The Basque Center for Applied Mathematics (BCAM) in Bilbao, Spain, is a Severo Ochoa center of excellence operating at the frontier of Mathematics, science and engineering to tackle mathematical and numerical problems in a variety of fields, including Particle Physics, Human Metabolism, Materials Science and Big Data. In this regard, BCAM is uniquely multidisciplinary and allows people with different backgrounds to exchange ideas on a daily basis. The Modelling and Simulation in Life and Materials Sciences (MSLMS) group of Ikerbasque Prof. Elena Akhmatskaya (http://www.bcamath.org/en/people/eakhmatskaya) at BCAM is coordinating an ambitious multi-disciplinary project on multi-scale modelling of advanced energy materials, bringing together Mathematicians, Computational Chemists and Physicists, Materials Scientists, Chemical Engineers and Experimentalists.

We are looking for an enthusiastic student with an M.Sc. in computational chemistry, physics, Applied Mathematics and related fields, to work with towards his/her PhD degree on the atomistic and coarse-grained modelling of heterogeneous battery materials. The student will apply through a competitive LaCaixa grant 2021, and thus an excellent academic curriculum is an absolute necessity.

Project details

Objective: Developing a framework for the Bayesian optimization of multiphase battery materials, accurately modelled at the atomistic and coarse-grained scale through machine learning methods and enhanced sampling methods.

Description: Heterogeneous battery materials (e.g. combining polymers and ceramics) are becoming increasingly important in the race towards efficient and cost-effective all-solid-state batteries. The interaction between phases strongly affects the material’s properties, and tuning their composition at the interface is a compelling strategy to enhance performance. This project aims at the development and implementation of a probabilistic Bayesian framework for the chemical optimization of heterogeneous battery materials. To achieve this goal, the student will develop accurate machine learning-based force fields for the atomistic and coarse-grained simulation of the evaluated systems, extend our Mix & Match Hamiltonian Monte Carlo (MMHMC) importance sampler and deploy an interface between our in-house software HaiCS for computational statistics (where MMHMC is implemented) and the massively parallelized molecular dynamics software LAMMPS. In addition, the student will adapt and implement our Generalised hybrid Monte Carlo method for particle dynamics simulations (both at the atomistic and meso-scale) and adaptive integrators in LAMMPS. 

How to Apply

Specific conditions and instructions on How to Apply, for interested students who have not resided in Spain for more than 12 months can be found here: https://fundacionlacaixa.org/en/la-caixa-foundation-doctoral-inphinit-fellowships-incoming

For those who have resided or have carried out their main activity (work, studies, etc.) in Spain for more than 12 months, conditions and instructions on How to Apply are found here:

https://fundacionlacaixa.org/en/la-caixa-foundation-doctoral-inphinit-fellowships-retaining

When you are prompted for Host Institution select: Bcam - Basque Center for Applied Mathematics. PhD-Position: Mathematical Modelling. Position Description: Employing Bayesian Statistics and Machine Learning for Modelling and Optimization of Heterogeneous Battery Materials.

Please apply directly through those links. For further inquires, please write to Elena Akhmatskaya [email protected] or Mauricio Rincon Bonilla [email protected].