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Taking On the "Curse of Dimensionality" in Chemical Kinetics: Complex Chemical Reaction Prediction Using Manifold Learning

应对化学动力学中的“维数诅咒”：利用流形学习预测复杂化学反应

基本信息

批准号：
2227112
负责人：
Dmitrij Rappoport
金额：
$ 41.87万
依托单位：
University of California-Irvine
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：
2022-08-15 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227112&HistoricalAwards=false
关键词：
Taking Curse Dimensionality Chemical Kinetics

项目摘要

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Dr. Dmitrij Rappoport of the University of California, Irvine aims to make computational discovery of new chemical reactions and optimization of known reactions faster and cheaper. While the demand for efficient and economical ways of making chemical compounds—pharmaceuticals, organic light-emitting diode (OLED) materials, and many more-is only increasing, systematic search for these synthetic methods remains a challenge. One major obstacle is fundamental: the number of possible pathways that a chemical reaction may take increases exponentially with the number of atoms, making it impossible to test them all even with the most powerful computers. In order to tackle this “curse of dimensionality”, Rappoport will develop methods to recognize low-dimensional structures in the abundance of possibilities, removing information from computational models that is unrelated to chemical reactions. Taking advantage of machine learning methods of dimensionality reduction to distill enormous data sets into compact representations, this research will enable modeling and discovery of complex chemical reactions for green chemistry and heavy metal-free catalytic processes. With its emphasis on data science and machine learning techniques to explore chemical reactions, this work will introduce the next generation of physical scientists to the tools and techniques of data science and helps to improve their data literacy.Under this CTMC award, Dmitrij Rappoport will develop methods for constructing low-dimensional coordinate sub-manifolds from potential energy surfaces of chemical reactions using non-linear dimensionality reduction and discretization techniques. This new set of computational tools is designed to complement the existing semilocal transition state search methods and to explicitly address the problem of high dimensionality of potential energy surfaces. Non-linear dimensionality reduction techniques separate reactive and nonreactive degrees of freedom and thus create computational models of chemical reaction mechanisms that are computationally efficient to sample and lend themselves to definitions of similarity between reaction mechanisms in terms of changes in bonding. These low-dimensional computational models will be used by Rappoport to make machine learning–based predictions of chemical reactivities that avoid the fundamental limitations of methods that operate on the full high-dimensional potential energy surfaces.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.

在加州大学Dmitrij Rappoport博士化学系化学理论、模型和计算方法项目的支持下，Irvine的目标是更快、更便宜地通过计算发现新的化学反应和优化已知反应。虽然对有效和经济的化学化合物（药物，有机发光二极管（OLED）材料等）制造方法的需求只会增加，但对这些合成方法的系统研究仍然是一个挑战。一个主要的障碍是根本性的：化学反应可能采取的途径数量随着原子数量的增加而呈指数级增加，即使用最强大的计算机也不可能测试所有这些途径。为了解决这种“维数灾难”，Rappoport将开发方法来识别可能性丰富的低维结构，从计算模型中删除与化学反应无关的信息。利用降维的机器学习方法将大量数据集提取为紧凑的表示，这项研究将使复杂的化学反应的建模和发现绿色化学和无重金属催化过程。凭借其对数据科学和机器学习技术的重视，以探索化学反应，这项工作将向下一代物理科学家介绍数据科学的工具和技术，并有助于提高他们的数据素养。Dmitrij Rappoport将开发从化学反应的势能表面构建低维坐标子流形的方法，线性降维和离散化技术。这套新的计算工具的目的是补充现有的半局部过渡态搜索方法，并明确地解决问题的高维度的势能面。非线性降维技术将反应性和非反应性自由度分开，从而创建化学反应机制的计算模型，该计算模型在计算上有效地进行采样，并使其本身适合于在键合变化方面的反应机制之间的相似性的定义。Rappoport将利用这些低维计算模型，对化学反应性进行基于机器学习的预测，从而避免在全高维势能面上操作的方法的根本局限性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。