Title:  Finding the needle in the haystack: Materials discovery through high-throughput ab initio computing and data mining

Abstract:

New materials enable technological breakthroughs in many fields from energy to electronics. The discovery and development of novel materials with unique properties is a long and difficult process. In the last few decades, computational techniques based on solving the fundamental laws of quantum physics at the atomistic level, so-called ab initio techniques, have risen as a powerful way to compute materials properties. This approach has offered unprecedented insight into materials behavior but also a powerful tool to predict materials properties. When coupled with the exponential increase in computational power, this predictive power provides an opportunity for large-scale, high-throughput, computational searches for new materials. We can nowadays screen thousands of materials by their computed properties even before the experiments. This computational paradigm allows experimentalists to focus on the most promising candidates, and enable researchers to efficiently and rapidly explores new chemical spaces.


In this talk, I will present some of the challenges and opportunities in high-throughput computational materials discovery using examples in various fields from transparent conducting materials to batteries. I will especially highlight computational predictions which have been followed by experimental synthesis and characterization. In addition to allowing the ability to navigate through a large volume of materials data to identify promising compounds, high-throughput computing also offers unprecedented data mining opportunities to detect new relationships between chemistry, structures, and properties. This will be as well illustrated along the talk.

The impact of high-throughput computing is multiplied when the generated data is shared with free and easy access. I will finish my talk by presenting the Materials Project (http://www.materialproject.org), a collaborative project which precisely targets such a data dissemination.

Bio: 

Professor Geoffroy Hautier is an Assistant Professor at the Université catholique de Louvain (Belgium). He received Master degrees in Materials Science and Engineering from the Université Libre de Bruxelles (Belgium) and from the Ecole Centrale Paris (France). Geoffroy Hautier obtained his PhD from the Massachusetts Institute of Technology (USA) in 2011 followed by a Marie Curie postdoctoral fellowship focusing on the computational search of new transparent conducting oxides. Since 2014, Professor Hautier leads a group working on computational materials design and high-throughput screening in different areas from electronics to energy (http://perso.uclouvain.be/geoffroy.hautier/). Professor Hautier is one of the early developer and co-PI of the Materials Project (http://www.materialsproject.org), a freely accessible high-throughput computational database. He authored more than 70 peer-reviewed papers which have been cited more than 8000 times.