Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena

TL;DR

This paper reviews how Generative AI techniques are advancing computational chemistry, highlighting their applications, challenges, and the need to integrate chemical principles for predicting emergent phenomena.

Contribution

It provides a structured overview of Generative AI methods in chemistry and emphasizes the importance of integrating chemical principles to improve predictive capabilities.

Findings

Generative AI has advanced molecular sampling and force field development.

Current methods face challenges in predicting emergent phenomena.

Future models should incorporate core chemical principles.

Abstract

The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics.