Why big data and compute are not necessarily the path to big materials science

Published

August 30, 2022

Author(s)

Naohiro Fujinuma, Brian DeCost, Jason Hattrick-Simpers, Sam Lofland

Abstract

Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental decision-making have become commonplace. Here, we reflect on the ongoing shift in the conversation from proving that machine learning can be used, to how to effectively implement it for advancing materials science. In particular, we advocate a shift from a big data and large-scale computations mentality to a model-oriented approach that prioritizes the use of machine learning to support the ecosystem of computational models and experimental measurements. We also recommend an open conversation about dataset bias to stabilize productive research through careful model interrogation and deliberate exploitation of known biases. Further, we encourage the community to develop machine learning methods that connect experiments with theoretical models to increase scientific understanding rather than incrementally optimizing materials. Moreover, we envision a future of radical materials innovations enabled by computational creativity tools combined with online visualization and analysis tools that support active outside-the-box thinking within the scientific knowledge feedback loop.

Citation

Communications Materials

Volume

3

Pub Type

Journals

Download Paper

https://doi.org/10.1038/s43246-022-00283-x

Local Download

Keywords

AI, machine learning, materials science

Citation

Fujinuma, N. , DeCost, B. , Hattrick-Simpers, J. and Lofland, S. (2022), Why big data and compute are not necessarily the path to big materials science, Communications Materials, [online], https://doi.org/10.1038/s43246-022-00283-x, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933719 (Accessed October 20, 2025)