Abstract: First principles or ab initio approaches have been revolutionizing materials science and condensed matter physics as they offer powerful ways to solve the fundamental laws of physics at the atomistic level. Essential materials properties (e.g., elastic constants, optical absorption, magnetization, …) can now be assessed through these first principles methods. Traditional materials science relies a lot on trial and error and finding the best material for an application is a long and cumbersome process, a true needle in the haystack problem. The predictive power of ab initio techniques in assessing materials properties provides an opportunity for large-scale, accelerated, computational searches for new materials. We can now screen thousands of materials by their computed properties even before any experiment has been performed. 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 the challenges and opportunities in materials discovery in high-throughput ab initio computing using examples from several fields we have studied lately from solar cell materials to ferroelectrics and materials for quantum computing. I will 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, we will show that high-throughput computing also offers unprecedented machine learning opportunities to detect new relationships between chemistry, structures, and properties.
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.materialsproject.org), a collaborative project which precisely targets such a data dissemination.