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



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


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.
Communications Materials


AI, machine learning, materials science


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],, (Accessed April 20, 2024)
Created August 30, 2022, Updated November 29, 2022