A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces. In this work, we propose and compare two novel reinforcement learning (RL) approaches to inverse inorganic oxide materials design to target promising compounds using specified property and synthesis objectives. Our models successfully learn chemical guidelines such as negative formation energy, charge neutrality, and electronegativity balance while maintaining high chemical diversity and uniqueness. We demonstrate multi-objective RL algorithms that can generate novel compounds with both desirable materials properties (band gap, formation energy, bulk modulus, shear modulus) and synthesis objectives (low sintering and calcination temperatures). We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. We analyze the benefits and drawbacks of the ML approaches tested in this work in the context of accelerated inorganic materials design. This work isolates and evaluates the effects of different RL methodologies to suggest promising, valid compounds of interest by exploring the chemical design space for materials discovery.

实现具有理想性能的新型无机材料的一个主要障碍是在材料性能和合成空间上有效地发现材料。在这项工作中，我们提出并比较了两种新颖的强化学习 （RL） 方法，用于反向无机氧化物材料设计，以使用特定的特性和合成目标来靶向有前途的化合物。我们的模型成功地学习了化学准则，如负形成能、电荷中性和电负性平衡，同时保持了高度的化学多样性和独特性。我们展示了多目标 RL 算法，该算法可以生成具有所需材料特性（带隙、形成能、体积模量、剪切模量）和合成目标（低烧结和煅烧温度）的新型化合物。我们应用基于模板的晶体结构预测，为机器学习 （ML） 算法确定的目标无机成分提供可行的晶体结构匹配建议，以突出已识别目标成分的合理性。我们分析了在加速无机材料设计的背景下，这项工作中测试的 ML 方法的优缺点。这项工作分离并评估了不同 RL 方法的效果，通过探索材料发现的化学设计空间来提出有前途、有效的目标化合物。