Buonassisi, Tonio

Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Oviedo, F., Ferres, J. L., Buonassisi, T., & Butler, K. T. (2022) · Accounts of Materials Research

Machine Learning in Materials ScienceExplainable Artificial Intelligence (XAI)

Cited by 435

Terawatt-scale photovoltaics: Trajectories and challenges

Haegel, N. M., Margolis, R., Buonassisi, T., Feldman, D., Froitzheim, A., Garabedian, R., Green, M., Glunz, S., Henning, H.-M., Holder, B., et al. (2017) · Science

Photovoltaic Systems and Sustainabilitysolar cell performance optimization

Cited by 389

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., Ramasamy, S., DeCost, B. L., Tian, S. I. P., Romano, G., et al. (2019) · npj Computational Materials

Machine Learning in Materials ScienceX-ray Diffraction in Crystallography

Cited by 327

How machine learning can help select capping layers to suppress perovskite degradation

Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J. J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., et al. (2020) · Nature Communications

Perovskite Materials and ApplicationsMachine Learning in Materials Science

Cited by 189

Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

Zhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023) · arXiv

Artificial Intelligence in EducationTopic Modeling

Cited by 19

Principled Exploration of Bipyridine and Terpyridine Additives to Promote Methylammonium Lead Iodide Perovskite Crystallization

Hartono, N. T. P., Ani Najeeb, M., Li, Z., Nega, P. W., Fleming, C. A., Sun, X., Chan, E. M., Abate, A., Norquist, A. J., Schrier, J., et al. (2022) · Crystal Growth & Design

Perovskite Materials and ApplicationsMachine Learning in Materials Science

Cited by 13