Machine Learning for Chemical Reactions.

Area of Science:

• Computational Chemistry
• Chemical Physics
• Materials Science

Background:

• Machine learning (ML) has a significant history in the application to chemical reactions.
• ML techniques offer novel solutions for complex problems in chemical dynamics and reaction planning.

Purpose of the Study:

• To review the diverse applications of ML in chemical reactions, from small molecule dynamics to large-scale reaction networks.
• To highlight the potential of ML to address computationally intractable problems and integrate experimental data.
• To provide an outlook on the future challenges and opportunities in this interdisciplinary field.

Main Methods:

• Bayesian inference for developing models consistent with experimental data.
• ML-based methods for characterizing state-to-state information in reactive collisions.
• Machine-learned neural network potentials for simulating reactive networks, such as in combustion.

Main Results:

• Demonstration of ML's capability in reaction dynamics, computational reaction planning, and handling complex chemical systems.
• Successful application of Bayesian inference and ML for intractable problems, including detailed state-to-state analysis.
• Feasible simulation of reactive networks using machine-learned potentials, advancing areas like combustion chemistry.

Conclusions:

• ML is transforming the approach to chemical reaction problems in both research and academic teaching.
• The integration of ML with computational and experimental chemistry offers significant potential for future discoveries.
• Continued development in ML techniques promises to further revolutionize the field of chemistry.