首页|University of California Reports Findings in Machine Learning (De- veloping Cheap but Useful Machine Learning-Based Models for In- vestigating High-Entropy Alloy Catalysts)
University of California Reports Findings in Machine Learning (De- veloping Cheap but Useful Machine Learning-Based Models for In- vestigating High-Entropy Alloy Catalysts)
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2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting originating from Davis, California, by NewsRx correspondents, research stated, “This work aims to address the challenge of developing interpretable ML-based models when access to large-scale compu- tational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NH ( = 1, 2, and 3) adsorbates.”
DavisCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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