Npj Comput. Mater.: 原子间固有的游戏规则—设计人工智能材料

海归学者发起的公益学术平台

分享信息，整合资源

交流学术，偶尔风月

这一利用深度学习融入约束来进行更有效搜索的化腐朽（约束）为神奇（有利）的思路，在其他工程设计、分子设计、蛋白质设计、药物设计等领域都有巨大应用潜力。该文近期发表于npj Computational Materials 9：38(2023)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

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

Yong Zhao, Edirisuriya M. Dilanga Siriwardane,  Zhenyao Wu,  Nihang Fu,  Mohammed Al-Fahdi,  Ming Hu & Jianjun Hu

Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches 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 crystal material 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 model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Database www.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.