  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.
