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Grand Challenges in Computational Materials Science: From description to prediction at all scales [PDF]
Frontiers in Materials, 2014 TRADITIONAL COMPUTATIONAL MATERIALS SCIENCE Materials science is historically linked to engineering driven by the need of materials with specific properties to manufacture infrastructure, machines, and devices. Therefore, there has always been a need for novel and better materials: stronger and lighter-weight, less expensive, easier to process, more ...
Thomas eHeine
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Implementing a Registry Federation for Materials Science Data Discovery
Data Science Journal, 2021 As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available ...
Raymond L. Plante +8 more
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Big Data-Driven Materials Science and Its FAIR Data Infrastructure [PDF]
Handbook of Materials Modeling, 2019 This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed.
C. Draxl, M. Scheffler
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Academia Materials Science: Advances and innovation in all topics of materials science
Academia Materials Science, 2023 Raul Duarte Salgueiral Gomes Campilho
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Journal of Science Teacher Education, 2021 The adoption of A Framework for K-12 Science Education and the Next Generation Science Standards (NGSS) across the U.S.
Alison Haas +5 more
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Graph neural networks for materials science and chemistry [PDF]
Communications Materials, 2022 Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
Patrick Reiser +10 more
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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science. [PDF]
Journal of Chemical Physics, 2023 The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks.
D. P. Kovács +3 more
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Advances of machine learning in materials science: Ideas and techniques [PDF]
Frontiers of Physics, 2023 In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large ...
S. Chong +3 more
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The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding [PDF]
npj Computational Materials, 2022 We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data.
Luigi Sbailò +3 more
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