biostudies-literatureJiao PNIBIB NIH HHSNational Key Research and Development Program of ChinaZhejiang UniversityNational Institute of Biomedical Imaging and BioengineeringNational Science Foundation2991-3001https://www.ebi.ac.uk/biostudies/studies/S-EPMC11744508biostudies-literatureUnknown17(2)Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo-metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery. We begin by generating a large database of corrugated thermo-metamaterials with various cell structures and bending stiffnesses. An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model alloACS applied materials & interfacesArtificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants.PMC117445082023YFC30081001R21EB034457-01A1R21 EB034457CMMI-2235494Wu ZAgarwal NJang DJiao PZhang CAlavi AHWang JMeng WfalseArtificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants.Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo-metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery. We begin by generating a large database of corrugated thermo-metamaterials with various cell structures and bending stiffnesses. An artificial intelligence inverse design model is subsequently developed by integrating an evolutionary algorithm with a neural network. This model allo2025-01-01T00:00:00Z2025 Jan2025-04-21T16:46:45.032Z2025-04-05T09:31:00.63ZS-EPMC117445083974603310.1021/acsami.4c17625