Reliable quantification of uncertainties: the biggest challenge for 
data-centric materials modelling?

Ghiringhelli, L. M.; Rossi, M.

doi:10.1088/1361-651X/ad4d0d

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Reliable quantification of uncertainties: the biggest challenge for data-centric materials modelling?

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Rossi, M.
Simulations from Ab Initio Approaches, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;

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https://arxiv.org/abs/2402.10932
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https://doi.org/10.1088/1361-651X/ad4d0d
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Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf
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Citation

Ghiringhelli, L. M., & Rossi, M. (2024). Reliable quantification of uncertainties: the biggest challenge for data-centric materials modelling? Modelling and Simulation in Materials Science and Engineering, 32(6), 13-15. doi:10.1088/1361-651X/ad4d0d.

Cite as: https://hdl.handle.net/21.11116/0000-000E-70A0-E

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.