AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening
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arXiv
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| Format: | Preprint |
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2025
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| author | Gashmard, H. Shakeripour, H. Alaei, M. |
| author_facet | Gashmard, H. Shakeripour, H. Alaei, M. |
| contents | Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and advanced machine learning models, we demonstrate that Functional Convolutional Neural Networks (CNNs) ensure accurate extrapolation of potential compounds. Leveraging extensive datasets from the ICSD, the Materials Project and COD, our implemented HTS pipeline classifies superconductors, with CNN and long short-term memory (LSTM) models predicting Tc and their foundational elements. We address the scarcity of non-superconducting material data by compiling a dataset of 53,196 non-superconducting materials (DataG Non-Sc) and introduce a novel neural network architecture using Functional API for improved prediction, offering a powerful tool for future superconductor discovery. Our findings underscore the transformative potential of combining HTS with AI-driven models in advancing HTSC materials, highlighting Pu and H elements (with Tc nearly 100 K) as significant predictors of high-temperature superconductivity, suggesting their role as a crucial gene in these materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening Gashmard, H. Shakeripour, H. Alaei, M. Strongly Correlated Electrons Superconductivity Inspired by nature, this study employs the Materials Genome Initiative to identify key components of HTSC superconductors. Integrating AI with high-throughput screening, we uncover crucial superconducting "genes". Through HTS techniques and advanced machine learning models, we demonstrate that Functional Convolutional Neural Networks (CNNs) ensure accurate extrapolation of potential compounds. Leveraging extensive datasets from the ICSD, the Materials Project and COD, our implemented HTS pipeline classifies superconductors, with CNN and long short-term memory (LSTM) models predicting Tc and their foundational elements. We address the scarcity of non-superconducting material data by compiling a dataset of 53,196 non-superconducting materials (DataG Non-Sc) and introduce a novel neural network architecture using Functional API for improved prediction, offering a powerful tool for future superconductor discovery. Our findings underscore the transformative potential of combining HTS with AI-driven models in advancing HTSC materials, highlighting Pu and H elements (with Tc nearly 100 K) as significant predictors of high-temperature superconductivity, suggesting their role as a crucial gene in these materials. |
| title | AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening |
| topic | Strongly Correlated Electrons Superconductivity |
| url | https://arxiv.org/abs/2511.03865 |