Advancing Materials Science through Machine Learning

The candidate will develop and implement cutting-edge methods utilizing the open-souce MiLaDy (Machine Learning Dynamics) package. Key responsibilities will include the integration and expansion of MiLaDy by connecting it with various packages and platforms from materials science community to enhance the functionality and performance of the code. Here are a few examples of previous work that will be improved upon:
(i) Combining ML with accelerated Molecular Dynamics based on the Bayesian adaptive biasing force method to sample the complex energy landscapes of defects.
(ii) Providing reliable force fields capable of handling radiation-induced defects in materials
(iii) Exploring the atomistic free energy landscape of metals with ab initio accuracy up to the melting temperature.
(iv) Proposing surrogate models to bypass traditional approaches for accessing challenging properties, such as vibrational entropies.
Great attention will be devoted to the database design to ensure robust performance of ML approaches for materials science applications.