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

在化学系化学理论、模型和计算方法计划的支持下，夏威夷大学MāNoa的孙锐将致力于开发一种新的机器学习算法来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，有时太快太小，以至于设备无法直接测量，因此计算机模拟通过求解运动方程来跟踪原子的运动，在寻求彻底了解化学反应的性质方面发挥着至关重要的作用。然而，这样的模拟在计算上要求非常高(例如，需要大量计算机长时间运行)，因此严重限制了它们的应用范围。孙锐和他的研究小组正在开发一种新型的机器学习算法，该算法利用在化学反应研究过程中收集的信息，可能会将模拟速度提高一个数量级或更多。这个算法，连同一个专门设计的数据存储和获取系统，将是开源的，并使用最先进的计算化学软件来实现。由这种机器学习算法推动的模拟有可能达到前所未有的效率和准确性，从而突破我们关于化学反应的知识界限，化学反应可能是化学领域的核心要素。通过引入计算作为一种不同的解决问题的方法，孙锐还将开发教育项目，以增强夏威夷大学学生的学习体验。夏威夷大学拥有美国最大的太平洋岛民学生人口。孙锐正在开发一种主动机器学习协议，目的是将化学反应的从头计算分子动力学模拟的效率提高至少一个数量级。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是根据从索引库获取的数据进行训练的，该索引库包含在先前的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。孙锐和他的研究小组还将开发一种最佳的分子描述符，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，表明其重现从头算能量梯度并保持良好运行轨迹的可能性。为了保护模拟的完整性，将使用高风险因素作为拒绝标准，以引用从头计算。由于效率有望大幅提高，所提出的主动机器学习协议有可能将AIMD的能力推到一个前所未有的水平，并为化学反应的动力学模拟设定一个新的标准。孙锐还将开发计算模块，以支持目前的化学实验室，以及夏威夷大学Māno.非计算机科学、技术、工程和数学专业的第一个计算课程。该奖项反映了美国国家科学基金会的法定使命，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 (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.