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1. Data-Enabled Discovery and Design of Energy Materials (D3EM): Structure of An Interdisciplinary Materials Design Graduate Program

   https://doi.org/10.1557/adv.2017.228  

   Chang, Chi-Ning; Semma, Brandie; Pardo, Marta Lynn; Fowler, Debra; Shamberger, Patrick; Arroyave, Raymundo (January 2017, MRS Advances)

   ABSTRACT The Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the integration of experiments and simulations within a data-aware/enabling framework. To realize this vision, MGI recognizes the need for the creation of a new kind of workforce capable of creating and/or deploying advanced informatics tools and methods into the materials discovery/development cycle. An interdisciplinary team at Texas A&M seeks to address this challenge by creating an interdisciplinary program that goes beyond MGI in that it incorporates the discipline of engineering systems design as an essential component of the new accelerated materials development paradigm. The Data-Enabled Discovery and Development of Energy Materials (D 3 EM) program seeks to create an interdisciplinary graduate program at the intersection of materials science, informatics, and design. In this paper, we describe the rationale for the creation of such a program, present the pedagogical model that forms the basis of the program, and describe some of the major elements of the program. 

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2. Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

   https://doi.org/10.1557/adv.2020.171  

   Oweida, Thomas J.; Mahmood, Akhlak; Manning, Matthew D.; Rigin, Sergei; Yingling, Yaroslava G. (January 2020, MRS Advances)

   ABSTRACT Since the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects. 

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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. Creating the Next-Generation Materials Genome Initiative Workforce

   https://doi.org/10.7449/mgiworkforce_1  

   Minerals Metals & Materials Society Inc, The (April 2019, Creating the Next-Generation Materials Genome Initiative Workforce)

   Building a world-class materials science and engineering workforce proficient in the tools and techniques necessary to accelerate the discovery, development, and deployment of advanced materials is one of four goals presented in the 2014 strategic plan of the Materials Genome Initiative (MGI). As we approach nearly a decade of progress since the initiative was first announced in 2011, this latest technical report from TMS builds on previous work and offers a series of action plans and recommendations that address the strategic plan’s goals for education and training initiatives, which are critical to achieving the MGI’s national objectives. The report also synthesizes the results of an in-depth examination of the required knowledge and skills for individuals entering or already in the workforce. 

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5. Intersystem crossing and exciton–defect coupling of spin defects in hexagonal boron nitride

   https://doi.org/10.1038/s41524-021-00525-5  

   Smart, Tyler J.; Li, Kejun; Xu, Junqing; Ping, Yuan (December 2021, npj Computational Materials)

   null (Ed.)

   Abstract Despite the recognition of two-dimensional (2D) systems as emerging and scalable host materials of single-photon emitters or spin qubits, the uncontrolled, and undetermined chemical nature of these quantum defects has been a roadblock to further development. Leveraging the design of extrinsic defects can circumvent these persistent issues and provide an ultimate solution. Here, we established a complete theoretical framework to accurately and systematically design quantum defects in wide-bandgap 2D systems. With this approach, essential static and dynamical properties are equally considered for spin qubit discovery. In particular, many-body interactions such as defect–exciton couplings are vital for describing excited state properties of defects in ultrathin 2D systems. Meanwhile, nonradiative processes such as phonon-assisted decay and intersystem crossing rates require careful evaluation, which competes together with radiative processes. From a thorough screening of defects based on first-principles calculations, we identify promising single-photon emitters such as Si VV and spin qubits such as Ti VV and Mo VV in hexagonal boron nitride. This work provided a complete first-principles theoretical framework for defect design in 2D materials. 

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