SOURCE-LINKED INTELLIGENCE

Defect-Tolerant Materials for Energy

CORDIS · observation · Publication date unknown

be achieved by developing and applying a novel method to capture the impact of defects in semiconductors rapidly and accurately. Based on proof-of-principle work where I demonstrated state-of-the-art machine learning approaches can accelerate and circumvent the use of costly computational defect calculations, MATERIALISE, will enable a 4-5 order-of-magnitude increase in the number of materials that can be accurately screened for energy applications. Crucially, I will identify defect design principles that can yield optimisations across the semiconductor sector more broadly. MATERIALISE will adopt an integrated approach to materials discovery, extending the boundaries of what is possible in computational design through electronic-structure calculations of bulk, surface, interfaces, transport, and synthesisability. My international network of collaborators will validate and build devices for the most promising candidates. This new approach to designing defects in materials will establish my group at the forefront of computational materials science. defects, doping, machine learning, d

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recordType

award

status

SIGNED

region

EU

value

1499995

unit

EUR