Evidence map›Paper›PMID 41603229›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Customizing Tactile Sensors via Machine Learning-Driven Inverse Design.

Baocheng Wang, Depeng Kong, Zhiao He, Jikai Liang, Yuyao Lu, Zikang Deng, Honghe Li, Mengke Wang, M Jamal Deen, Zhiqiu Ye and 6 more

Abstract read
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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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Baocheng WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Depeng KongState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Zhiao HeState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Jikai LiangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Yuyao LuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Zikang DengZJU-UIUC Institute, Zhejiang University, Haining, China.
Honghe LiZJU-UIUC Institute, Zhejiang University, Haining, China.
Mengke WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
M Jamal DeenDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada.
Zhiqiu YeInstitute of AI for Industries, Chinese Academy of Sciences, Nanjing, China.
Shuyao ZhouState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Huayong YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Honghao LyuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Jun ChenDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA.
Kaichen XuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Geng YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.ORCID https://orcid.org/0000-0002-8685-5426

Funding

Bellwethers + X Research and Development Plan of Zhejiang Province 2024C04057(CSJ)Bellwethers + X Research and Development Plan of Zhejiang Province 2025C01012Bellwethers + X Research and Development Plan of Zhejiang Province 2026C02A1208China Postdoctoral Science Foundation 2024M762812China Postdoctoral Science Foundation 2025T180371China Postdoctoral Science Foundation GZB20240654Joint Research Project of Sci-Tech Innovation Community in Yangtze River Delta 2023CSJGG1400National Natural Science Foundation of China 52375031National Natural Science Foundation of China 52405038Natural Science Foundation of Zhejiang Province LRG25E050001Priority-Funded Postdoctoral Research Project of Zhejiang Province ZJ2024013
6 · The paper itself

Abstract

Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure-property relationship results in inefficient and suboptimal designs. Here, we present a machine learning-accelerated, multi-objective inverse design methodology for the automatic customization of tactile sensors. At its core is a data-efficient microstructure-property predictor designed to ensure robust accuracy with minimal experimental data. It achieves this by synergistically combining support vector machine-based boundary definition with dual-phase active learning. This predictor then drives a multi-objective inverse design software, enabling real-time, on-demand sensor customization. This methodology not only dramatically enhances the design efficiency but also yields sensors with exceptional characteristics-high sensitivity (1.2 V/kPa), high linearity (R

Indexed as

inverse designmachine learningmicrostructuretactile sensor

Identifiers

PMID41603229
PMCPMC13205881

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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.