We propose a material design method via gradient-based optimization on compositions, overcoming the limitations of traditional methods: exhaustive database searches and conditional generation models. It optimizes inputs via backpropagation, aligning the model's output closely with the target property and facilitating the discovery of unlisted materials and precise property determination. Our method is also capable of adaptive optimization under new conditions without retraining. Applying to exploring high-Tc superconductors, we identified potential compositions beyond existing databases and discovered new hydrogen superconductors via conditional optimization. This method is versatile and significantly advances material design by enabling efficient, extensive searches and adaptability to new constraints.

翻译：我们提出了一种基于梯度优化的材料设计方法，通过优化成分来克服传统方法的局限性：即穷举数据库搜索和条件生成模型。该方法通过反向传播优化输入，使模型输出与目标属性紧密对齐，从而促进未收录材料的发现及属性的精确确定。该技术无需重新训练即可在新条件下实现自适应优化。将其应用于高温超导体探索时，我们识别出超越现有数据库的潜在成分，并通过条件优化发现了新型氢基超导体。该方法具有通用性，通过实现高效大范围搜索及对新约束的适应性，显著推进了材料设计领域的发展。