CAREER: Minimize ab initio Tasks in Dynamics Simulations of Chemical Reactions with Active Machine Learning

职业：通过主动机器学习最小化化学反应动力学模拟中的从头开始任务

• 批准号: 2144031
• 负责人: Rui Sun
• 金额: $ 46.53万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144031&HistoricalAwards=false
• 关键词: CAREER; Minimize; ab; initio; Tasks

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Rui Sun of University of Hawaii, Mānoa will work to develop a novel machine learning algorithm to accelerate simulations of chemical reactions. Since a chemical reaction can take place at a very fast rate and on a very small scale, sometimes too fast and small for equipment to directly measure, computer simulations, which follow the motions of atoms by solving equations of motion, play an essential role in seeking a thorough understanding of the nature of a chemical reaction. However, such simulations are computationally very demanding (e.g., require a large number of computers to run for a long period of time), thus severely limiting the scope of their applications. Rui Sun and his research group are developing a novel machine learning algorithm that utilizes the information gathered along the study of the chemical reaction to speed up simulations potentially by an order of magnitude or more. This algorithm, along with a specifically designed data storage and fetch system, will be open-source and implemented with state-of-the-art computational chemistry software. Simulations boosted by this machine learning algorithm have the potential to achieve unprecedented efficiency and accuracy, and thus to push the boundary of our knowledge on chemical reactions, perhaps the central element of the field of chemistry. By introducing computation as a different means for problem solving, Rui Sun will also develop educational programs to enhance the learning experience of students at the University of Hawaii, which hosts the largest population of Pacific islander students in America.Rui Sun is developing an active machine learning protocol with the aim of increasing the efficiency of ab initio molecular dynamics simulations of chemical reactions by at least one order of magnitude. This is to be achieved by replacing 90+% of the ab initio energy gradient calculations with a specifically designed machine learning algorithm, interpolating moving ridge regression (IMRR). IMRR is trained on data fetched from an indexed library containing all the ab initio energy gradients calculated in the previous simulations and updated on the fly as each trajectory progresses. Rui Sun and his research group will also develop an optimal molecular descriptor to efficiently identify ab initio training data that yields the smallest error in IMRR-predicted energy gradients. Each IMRR will provide a risk factor, indicating its probability of reproducing the ab initio energy gradient and maintaining a well-behaved trajectory. A high-risk factor will be used as a rejection criterion to refer back to ab initio calculation in order to protect the integrity of the simulation. Due to the expected dramatic boost in efficiency, the proposed active machine learning protocol has the potential to push the capability of AIMD to an unprecedented level and to set a new standard for dynamics simulation of chemical reactions. Rui Sun will also develop computation modules to support current chemistry labs as well as the very first computational course for non-CS (Computer Science) STEM (Science, Technology, Engineering and Mathematics) majors at the University of Hawaii at Mānoa.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（CTMC）计划和刺激竞争性研究的既定计划（EPSCoR）的支持下，夏威夷大学的Rui Sun，Mānoa将致力于开发一种新的机器学习算法，以加速化学反应的模拟。由于化学反应可以以非常快的速度和非常小的规模发生，有时太快和太小以至于设备无法直接测量，因此通过求解运动方程来跟踪原子运动的计算机模拟在寻求彻底理解化学反应的本质方面发挥着至关重要的作用。然而，这样的模拟在计算上非常苛刻（例如，需要大量计算机长时间运行），从而严重限制了其应用范围。Rui Sun和他的研究小组正在开发一种新的机器学习算法，该算法利用沿着化学反应研究收集的信息，将模拟速度提高一个数量级或更多。这种算法，沿着一个专门设计的数据存储和提取系统，将是开源的，并与最先进的计算化学软件实现。由这种机器学习算法推动的模拟有可能实现前所未有的效率和准确性，从而推动我们对化学反应的知识边界，这可能是化学领域的核心要素。通过引入计算作为解决问题的不同手段，孙瑞还将开发教育项目，以提高夏威夷大学学生的学习体验，Rui Sun正在开发一种主动机器学习协议，旨在将化学反应的从头算分子动力学模拟的效率提高至少一个数量级。大小这是通过用专门设计的机器学习算法，内插移动岭回归（IMRR）取代90%以上的从头算能量梯度计算来实现的。IMRR是在从索引库中获取的数据上训练的，该索引库包含在先前模拟中计算的所有从头算能量梯度，并随着每个轨迹的进展而动态更新。Rui Sun和他的研究小组还将开发一种最佳分子描述符，以有效地识别从头计算训练数据，从而在IMRR预测的能量梯度中产生最小的误差。每个IMRR将提供一个风险因子，表明其再现从头算能量梯度和保持良好轨迹的概率。将使用高风险因子作为拒绝标准，以参考从头计算，以保护模拟的完整性。由于预期的效率大幅提升，拟议的主动机器学习协议有可能将AIMD的能力推向前所未有的水平，并为化学反应的动力学模拟设定新的标准。孙瑞还将开发计算模块，以支持现有的化学实验室，以及夏威夷的第一个非计算机科学STEM（科学、技术、工程和数学）专业的计算课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。