Transformative Applications of Machine Learning for Chemical Reactions
arXiv:2101.03530
Abstract
Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. 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, both, in research and academic teaching.
References in corpus (21)
- Adam: A Method for Stochastic Optimization
- On representing chemical environments
- SchNet - a deep learning architecture for molecules and materials
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- A KInetic Database for Astrochemistry (KIDA)
- Neural Network Based in Silico Simulation of Combustion Reactions
- Duo: a general program for calculating spectra of diatomic molecules
- How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase
- Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
- Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality
- Learning-based Quantum Robust Control: Algorithm, Applications and Experiments
- Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase
- Interactive Chemical Reactivity Exploration
- Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
- Machine Learning Quantum Reaction Rate Constants
- ML Models of Vibrating HCO: Comparing Reproducing Kernels, FCHL and PhysNet
- Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer
- Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics
- Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
- Bayesian optimization for inverse problems in time-dependent quantum dynamics