Efficient exploration of high-Tc superconductors by a gradient-based composition design

• URL: http://arxiv.org/abs/2403.13627v1
• Date: Wed, 20 Mar 2024 14:23:17 GMT
• Title: Efficient exploration of high-Tc superconductors by a gradient-based composition design
• Authors: Akihiro Fujii, Koji Shimizu, Satoshi Watanabe,
• Abstract summary: We propose a material design method via gradient-based optimization on compositions. It overcomes the limitations of traditional methods: exhaustive database searches and conditional generation models. This method is versatile and significantly advances material design by enabling efficient, extensive searches and adaptability to new constraints.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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