| Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks K Yang, Y Cao, Y Zhang, S Fan, M Tang, D Aberg, B Sadigh, F Zhou Patterns 2 (5), 2021 | 78 | 2021 |
| High sulfur content multifunctional conducting polymer composite electrodes for stable Li-S battery AB Puthirath, A Baburaj, K Kato, D Salpekar, N Chakingal, Y Cao, G Babu, ... Electrochimica Acta 306, 489-497, 2019 | 53 | 2019 |
| Machine-learning potentials for crystal defects R Freitas, Y Cao MRS Communications 12 (5), 510-520, 2022 | 36 | 2022 |
| Quantifying chemical short-range order in metallic alloys K Sheriff, Y Cao, T Smidt, R Freitas Proceedings of the National Academy of Sciences 121 (25), e2322962121, 2024 | 17 | 2024 |
| Capturing short-range order in high-entropy alloys with machine learning potentials Y Cao, K Sheriff, R Freitas arXiv preprint arXiv:2401.06622, 2024 | 10 | 2024 |
| Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks K Sheriff, Y Cao, R Freitas npj Computational Materials 10 (1), 215, 2024 | 6 | 2024 |
| Nonequilibrium chemical short-range order in metallic alloys M Islam, K Sheriff, Y Cao, R Freitas arXiv preprint arXiv:2409.15474, 2024 | 2 | 2024 |
| Comprehensive analysis of ordering in CoCrNi and CrNi2 alloys VP Bacurau, PAFP Moreira, G Bertoli, AF Andreoli, E Mazzer, FF de Assis, ... Nature Communications 15 (1), 7815, 2024 | 2 | 2024 |
| Roadmap for the development of machine learning-based interatomic potentials YW Zhang, V Sorkin, ZH Aitken, A Politano, J Behler, AP Thompson, ... Modelling and Simulation in Materials Science and Engineering 33 (2), 023301, 2025 | 1 | 2025 |
| Section 12–Capturing chemical complexity in high-entropy materials K Sheriff, Y Cao, R Freitas Roadmap for the development of machine learning-based interatomic potentials, 0 | | |
Rodrigo FreitasAssistant Professor of Materials Science and Engineering, MITCorreu electrònic verificat a mit.edu
