Finding the perfect imperfection: Accelerating the search of quantum defects using high-throughput computing

Abstract : Color centers in semiconductors are emerging as potential qubits for quantum networking and sensing. Well-established “quantum defects” such as the NV center in diamond have demonstrated a range of quantum applications but have all been found serendipitously. Ultimately, the performance of the quantum device will depend on the intrinsic properties of the defect it is built with. Finding new quantum defects with enhanced performances would be strongly beneficial to the field of quantum information science. New materials have been more and more identified and discovered through first principles computations sometimes ran at a large scale in a high-throughput fashion. Here, I will show how high-throughput computing approaches can be extended to defects and used to discover quantum defects in two important hosts: silicon and WS2. By building computed database containing thousands of defects candidates, we are able to identify the most promising ones and guide experiments towards their realization. I will highlight some successes in identifying new defects that are confirmed experimentally in both hosts (CoS in WS2 and “T center-like” defects in silicon). Our data set is also useful to identify trends, opportunities and challenges using these hosts for quantum applications. I will finish my talk by showing how our defect screening approach can be applied to more traditional opto-electronic applications such as materials for thin-film photovoltaics. Looking forward, I will outline a vision where defect in materials and their related properties will catch with our knowledge on bulk properties paving the way to building a “defect genome”. 

Bio : Geoffroy Hautier is the Hogdson Family Professor of Engineering at Dartmouth. Professor Hautier graduated with a PhD in Materials Science and Engineering from MIT in 2011 followed by a postdoctoral and faculty position at UCLouvain (Belgium). He moved to Dartmouth in October 2020. His research focuses on high-throughput computational materials discovery and design in various fields from transparent conducting materials, to photovoltaics and quantum information science ( http://sites.dartmouth.edu/hautiergroup/ ). Professor Hautier is one of the early developers and co-PI of the Materials Project ( http://www.materialsproject.org ), a freely accessible high-throughput computational database. He is also the co-founder and chief scientific officer of the start-up Matgenix which provides computational and machine learning support in the materials and chemical industry. Professor Hautier authored more than 150 peer-reviewed publications which have been cited 37,000 times with a h-index of 67. He is an associate editor for the journal npj computational materials science.