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Computational Discovery of Novel Sulfide Lithium Superionic Conductors via a Structural-Feature-Constrained Generative Model
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DOI：10.1002/aenm.71368.png)
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Abstract
En 中文
Recent advances in generative models have opened new avenues for discovering novel materials with desired properties by steering generation toward property constraints. However, for properties that are scarce, inconsistent across different sources, or difficult to obtain, such as Li ionic conductivity or activation energy, directly incorporating property constraints into generative models remains challenging. To address this challenge, a generative strategy is proposed based on structural feature constraints rather than property constraints, utilizing structural features strongly correlated with target properties to steer the generation process. Focusing on lithium sulfides, a simple descriptor is constructed based on the average anion-anion coordination number to identify the body-centered cubic anion framework, a structural feature known to facilitate fast Li ionic diffusion. By incorporating this structural feature constraint into a generative model, a set of ternary lithium sulfides with body-centered cubic anion framework is generated. Among them, 21 materials exhibit room-temperature ionic conductivities exceeding 0.1 mS/cm according to machine learning molecular dynamics simulations, confirming their superionic behavior. This structural-feature-constrained generative model not only accelerates the discovery of lithium superionic conductors but also provides a general framework and universal strategy for the inverse design of materials with desired properties.
Keywords:
generative model
lithium sulfides
structure-property relationship
superionic conductor
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