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

在化学系化学理论、模型和计算方法计划的支持下，加州大学欧文分校的Dmitrij Rapoport博士的目标是使新化学反应的计算发现和已知反应的优化更快、更便宜。虽然对高效、经济的化合物制造方法-药物、有机发光二极管(OLED)材料等-的需求只会不断增加，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路径的数量随着原子数量的增加而指数级增加，这使得即使在最强大的计算机上也不可能测试所有的路径。为了解决这种“维度诅咒”，拉波波特将开发出在大量可能性中识别低维结构的方法，从计算模型中去除与化学反应无关的信息。利用机器学习降维的方法将海量数据集提取为紧凑的表示形式，这项研究将使绿色化学和无重金属催化过程的复杂化学反应的建模和发现成为可能。这项工作的重点是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理解能力。在该CTMC奖下，Dmitrij Rapoport将开发使用非线性降维和离散化技术从化学反应的势能面构造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，并明确地解决势能面的高维问题。非线性降维技术分离了反应和非反应自由度，从而创建了化学反应机理的计算模型，该模型在计算上高效地采样，并根据成键的变化来定义反应机理之间的相似性。Rapoport将使用这些低维计算模型来对化学反应进行基于机器学习的预测，从而避免在整个高维势能表面上操作的方法的根本限制。这一奖项反映了NSF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 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.