The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Extensive efforts to gather materials data have largely overlooked potential data
redundancy. In this study, we present evidence of a significant degree of redundancy across …

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 …

Machine learning, as a significant branch of artificial intelligence, has provided effective
guidance for material design by establishing virtual mappings between data and desired …

Experimental science is enabled by the combination of synthesis, imaging, and functional
characterization organized into evolving discovery loop. Synthesis of new material is …

This paper focuses on the discovery of optimal flapping wing kinematics using a deep
learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First …

Solving partial differential equations (PDEs) is a fundamental problem in engineering and
science. While neural PDE solvers can be more efficient than established numerical solvers …

Developing 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 …

Rapid discovery and synthesis of future materials requires intelligent data acquisition
strategies to navigate large design spaces. A popular strategy is Bayesian optimization …

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 …