Gilles Frapper was born in Saint-Cado, south Bretagne. He studied chemistry at Rennes U. (PhD in Applied Quantum Chemistry 1996). Since 1997, he is at Poitiers University, having previously held research positions at NRC Ottawa and Georgetown U. in Washington D.C. He taught introductory chemistry, and he (still) enjoys teaching theoretical chemistry and material sciences. His primary research focus is to comprehend the atomic arrangements in molecular and solid-state compounds in conjunction with their properties. He specializes in the field of Computational Materials Discovery, predicting bidimensional systems and bulk materials under pressure. Nowadays, he combines evolutionary (genetic) algorithms, machine-learning interatomic potentials and quantum mechanics calculations to design new materials with specific properties and applications.

When Darwin meets Mendeleev: predicting materials from evolutionary algorithms and first-principles calculations.

Using numerical simulation to determine the crystal structure of a compound, based on the sole knowledge of its chemical composition, is a major challenge in materials science. The task is far from trivial: it involves identifying the lowest-energy structural arrangement from among millions of possible structures. To illustrate this challenge, the arrangement of twenty atoms in a box - a repeating lattice of variable shape and volume - can a priori generate more than 1021 possible structures that lie on the potential energy surface (PES). If it took 1 hour of computing time to numerically determine the energy associated with each optimized structure, the computing time required would exceed the age of the universe...   The problem is therefore: how to access the lowest energy well (global minimum on the PES) while monopolizing a minimum of computational resources? 

This talk will discuss a self-learning method for exploring the PES of a crystalline compound, an evolutionary (genetic) algorithm combined with DFT calculations. I will briefly outline the conceptual foundations of this CSP algorithm, which is based on the concepts of the Darwinian evolutionary theory. I will then illustrate its use by presenting some recent results from work carried out in my Applied Quantum Chemistry group: the exploration of the Xenon-Nitrogen binary phase diagram under pressure (0-100 GPa), and the prediction of two new nitrogen allotropes, with 2D crown-like and 3D chlathrate-like networks.