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Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

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Energy and AI 2023年第4期14卷 360-370页

作者： joshua ojih Alejandro Rodriguez Jianjun Hu Ming Hu Department of Mechanical Engineering University of South CarolinaSC 29208United States of America Department of Computer Science and Engineering University of South CarolinaSC 29208United States of America

Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy ***,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and *** contrast,prediction at the classical level is highly efficient but less *** deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical *** 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and *** the trained model,we predicted 775,947 data in search of materials with ultrahigh *** further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in *** those,two super hard materials are completely new and have not been reported in literature so *** further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first *** results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.

关键词： Graph neural network Machine learning Mechanical properties Ultrahigh hardness Lattice thermal conductivity DFT calculations Novel material discovery

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Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

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npj Computational Materials 2022年第1期8卷 1336-1347页

作者： joshua ojih Mohammed Al-Fahdi Alejandro David Rodriguez Kamal Choudhary Ming Hu Department of Mechanical Engineering University of South CarolinaColumbiaSC29208USA Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA

Despite the machine learning(ML)methods have been largely used recently,the predicted materials properties usually cannot exceed the range of original training *** deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory(DFT)in searching extreme material *** combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry,but also yields properties beyond original training *** use Stein novelty to recommend outliers and then verify using *** data are then added into the training dataset for next round *** test the loop of training-recommendation-validation in mechanical property *** screening 85,707 crystal structures,we identify 21 ultrahigh hardness structures and 11 negative Poisson’s ratio *** algorithm is very promising for future materials discovery that can push materials properties to the limit with minimal DFT calculations on only~1%of the structures in the screening pool.

关键词： extreme properties minimal

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