A framework for materials informatics education through workshops.

Abstract: The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. In this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

A semi-supervised deep-learning approach for automatic crystal structure classification.

The structural solution problem can be a daunting and time-consuming task. Especially in the presence of impurity phases, current methods, such as indexing, become more unstable. In this work, the novel approach of semi-supervised learning is applied towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. The reported semi-supervised generative deep-learning model can train on both labeled data, i.e. diffraction patterns with the associated crystal structure, and unlabeled data, i.e. diffraction patterns that lack this information. This approach allows the models to take advantage of the troves of unlabeled data that current supervised learning approaches cannot, which should result in models that can more accurately generalize to real data. In this work, powder diffraction patterns are classified into all 14 Bravais lattices and 144 space groups (the number is limited due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies. The reported models also outperform current deep-learning approaches for both space group and Bravais lattice classification using fewer training data.

On-the-fly closed-loop materials discovery via Bayesian active learning.

Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods.

High-throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many technological applications. However, there are very few publications on the reproducibility of the HTE results obtained across different laboratories for the same materials system, and on the associated sample and data exchange standards. Here, we report a comparative study of Zn-Sn-Ti-O thin films materials using high-throughput experimental methods at National Institute of Standards and Technology (NIST) and National Renewable Energy Laboratory (NREL). The thin film sample libraries were synthesized by combinatorial physical vapor deposition (cosputtering and pulsed laser deposition) and characterized by spatially resolved techniques for composition, structure, thickness, optical, and electrical properties. The results of this study indicate that all these measurement techniques performed at two different laboratories show excellent qualitative agreement. The quantitative similarities and differences vary by measurement type, with 95% confidence interval of 0.1-0.2 eV for the band gap, 24-29 nm for film thickness, and 0.08 to 0.37 orders of magnitude for sheet resistance. Overall, this work serves as a case study for the feasibility of a High-Throughput Experimental Materials Collaboratory (HTE-MC) by demonstrating the exchange of high-throughput sample libraries, workflows, and data.

Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics.

Today's cities generate tremendous amounts of data, thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context-aware services and systems, ensuring smart city services are optimized to the dynamic city environment. Critical resources in these smart cities will be more rapidly deployed to regions in need, and those regions predicted to have an imminent or prospective need. For example, crime data analytics may be used to optimize the distribution of police, medical, and emergency services. However, as smart city services become dependent on data, they also become susceptible to disruptions in data streams, such as data loss due to signal quality reduction or due to power loss during data collection. This paper presents a dynamic network model for improving service resilience to data loss. The network model identifies statistically significant shared temporal trends across multivariate spatiotemporal data streams and utilizes these trends to improve data prediction performance in the case of data loss. Dynamics also allow the system to respond to changes in the data streams such as the loss or addition of new information flows. The network model is demonstrated by city-based crime rates reported in Montgomery County, MD, USA. A resilient network is developed utilizing shared temporal trends between cities to provide improved crime rate prediction and robustness to data loss, compared with the use of single city-based auto-regression. A maximum improvement in performance of 7.8% for Silver Spring is found and an average improvement of 5.6% among cities with high crime rates. The model also correctly identifies all the optimal network connections, according to prediction error minimization. City-to-city distance is designated as a predictor of shared temporal trends in crime and weather is shown to be a strong predictor of crime in Montgomery County.

Combinatorial study of Fe-Co-V hard magnetic thin films.

Thin film libraries of Fe-Co-V were fabricated by combinatorial sputtering to study magnetic and structural properties over wide ranges of composition and thickness by high-throughput methods: synchrotron X-ray diffraction, magnetometry, composition, and thickness were measured across the Fe-Co-V libraries. In-plane magnetic hysteresis loops were shown to have a coercive field of 23.9 kA m-1 (300 G) and magnetization of 1000 kA m-1. The out-of-plane direction revealed enhanced coercive fields of 207 kA m-1 (2.6 kG) which was attributed to the shape anisotropy of column grains observed with electron microscopy. Angular dependence of the switching field showed that the magnetization reversal mechanism is governed by 180° domain wall pinning. In the thickness-dependent combinatorial study, co-sputtered composition spreads had a thickness ranging from 50 to 500 nm and (Fe70Co30)100-xVx compositions of x = 2-80. Comparison of high-throughput magneto-optical Kerr effect and traditional vibrating sample magnetometer measurements show agreement of trends in coercive fields across large composition and thickness regions.