Abstract
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
Original language | English |
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Article number | 063301 |
Journal | Modelling and Simulation in Materials Science and Engineering |
Volume | 32 |
Issue number | 6 |
DOIs | |
State | Published - Sep 2024 |
Funding
This project was supported by the NOMAD Center of Excellence (European Union\u2019s Horizon 2020 research and innovation program, Grant Agreement No. 951786) and the ERC Advanced Grant TEC1p (European Research Council, Grant Agreement No. 740233). LMG acknowledges funding from the NOMAD Center of Excellence (European Union\u2019s Horizon 2020 research and innovation program, Grant Agreement No. 951786) and the project FAIRmat (FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids, German Research Foundation, Project No. 460197019). We acknowledge financial support from BiGmax, the Max Planck Society\u2019s Research Network on Big-Data-Driven Materials-Science. We thank Matthias Scheffler for a critical read of the earlier version of this manuscript. The author thanks BiGmax, the Max Planck Society\u2019s Research Network on Big-Data-Driven Materials-Science, for support and stimulating interdisciplinary interactions with members of the consortium. The authors are grateful for funding by the EPSRC Centre-to Centre Project (Grant Reference. EP/S030468/1). AJL acknowledges funding by the UKRI Future Leaders Fellowship program (MR/T018372/1). ZL acknowledges funding by the China Scholarship Council. The work was partially supported by BiGmax, the Max Planck Society\u2019s Research Network on Big-Data-Driven Materials Science. Y Li acknowledges the research fellowship provided by the Alexander von Humboldt Foundation. A S appreciates funding by Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE). BG acknowledges support from the Deutsche Forschungsgemeinschaft (DFG) for funding from the Leibniz Prize 2020 (GA 2450/2-1) and for funding of the TRR 270 HoMMage (INST 163/578-1). B G, Y W, Y L and C F are grateful for financial support from BiGmax, the Max Planck Society\u2019s Research Network on Big-Data-Driven Materials Science. The colleagues from the Max Planck Computing and Data Facility, Garching, Germany are warmly acknowledged for their help and support. This work was supported by the Australian Research Council (DP210100045) and the ERC Advanced Grant TEC1p (European Research Council, Grant Agreement No. 740233). M S and C T K acknowledge financial support from the DFG\u2014Project-ID 414984028\u2014SFB 1404.
Keywords
- centric
- data
- materials
- molecular simulations
- roadmap
- science