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Machine-learned metrics for predicting the likelihood of success in materials discovery
Materials discovery is often compared to the challenge of finding a needle in a haystack.
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning.
Overcoming data scarcity with transfer learning
Here, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure.
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck.
