New optimization algorithm enhances crystal structure prediction

By PulseAugur Editorial · [1 sources] · 2026-05-16 20:45

Researchers have developed an improved Manta Ray Foraging Optimization algorithm, enhanced with Levy Flight, to train Extreme Learning Machines (ELMs). This new method, termed Evolutionary EELM-MRFO-LF, is applied to predict the formation energy of compounds in binary systems, aiming to improve crystal structure prediction. The algorithm's performance is benchmarked against other nature-inspired optimization techniques. AI

IMPACT Introduces a novel optimization technique for machine learning models, potentially improving accuracy in materials science predictions.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its application. [lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; The cluster contains an academic paper detailing a new algorithm and its application. [lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Adrian Rubio-Solis · 2026-05-16 20:45

Evolutionary Extreme Learning Machine of ab-initio Energy Landscapes for Crystal Structure Prediction using Manta Ray Optimization with Levy Flight

The Manta Ray Foraging Optimization algorithm (MRFO) has proven to be a powerful heuristic strategy in the optimal solution of a large number of engineering problems. In this paper, an improvement of MRFO with Levy Flight is suggested for the training of extreme learning machines…