SOURCE-LINKED INTELLIGENCE

ab initio PRediction Of MaterIal SynthEsis

CORDIS · observation · Publication date unknown

, and thus greatly enhance the speed of materials discovery. To achieve this goal, computational methods that combine crystal structure prediction, advanced statistical sampling, and state-ofthe- art machine learning techniques will be designed. The whole framework will be benchmarked on model systems with known properties. The resulting software will be made generic and open-source. The computational framework will be used to gain mechanistic insight into the physical processes that control the formation of specific functional materials including high-pressure phases of matter, perovskites, and molecular crystals. Computational materials science, ab initio methods, statistical mechanics, machine learning for chemistry and materials, atomistic simulations

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recordType

award

status

TERMINATED

region

EU

value

1496991

unit

EUR