Machine Learning Accelerates Materials Development
Learn how Citrine's platform supported Panasonic as it developed new organic semiconductors.
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. However, standard global error metrics for model quality are not predictive of discovery performance,...
Machine Learning for Alloy Composition and Process Optimization
The drive for greater efficiency in turbomachinery has led to increasingly stringent specifications for the materials used. Current methods for optimizing alloy composition and processing to meet these requirements...
Research Newsletter Archive
The Data-Driven Material Science & Chemistry newsletter provides weekly scholarly and community news and updates on data science and physical science.
Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior
In general, data-driven approaches aim to identify objectively (relying largely on the available data) the embedded correlations among selected inputs and outputs needed to study or model a given...
The 2019 Materials by Design Roadmap
Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling...
Strategies for accelerating the adoption of materials informatics
Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials...
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-one-cluster-out cross-validation (LOCO CV) and (2) a simple nearest-neighbor benchmark to show that model performance...
Materials Data Infrastructure and Materials Informatics
Data-driven materials research requires two key supporting components: data infrastructure and informatics. In this chapter, we review the state of the art in materials data infrastructure, focusing in detail...
Overcoming data scarcity with transfer learning
In this paper, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.