A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials†
Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials. Using existing databases for hydrogen storage alloys, a regression model to predict the enthalpy of hydrogenation was generated with a mean absolute error of 8.56 kJ mol−1 and a mean relative error of 28%. Model predictions for new hydride materials were constrained by techno-economic analysis and used to identify 6110 potential alloys matching the criteria required for hydrogen compressors. Additional constraints such as alloy cost, composition, and likely structure were used to reduce the number of possible alloys for experimental verification to less than 400. Finally, expert heuristics and a novel machine learning approach to approximating alloy stability were employed to select an Fe–Mn–Ti–X alloy system for future experimental studies.
- This article is part of the themed collections: Industry R&D collection and Machine Learning and Data Science in Materials Design