Computational synthesis of a new generation of 2D-based perovskite quantum materials
Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.
- Research Organization:
- Lehigh Univ., Bethlehem, PA (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- SC0024099
- OSTI ID:
- 2564475
- Journal Information:
- APL Machine Learning, Journal Name: APL Machine Learning Journal Issue: 2 Vol. 2; ISSN 2770-9019
- Publisher:
- American Institute of PhysicsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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