PAC Generalization via Invariant Representations
Advait Parulekar, Karthikeyan Shanmugam, et al.
ICML 2023
Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.
Advait Parulekar, Karthikeyan Shanmugam, et al.
ICML 2023
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
Pin-Yu Chen, Cho-Jui Hsieh, et al.
KDD 2022
Yunfei Teng, Anna Choromanska, et al.
ECML PKDD 2022