Reactive Force Field Design Guided by Energy Decomposition Analysis

能量分解分析引导的反作用力场设计

• 批准号: 2313791
• 负责人: Teresa Head-Gordon
• 金额: $ 70万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313791&HistoricalAwards=false
• 关键词: Reactive; Force; Field; Design; Guided

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.

在化学系化学理论，模型和计算方法课程的支持下，加州伯克利大学的Teresa Head-Gordon和Martin Head-Gordon教授将开发由能量分解分析（EDA）新进展指导的下一代力场。力场（Force fields，FF）是一种经验函数，旨在精确而廉价地描述系统的势能如何随原子和分子的位置而变化。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重要的挑战，可以部分使用EDA来解决。EDA对分子组进行高级量子力学计算，并将它们的相互作用能提取为捕获排斥和吸引物理驱动力的项的总和。这些术语提供了宝贵的第一原理数据，为新力场的设计和参数化提供信息。这项研究应产生新的科学方法和具体化学系统的结果。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。研究机会将提供给那些在短期内难以找到研究实验室的大三转学生。在分子科学和软件工程专业硕士学位的发展将有助于开发一个多样化的劳动力，是高度准备编程，数据建模和机器学习。具体来说，在加州大学伯克利分校的合作研究团队将制定一个新的吉布斯分解分析（GDA），以解开分子驱动力和熵的权衡之间的联系。GDA将适用于FF模拟，以探测控制界面化学的分子相互作用。对于非共价相互作用，EDA的静电极化将被推进，以揭示每个片段的能量降低和轨道重排。将进行新的分析，以调和电荷转移的真实的空间和希尔伯特空间措施，并了解泡利松弛。这些EDA进展有可能回答有关非键合相互作用的基本问题，并帮助指导FF开发，使用电荷平衡来定义极化和电荷转移。最近出现的机器学习来表示潜在的表面，为化学键合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可集成到力场中的EDA数据进行训练。总之，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合相互作用，反应化学，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值进行评估来支持和更广泛的影响审查标准。