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Physics guided deep learning for generative design of crystal materials with symmetry constraints

npj Computational Materials 2023年第1期9卷 1969-1980页

作者： Yong Zhao Edirisuriya M.Dilanga Siriwardane Zhenyao Wu nihang fu Mohammed Al-Fahdi Ming Hu Jianjun Hu Department of Computer Science and Engineering University of South Carolina550 Assembly St.29201 ColumbiaSCUSA Department of Physics University of ColomboColombo 3Sri Lanka Department of Mechanical Engineering University of South Carolina301 Main Street29201 ColumbiaSCUSA

Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventionalapproaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystalmaterial design with high structural diversity and symmetry. Our model increases the generation validity by more than 700%compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN *** functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 aresuccessfully optimized and deposited into the Carolina Materials Database ***, of which 39.6% have negativeformation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potentialsynthesizability.

关键词： symmetry crystal materials

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Generative AI for Materials Discovery:Design Without Understanding

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Engineering 2024年第8期39卷 13-17页

作者： Jianjun Hu Qin Li nihang fu Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29208USA College of Big Data Statistics Guizhou University of Finance and EconomicsGuiyang 550025China

*** as building-block assembling In the 21st century,society is facing several fundamental challenges,including global climate change,the energy crisis,and public health crises such as cancers and coronavirus disease 2019(COVID-19).Their solutions share a certain commonality:They all involve the discovery of novel atomic structures such as materials,molecules,proteins,and *** such functional atomic structures is challenging due to the astonishing complexity of their inter-atomic interactions,sophisticated physical/chemical/geometric constraints and patterns in the formation of stable structures,and the relation between structures and their functions.

关键词： functions. atomic constraints

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Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints

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npj Computational Materials 2023年第1期9卷 1275-1275页

作者： Yong Zhao Edirisuriya M.Dilanga Siriwardane Zhenyao Wu nihang fu Mohammed Al-Fahdi Ming Hu Jianjun Hu Department of Computer Science and Engineering University of South Carolina 550 Assembly St. 29201 Columbia SC USA Department of Physics University of Colombo Colombo 3 Sri Lanka Department of Mechanical Engineering University of South Carolina 301 Main Street 29201 Columbia SC USA

Correction to:npj Computational Materials https://***/10.1038/s41524-023-00987-9,published online 18 March 2023 The original version of this Article contained typos in both the PDF and the HTML *** the fourth and fifth sentences of the first paragraph of the‘Case study of three example stable materials’section of the Results,the incorrect expression“β=β”has been replaced by“α=β”.

关键词： HTML constraints symmetry

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