Summary
ab initio simulation techniques have evolved to the point that we can reliably predict many properties of materials before they have been synthesized. This paradigmatic change has led to databases that contain millions of theoretically predicted materials with desirable attributes. However, all this information is of little use if we cannot predict if these novel materials can be made at all. I will develop a framework based on first-principles computer simulations to predict if and how a material can be made. The proposed approach will boost the success rate of “materials by design”, expedite experiments to create novel materials, 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.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101115332 |
| Start date: | 01-04-2024 |
| End date: | 31-03-2029 |
| Total budget - Public funding: | 1 496 991,00 Euro - 1 496 991,00 Euro |
Cordis data
Original description
ab initio simulation techniques have evolved to the point that we can reliably predict many properties of materials before they have been synthesized. This paradigmatic change has led to databases that contain millions of theoretically predicted materials with desirable attributes. However, all this information is of little use if we cannot predict if these novel materials can be made at all. I will develop a framework based on first-principles computer simulations to predict if and how a material can be made. The proposed approach will boost the success rate of “materials by design”, expedite experiments to create novel materials, 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.Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
Images
No images available.
Geographical location(s)
