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

在化学系化学理论、模型和计算方法项目的支持下，加州大学欧文分校的Dmitrij Rappoport博士的目标是使新的化学反应的计算发现和已知反应的优化更快、更便宜。尽管对高效、经济的化学合成方法的需求——药物、有机发光二极管（OLED）材料等——只在增加，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呈指数增长，这使得即使使用最强大的计算机也无法测试所有途径。为了解决这个“维度的诅咒”，Rappoport将开发方法来识别大量可能性中的低维度结构，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炼成紧凑的表示，本研究将使绿色化学和无重金属催化过程的复杂化学反应建模和发现成为可能。通过强调数据科学和机器学习技术来探索化学反应，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。根据该CTMC合同，Dmitrij Rappoport将开发利用非线性降维和离散化技术从化学反应的势能面构建低维坐标子流形的方法。这套新的计算工具旨在补充现有的半局部过渡态搜索方法，并明确地解决高维势能面问题。非线性降维技术分离了反应性和非反应性自由度，从而创建了化学反应机制的计算模型，这些模型计算效率很高，可以根据键的变化来定义反应机制之间的相似性。Rappoport将使用这些低维计算模型来进行基于机器学习的化学反应预测，从而避免了在全高维势能表面上操作的方法的基本限制。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。

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.