Deep reinforcement learning for inverse inorganic materials design
Abstract A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces. In this work, we propose and compare two novel reinforcement learning (RL) approaches to inverse inorganic oxide...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01474-5 |
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