Claudio Zeni from the Scuola Internazionale Superiore di Studi Avanzati is the scheduled speaker at an upcomoing virtual Department of Physics and Astronomy colloquium. His talk is titled, "Machine Learning for Building Efficient and Interpretable Force Fields."
The recent years have seen a surge in the development of machine learning algorithms in different areas of scientific research. In the field of simulation of condensed matter, the development of machine learning force fields to carry out molecular dynamics simulations is a topic that has attracted a lot of interest ever since the pioneering work of Behler and Parrinello in 2007.
Machine learning force fields are trained using reference data coming from expensive ab initio simulations, and try to approximate these accurate methods in a computationally more efficient way. Many methods have been developed and showcase good force and global energy prediction accuracies with respect to the data used to train them, usually obtained from ab initio calculations, e.g. density functional theory (DFT) calculations.
After a brief introduction regarding the differences between artificial neural networks and Bayesian methods such as Gaussian process regression, we present the case for the use of Gaussian process regression to construct machine learning force fields that retain the interpretability of classical parametrized force fields, but are inherently non-parametric and can, therefore, be automatically trained from reference data.
We design algorithms to construct force fields that have an explicit dependence on atomic pair distances (2- body), angles (3-body) and atomic embedding (many-body). Then, by exploiting this explicit dependencies, we are able to ``map” the Gaussian process force fields into nonparametric classical force fields, thus increasing very substantially the computational speed in prediction.
These ``mapped” force fields have been recently employed for MD simulations of Ni19 nanoparticles, and for a variety of bulk materials , and ongoing research aims to increase their accuracy in challenging systems such as Au nanoparticles and metallic surfaces for catalytic reactions.