MaLeR · Machine Learning applied to Reactivity: combination of HDNNs with ReaxFF

Machine Learning applied to Reactivity: combination of HDNNs with ReaxFF

Machine learning (ML) methods have become increasingly important in business and science for making predictions based on the substantial amount of data that many organizations collect or generate. ML methods are extremely powerful, since computers are much better suited than humans to identify correlations. In computational chemistry and materials science, a typical scientific goal is to predict properties of different molecules and compounds. Computer simulations generate new, physically meaningful data, based on some predefined rule set. In lieu of an actual experiment, which can be costly and dangerous for both people and the environment, simulations are used to answer the scientist’s questions. The main scientific goal of this project was to explore and apply ML techniques to predict chemical reactivity of molecules and compounds. The rate of chemical reactions can be obtained through computer simulations, if the potential energy surface (the PES) is known. For each possible placement of some collection of atoms in 3-dimensional space, a single number, namely the potential energy, determines how likely that placement of atoms is at any given thermodynamic condition (temperature and pressure). Predicting the potential energy surface for different molecules can be done through quantum-chemical calculations, for example based on density functional theory (DFT) or coupled cluster (CC). However, such calculations are computationally demanding, and it is not feasible to perform such calculations for very large (i.e., realistic) systems. The main scientific goal of this project was to provide a much faster way of evaluating the potential energy during computer simulations, parameterizing force fields and ML methods to predict the potential energy.

Data: CORDIS, © European Union