Accelerating materials discovery for polymer solar cells: data-driven insights enabled by natural language processing

P Shetty, A Adeboye, S Gupta, C Zhang… - Chemistry of …, 2024 - ACS Publications
We present a simulation of various active learning strategies for the discovery of polymer
solar cell donor/acceptor pairs using data extracted from the literature spanning∼ 20 years …

Designing workflows for materials characterization

SV Kalinin, M Ziatdinov, M Ahmadi, A Ghosh… - Applied Physics …, 2024 - pubs.aip.org
Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …

Discovering optimal flap** wing kinematics using active deep learning

B Corban, M Bauerheim, T Jardin - Journal of Fluid Mechanics, 2023 - cambridge.org
This paper focuses on the discovery of optimal flap** wing kinematics using a deep
learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First …

Active learning for neural pde solvers

D Musekamp, M Kalimuthu, D Holzmüller… - ar** efficient methods to find materials that satisfy multiple properties simultaneously
is an important task so that the material screening process can be accelerated with reduced …

Artificial intelligence for materials research at extremes

B Maruyama, J Hattrick-Simpers, W Musinski… - MRS Bulletin, 2022 - Springer
Materials development is slow and expensive, taking decades from inception to fielding. For
materials research at extremes, the situation is even more demanding, as the desired …

Targeted materials discovery using Bayesian algorithm execution

SR Chitturi, A Ramdas, Y Wu, B Rohr… - npj Computational …, 2024 - nature.com
Rapid discovery and synthesis of future materials requires intelligent data acquisition
strategies to navigate large design spaces. A popular strategy is Bayesian optimization …