论文标题
无机材料设计的深度加固学习
Deep Reinforcement Learning for Inverse Inorganic Materials Design
论文作者
论文摘要
实现具有理想特性的新型无机材料的主要障碍是无法在材料特性和这些材料的合成中进行有效的优化。在这项工作中，我们提出了一种增强学习方法（RL）方法来进行反无机材料设计，该方法可以识别具有指定属性和合成性约束的有希望的化合物。我们的模型学习化学指南，例如电荷和电负性中立性，同时保持化学多样性和独特性。我们展示了一种多目标RL方法，该方法可以生成具有靶向材料特性的新型化合物，包括形成能量和散装/剪切模量以及较低的烧结温度合成目标。使用这种方法，该模型可以预测有希望的感兴趣的化合物，同时提出了针对无机材料发现的优化化学设计空间。
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this work, we propose a reinforcement learning (RL) approach to inverse inorganic materials design, which can identify promising compounds with specified properties and synthesizability constraints. Our model learns chemical guidelines such as charge and electronegativity neutrality while maintaining chemical diversity and uniqueness. We demonstrate a multi-objective RL approach, which can generate novel compounds with targeted materials properties including formation energy and bulk/shear modulus alongside a lower sintering temperature synthesis objectives. Using this approach, the model can predict promising compounds of interest, while suggesting an optimized chemical design space for inorganic materials discovery.
