Deep learning-driven intelligent mesoscopic model (DeepMeso): a case study on ferroelectrics.

Run-Lin Liu, Zhong-Hui Shen, Han-Xing Liu, Yang Shen, Ce-Wen Nan

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Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Run-Lin LiuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices, Wuhan University of Technology, Wuhan 430070, China.

Zhong-Hui ShenState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices, Wuhan University of Technology, Wuhan 430070, China.ORCID https://orcid.org/0000-0001-7828-0397

Han-Xing LiuState Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices, Wuhan University of Technology, Wuhan 430070, China.

Yang ShenSchool of Materials Science and Engineering, State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084, China.

Ce-Wen NanSchool of Materials Science and Engineering, State Key Laboratory of New Ceramics and Fine Processing, Tsinghua University, Beijing 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating artificial intelligence with computational methods has emerged as a transformative force in materials research, exemplified by breakthroughs such as DeepH and DeepMD. However, a critical gap persists in multiscale intelligent design: the lack of mesoscopic models capable of bridging microstructural features with macroscopic properties. Here, we develop DeepMeso, a generative deep learning-enabled mesoscopic model, for the intelligent design of complex heterogeneous materials. To exemplify the framework, we instantiate it in ferroelectrics (DeepFerro), where our workflow first integrates a data-driven surrogate model to predict key ferroelectric properties with an accuracy exceeding 99.6%. Then, we implement a 3D generative network to achieve inverse design across both composition and microstructure spaces, directly targeting predefined polarization objectives. When tasked with multi-objective on-demand generation, DeepFerro yields a mean squared error of 0.0497 and an

Indexed as

deep learningferroelectricsinverse designlatent diffusion modelmesoscopic models

Identifiers

PMID42437169

DOI10.1093/nsr/nwag324

PMCPMC13355323

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