A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84.5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules. Our algorithm could be used to speed up inverse design or computational screening of inorganic materials.

翻译：在材料设计方面,一个重大挑战是如何有效地搜索庞大的化学设计空间,以找到具有所需特性的材料。一个有效的战略是制定抽样算法,既可以利用显性化学知识,也可以利用大型材料数据库所包含的隐含成分规则。在这里,我们提议了一个基于基因对抗网络的基因化机器学习模型(MatGAN),以高效生成新的假设无机材料。我们GAN模型通过由ICSD数据库提供的材料培训,可以产生培训数据集中不存在的假设材料,在产生200万个样本时达到92.53%的新颖值。所有生成样本中化学有效样本(充电中和电子平衡)的百分比(在接受ICSD材料培训时,我们GAN的比例达到84.5%,尽管我们的GAN模型没有明确执行这种化学规则,表明其学习隐性化学成分规则的能力。我们的算法可以用来加速对无机材料的反向设计或计算筛选。