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Found 6 papers, 1 papers with code
Interpretable models for extrapolation in scientific machine learning
In efforts to achieve state-of-the-art model accuracy, researchers are employing increasingly complex machine learning algorithms that often outperform simple regressions in interpolative settings (e. g. random k-fold cross-validation) but suffer from poor extrapolation performance, portability, and human interpretability, which limits their potential for facilitating novel scientific insight.
AutoMat: Accelerated Computational Electrochemical systems Discovery
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation.
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.
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.
