (MSSE) degree will aid development of a diverse workforce that is highly prepared for programming, data modeling, and machine learning.Specifically, the collaborative research team at UC-Berkeley will formulate a new Gibbs decomposition analysis (GDA) to unravel connections between molecular driving forces and enthalpy-entropy trade-offs. GDA will be adapted to FF simulations in order to probe the molecular interactions controlling interfacial chemistry. For non-covalent interactions, EDA for electrostatic polarization will be advanced to reveal each fragment’s energy lowering and orbital rearrangements. New analysis will be performed to reconcile real space and Hilbert space measures of charge transfer, and to understand Pauli relaxation. These EDA advances have the potential to answer basic questions about non-bonded interactions and help guide the FF development that use charge equilibration to define polarization and charge transfer. The recent emergence of machine learning to represent potential surfaces offers a complementary approach to reactive force field development for chemical bonding. The NewtonNet machine learning model will be trained with EDA data that can be integrated into force fields. In summary, the main objectives of these studies are to provide more powerful EDA tools as well as machine learning models to expand the scope of advanced potential energy surfaces to non-bonded interactions, reactive chemistry, and to better interpretation of observables in condensed phase and interfacial systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.