Generative design of inorganic materials
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918473802711040 |
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| author | Recatala-Gomez, Jose Dai, Haiwen Ruiming, Zhu Kazeev, Nikita Wei, Nong Wu, Gang Koperski, Maciej Leong, Tan Teck Ustyuzhanin, Andrey Ceder, Gerbrand Novoselov, Kostya Hippalgaonkar, Kedar |
| author_facet | Recatala-Gomez, Jose Dai, Haiwen Ruiming, Zhu Kazeev, Nikita Wei, Nong Wu, Gang Koperski, Maciej Leong, Tan Teck Ustyuzhanin, Andrey Ceder, Gerbrand Novoselov, Kostya Hippalgaonkar, Kedar |
| contents | Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are efficient at finding materials with desired properties, via multi-modal learning using multiscale data. This perspective examines the landscape of generative design for inorganic materials and discusses the integration of multi-modal learning with high-throughput experimental validation. We contextualize these challenges through the lens of a generative design framework as a unified approach to address the data-driven inverse design of functional materials. The central idea of the framework is constructed around a foundation AI model for inorganic materials interlinked deeply with various property databases and high-throughput experiments via a machine learning driven closed loop, which enables the framework to solve key challenges in functional materials. We argue that domain-specific implementations of such integrated workflows represent a promising pathway toward the unresolved challenge of data-driven inverse design for atom-engineered inorganic functional materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14082 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative design of inorganic materials Recatala-Gomez, Jose Dai, Haiwen Ruiming, Zhu Kazeev, Nikita Wei, Nong Wu, Gang Koperski, Maciej Leong, Tan Teck Ustyuzhanin, Andrey Ceder, Gerbrand Novoselov, Kostya Hippalgaonkar, Kedar Materials Science Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are efficient at finding materials with desired properties, via multi-modal learning using multiscale data. This perspective examines the landscape of generative design for inorganic materials and discusses the integration of multi-modal learning with high-throughput experimental validation. We contextualize these challenges through the lens of a generative design framework as a unified approach to address the data-driven inverse design of functional materials. The central idea of the framework is constructed around a foundation AI model for inorganic materials interlinked deeply with various property databases and high-throughput experiments via a machine learning driven closed loop, which enables the framework to solve key challenges in functional materials. We argue that domain-specific implementations of such integrated workflows represent a promising pathway toward the unresolved challenge of data-driven inverse design for atom-engineered inorganic functional materials. |
| title | Generative design of inorganic materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2604.14082 |