Fusing Direct and Indirect Measurements Through Multi-Fidelity Learning For Accelerated Electrocaloric Materials Discovery.

Bo Wang, Pengfei Dang, Yuan Tian, Zhengwang He, Wei Gu, Lixue Zhang, Turab Lookman, Yumei Zhou, Dezhen Xue

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Bo WangState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Pengfei DangState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Yuan TianCollege of Materials Science and Engineering, Chongqing University, Chongqing, China.

Zhengwang HeState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Wei GuState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Lixue ZhangState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Turab LookmanState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.

Yumei ZhouState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0009-0009-5271-3233

Dezhen XueState Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0000-0001-6132-1236

Funding

National Natural Science Foundation of China 524B2165National Natural Science Foundation of China 52541106National Natural Science Foundation of China 52573255National Natural Science Foundation of China 92570301

6 · The paper itself

Abstract

The data-driven discovery of high-performance electrocaloric (EC) materials is challenged by sparse direct measurements and systematic discrepancies between direct and indirect measurements, resulting in heterogeneous datasets with varying fidelity levels. Here, a co-kriging-based multi-fidelity learning framework is developed to integrate these data sources and construct a robust predictive model for

Indexed as

active learningdata‐driven materials discoveryelectrocaloric materialsferroelectric ceramicsmulti‐fidelity learning

Identifiers

PMID42693574

DOI10.1002/advs.77152

PMCPMC13542402

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.