Postdoctoral Researcher - Phase Diagrams with Bayesian Inference

About the Role

We have recently demonstrated that AIMD simulations can be accelerated by a factor of 100 using an interatomic potential adjusted by machine learning (MLIP) [1-3]. This accelerated sampling of the equilibrium canonical distribution can be followed by free energy calculations via thermodynamic integration [4]. This 'on-the-fly' learning strategy ensures quasi-equivalent accuracy to AIMD calculations and is now available in the Python code MLACS, which we develop and use in production. Nevertheless, repeating such simulations tens or even hundreds of times to systematically sample a phase diagram remains extremely computationally demanding, or even unthinkable.

During this postdoctoral project, the candidate will develop an optimal and automated sampling strategy. The objective is twofold: to reduce the number of MLACS simulations required to construct a phase diagram, and to provide an estimate of the uncertainty associated with the obtained diagram. They will build upon recent developments in the literature [5, 6].

Key Responsibilities

• Develop an optimal and automated sampling strategy for phase diagram construction.
• Reduce the computational cost of phase diagram simulations.
• Provide uncertainty estimates for the generated phase diagrams.
• Collaborate with researchers on cutting-edge machine learning potentials for materials science.

Candidate Profile

• Ph.D. in a relevant scientific field (e.g., Physics, Materials Science, Chemistry, Computer Science).
• Strong background in computational materials science, statistical mechanics, and/or machine learning.
• Experience with programming, particularly in Python.
• Familiarity with ab initio methods and/or machine learning interatomic potentials is a plus.
• Excellent problem-solving skills and ability to work independently and collaboratively.

About CEA

CEA is committed to the integration of people with disabilities and offers accommodations for inclusion. A national security background check is conducted for all employees.

Key skills/competency

• Postdoctoral Researcher
• Bayesian Inference
• Phase Diagrams
• Machine Learning Potentials (MLIP)
• AIMD Simulations
• Thermodynamic Integration
• Python
• Computational Materials Science
• Statistical Mechanics
• Automated Sampling