AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-ai Tsao, Lei Hsiung, et al.
ICLR 2024
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.
Hsi-ai Tsao, Lei Hsiung, et al.
ICLR 2024
Teng Xiao, Huaisheng Zhu, et al.
ICML 2024
Jannis Born, Matteo Manica
ICLR 2022
Thanh Nguyen, Natasha Mulligan, et al.
AMIA Informatics Summit 2021