# Advances of Machine Learning in Molecular Modeling and Simulation

## TL;DR

This review discusses recent advances in applying machine learning to molecular modeling and simulation, highlighting how it complements traditional methods and outlining future challenges for mainstream adoption.

## Contribution

It provides a comprehensive overview of recent developments, integrating machine learning with traditional approaches, and discusses future research directions in chemical engineering.

## Key findings

- Machine learning enhances molecular modeling accuracy.
- Integration of ML with physics-based methods improves simulations.
- Identifies key challenges for ML adoption in chemistry.

## Abstract

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool.