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1. 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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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. Digital Twins for Materials

   https://doi.org/10.3389/fmats.2022.818535  

   Kalidindi, Surya R.; Buzzy, Michael; Boyce, Brad L.; Dingreville, Remi (March 2022, Frontiers in Materials)

   Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data. 

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4. ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

   https://doi.org/10.1002/wcms.1458  

   Haghighatlari, Mojtaba; Vishwakarma, Gaurav; Altarawy, Doaa; Subramanian, Ramachandran; Kota, Bhargava U.; Sonpal, Aditya; Setlur, Srirangaraj; Hachmann, Johannes (January 2020, WIREs Computational Molecular Science)

   Abstract ChemMLis an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain.ChemMLallows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community.ChemMLis also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under:Software > Simulation MethodsComputer and Information Science > ChemoinformaticsStructure and Mechanism > Computational Materials ScienceSoftware > Molecular Modeling 

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5. Materials discovery through machine learning formation energy

   https://doi.org/10.1088/2515-7655/abe425  

   Peterson, Gordon G. C.; Brgoch, Jakoah (March 2021, Journal of Physics: Energy)

   Abstract The budding field of materials informatics has coincided with a shift towards artificial intelligence to discover new solid-state compounds. The steady expansion of repositories for crystallographic and computational data has set the stage for developing data-driven models capable of predicting a bevy of physical properties. Machine learning methods, in particular, have already shown the ability to identify materials with near ideal properties for energy-related applications by screening crystal structure databases. However, examples of the data-guided discovery of entirely new, never-before-reported compounds remain limited. The critical step for determining if an unknown compound is synthetically accessible is obtaining the formation energy and constructing the associated convex hull. Fortunately, this information has become widely available through density functional theory (DFT) data repositories to the point that they can be used to develop machine learning models. In this Review, we discuss the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability. We investigate several models presented in the literature that cover various possible architectures and feature sets and find that they have succeeded in uncovering new DFT-stable compounds and directing materials synthesis. To expand access to machine learning models for synthetic solid-state chemists, we additionally presentMatLearn. This web-based application is intended to guide the exploration of a composition diagram towards regions likely to contain thermodynamically accessible inorganic compounds. Finally, we discuss the future of machine-learned formation energy and highlight the opportunities for improved predictive power toward the synthetic realization of new energy-related materials. 

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