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1. New frontiers for the materials genome initiative

   https://doi.org/10.1038/s41524-019-0173-4  

   de Pablo, Juan J.; Jackson, Nicholas E.; Webb, Michael A.; Chen, Long-Qing; Moore, Joel E.; Morgan, Dane; Jacobs, Ryan; Pollock, Tresa; Schlom, Darrell G.; Toberer, Eric S.; et al (April 2019, npj Computational Materials)

   Abstract The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts. 

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2. MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art

   https://doi.org/10.1038/s41524-022-00750-6  

   Hu, Jianjun; Stefanov, Stanislav; Song, Yuqi; Omee, Sadman Sadeed; Louis, Steph-Yves; Siriwardane, Edirisuriya M. D.; Zhao, Yong; Wei, Lai (April 2022, npj Computational Materials)

   Abstract The availability and easy access of large-scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, the lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein we first survey current materials informatics web apps and then propose and develop MaterialsAtlas.org, a web-based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including material’s composition and structure validity check (e.g. charge neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, and thermal conductivity), search for hypothetical materials, and utility tools. These user-friendly tools can be freely accessed athttp://www.materialsatlas.org. We argue that such materials informatics apps should be widely developed by the community to speed up materials discovery processes. 

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3. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

   https://doi.org/10.1038/s41524-020-00440-1  

   Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C.; DeCost, Brian; Biacchi, Adam J.; Hight Walker, Angela R.; Trautt, Zachary; Hattrick-Simpers, Jason; Kusne, A. Gilad; Centrone, Andrea; et al (December 2020, npj Computational Materials)

   null (Ed.)

   Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov . 

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4. Systems Approaches to Materials Design: Past, Present, and Future

   https://doi.org/10.1146/annurev-matsci-070218-125955  

   Arróyave, Raymundo; McDowell, David L. (July 2019, Annual Review of Materials Research)

   There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design. 

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5. The Potential Utility of Materials Informatics in Developing Non-Conventional Materials.

   Zheng, C.; Akinbade, Y.; Nettleship, I.; Harries, K.A.. (January 2019, 18th International Conference on Non-Conventional Materials and Technologies (18NOCMAT))

   Materials informatics is widely acknowledged as a means of accelerating the rate of new materials discovery and material development. Such an approach will require widespread use of data driven methodologies and it is anticipated that downstream stakeholders, particularly industry, will be heavily involved in providing the required resources. This paper will propose workflows that illustrate how data driven methods could reasonably be applied to the processing-structure-properties relationships of locally sourced non-conventional materials. It is anticipated that simple material tests and imaging techniques will provide sufficient digital information for data driven approaches at relatively low cost. This utility will be illustrated using the example of bamboo and the levels of structure observable using widely available digital phone cameras. Finally, the possible role of the NOCMAT community in facilitating materials informatics will be considered. 

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