Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization
Our paper on evaluting machine learning methods for potential energy curves and geometry optimzations is now published in J. Phys. Chem. A. Our aim was to determine how well state of the art machine learning methods perform at tasks crucial for geometry optimization. All of the raw data and scripts used for this work are available on GitHub.
