AI-driven discovery of new materials: breaking data bottlenecks and transforming research paradigms

2026/04/01 by Pengfei Huang, Huang Pengfei, Yanan Chen
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Field (mathematics) #Focus (optics) #Identification (biology) #Key (lock) #Machine Learning in Materials Science #Scientific Computing and Data Management #Scope (computer science)

paper · doi:10.1360/csb-2026-0182

published in Chinese Science Bulletin (Chinese Version) (Science China Press)

openalex publication_date 2026/04/01 · crossref issued 2026/04/17 · crossref published 2026/04/17 · crossref published-online 2026/04/17 · openalex created_date 2026/04/17 · crossref created 2026/04/17 · openalex updated_date 2026/06/27 · crossref published-print 2026/08/01 · crossref deposited 2026/08/05 · crossref indexed 2026/08/05

Citations

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• The FAIR Guiding Principles for scientific data management and stewardship
• SchNet – A deep learning architecture for molecules and materials
• A mobile robotic chemist
• An autonomous laboratory for the accelerated synthesis of inorganic materials
• Scaling deep learning for materials discovery
• Accelerated data-driven materials science with the Materials Project