An acceleration search method of higher T c superconductors by a machine learning algorithm
作者： Kaname Matsumoto , Tomoya Horide
关键词: Algorithm 、 Throughput (business) 、 Space (mathematics) 、 Acceleration 、 Superconductivity 、 Computer science 、 Machine learning 、 Artificial intelligence 、 Calculation algorithm 、 New materials 、 Power (physics) 、 Scope (computer science) 、 General Engineering 、 General Physics and Astronomy
摘要: We propose a method to efficiently search for superconductors with higher critical temperature T c by machine learning based on superconductor database. The prediction and the new are still difficult problems. With progress of computer power calculation algorithms, possibility finding materials at high throughput is emerging. Using obtained model, scope expanded space multielement that have never been searched, candidates can be synthesized proposed.
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