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.

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.

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.

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm.

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward.

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).

Machine-learning guided discovery of a new thermoelectric material.

Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect.

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.

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 (300 G) and magnetization of 1000 kA m.

High-throughput determination of structural phase diagram and constituent phases using GRENDEL.

Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material process-structure-property relationships requires high-throughput data analysis techniques. This work explores the use of the Graph-based endmember extraction and labeling (GRENDEL) algorithm as a high-throughput method for analyzing structural data from combinatorial libraries, specifically, to determine phase diagrams and constituent phases from both x-ray diffraction and Raman spectral data.

Volatile organic compound detection using nanostructured copolymers.

Regioregular polythiophene-based conductive copolymers with highly crystalline nanostructures are shown to hold considerable promise as the active layer in volatile organic compound (VOC) chemresistor sensors. While the regioregular polythiophene polymer chain provides a charge conduction path, its chemical sensing selectivity and sensitivity can be altered either by incorporating a second polymer to form a block copolymer or by making a random copolymer of polythiophene with different alkyl side chains. The copolymers were exposed to a variety of VOC vapors, and the electrical conductivity of these copolymers increased or decreased depending upon the polymer composition and the specific analytes.