January 8, 2025
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An AI software might assist optimize methane storage options
A brand new method harnesses machine studying to seek for supplies to retailer methane, serving to speed up the adoption of methane as a cleaner various gasoline for automobiles. The College of Michigan-led examine is revealed in Bodily Overview Supplies.
Though methane boasts a better vitality density and a 25% decrease carbon footprint than gasoline, it stays a gasoline at room temperature, making it tough to retailer. Up so far, methane has been saved in heavy, extremely pressurized tanks or at cryogenic temperatures, stopping sensible adoption as a gasoline various.
Not too long ago, covalent natural frameworks (COFs)—a category of light-weight, extremely porous supplies—have been explored as a substitute storage methodology that works by adhering methane to their surfaces. Whereas high-throughput computational screening has recognized potential COFs, the sheer quantity of potentialities and the necessity for intensive simulations limits progress.
"The urgent want for cleaner vitality options motivates me to develop modern, accessible, and environment friendly instruments to optimize methane storage supplies," mentioned Alauddin Ahmed, a U-M affiliate analysis scientist of mechanical engineering and corresponding writer of the examine.
The brand new method combines machine studying with symbolic regression—a kind of research that searches for the very best mathematical equation to explain an noticed dataset. The ensuing, easily-interpretable equations predicted methane storage capability with excessive accuracy at 4.2% imply absolute proportion error.
"By prioritizing bodily, significant and measurable options, we've made it simpler for experimentalists to use these fashions instantly, enabling broader participation within the area and accelerating the event of high-performance supplies," mentioned Ahmed.
The high-fidelity fashions recognized tons of of COFs with superior efficiency, together with some that meet U.S. Division of Vitality targets for methane storage.
Bridging the hole between computational materials discovery and sensible software, the fashions empower researchers to quickly determine promising methane storage supplies with out counting on costly and time-intensive simulations.
This examine assessed 84,800 potential COFs, marking the primary software of symbolic regression to a large-scale dataset. A multistage computational workflow made this feat potential, decreasing computational calls for by figuring out consultant subsets of bigger datasets (e.g., 400 COFs) for symbolic regression.
"We anticipated the symbolic regression fashions to wrestle with the complexity of the dataset, given its dimension and the various nature of COFs. What shocked us was how successfully the multistage technique labored, permitting the algorithm to derive interpretable equations that maintained excessive predictive accuracy even on unseen information," mentioned Ahmed.
The multistage method additionally builds in flexibility, permitting equations to evolve as new information turns into out there. The mannequin's adaptability gives a scalable framework for optimizing solid-state adsorbents like COFs or different areas like renewable vitality storage, gasoline cells and superior batteries.
The mixture of machine studying and symbolic regression may be tailored to different domains akin to catalysis, prescribed drugs or any area involving a posh relationship between a cloth's construction and its properties.
In a dedication to open science, all of the datasets used on this analysis are publicly out there on the Zenodo repository. The analysis used open-source software program like RASPA and SISSO for simulations and symbolic regression.
Extra info: Alauddin Ahmed, Machine-learning-enhanced symbolic regression for methane storage prediction in covalent natural frameworks, Bodily Overview Supplies (2024). DOI: 10.1103/PhysRevMaterials.8.115408
Supplied by College of Michigan School of Engineering Quotation: An AI software might assist optimize methane storage options (2025, January 8) retrieved 8 January 2025 from https://techxplore.com/information/2025-01-ai-tool-optimize-methane-storage.html This doc is topic to copyright. Other than any truthful dealing for the aim of personal examine or analysis, no half could also be reproduced with out the written permission. The content material is supplied for info functions solely.
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