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% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

材料设计的主要挑战是如何有效地搜索广阔的化学设计空间，以找到具有所需性能的材料。一种有效的策略是开发可利用显式化学知识和大型材料数据库中包含的隐式组成规则的采样算法。在这里，我们提出了一种基于生成对抗网络（GAN）的生成机器学习模型（MatGAN），以有效地生成新的假设无机材料。通过使用ICSD数据库中的材料进行训练，我们的GAN模型可以生成训练数据集中不存在的假设性材料，当生成200万个样本时，新颖性达到92.53％。在所有生成的样本中，化学有效的样本（电荷中性和电负性平衡）的百分比达到84。即使我们的GAN模型中未明确执行此类化学规则，但由训练有素的GAN（使用从ICSD筛选的此类样品进行训练）生成的5％仍表明其具有学习隐式化学组成规则以形成化合物的能力。我们的算法有望用于大大扩展无机材料的逆向设计和大规模计算筛选的设计空间范围。