2018

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 phenomenon. Data-driven approaches are particularly advantageous when a full, physics-based understanding of relevant phenomena is lacking, and also when gathering new experimental data is particularly slow […]

Papers By Citrine
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 between materials prediction, synthesis and characterization. Increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. […]

Papers By Citrine
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 R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials […]

Papers By Citrine
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 in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering […]

Papers By Citrine
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 on four infrastructure projects spanning academia, government, and industry. We also discuss data standards as an enabling step on the path to community-scale materials data infrastructure. […]

Papers By Citrine

2017

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.

Papers By Citrine
Building Data-driven Models with Microstructural Images: Generalization and Interpretability

This paper explores the use of convolutional neural networks for classifying microstructure.

Papers By Citrine
3D Printing of High-Strength Aluminium Alloys

Metal-based additive manufacturing, or three-dimensional (3D) printing, is a potentially disruptive technology across multiple industries, including the aerospace, biomedical and automotive industries. Building up metal components layer by layer increases design freedom and manufacturing flexibility, thereby enabling complex geometries, increased product customization and shorter time to market, while eliminating traditional economy-of-scale constraints. However, currently only […]

Papers By Citrine
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. We propose a methodology that can accelerate this process by fitting data-driven models to experimental data as it is collected to suggest which experiment should be performed next. […]

Papers By Citrine
Robust FCC solute diffusion predictions from ab-initio machine learning methods

We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural […]

Papers By Citrine