Machine Learning for Chemical Reactions

Abstract

Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.

Transfer learned potential energy surfaces: accurate anharmonic vibrational dynamics and dissociation energies for the formic acid monomer and dimer

“…Generating full-dimensional, reactive PESs even for small molecules is a challenging task. 12,13 This often requires datasets consisting of tens of thousands of ab initio calculations to adequately describe configurational space of the system of interest. While calculations at the density functional theory (DFT) or Møller–Plesset perturbation (MP2) levels of theory are cost-efficient, reaction barriers are less accurate.…”

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

“…Deep learning, which is a subset of ML, has been used in chemical reaction prediction [109] and to predict reaction yields [110] using interfaces like IBM RXN for Chemistry [111,112], which can be further modified to predict enzymatic reactions [113], and being open-source, these approaches can be readily modified to meet user requirements. Meuwly [114] reviewed the utility of ML methods for chemical reactions. To date, we are not aware of careful comprehensive comparisons of these methods which would suggest one approach is better than another, merely that applying such approaches culls CRNR outputs.…”

Towards Predictive Synthesis of Inorganic Materials Using Network Science

“…In this regard neural networks have shown promise in navigating the huge network space in organic molecular systems. Recently, a three layered neural network has been able to uncover retrosynthetic routes through the use of Monte Carlo tree search algorithms (Segler et al, 2018) based on reactions found in the Reaxys database, and we refer the reader to a recent review on machine-learning methods for more information (Meuwly, 2021). Compared to molecular synthesis, inorganic synthesis prediction is more complex, given the sheer number of elements, metastability and the possibility of new unchartered materials.…”