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In nature, structural and functional materials often form programmed three-dimensional (3D) assembly to perform daily functions, inspiring researchers to engineer multifunctional 3D structures. Despite much progress, a general method to fabricate and assemble a broad range of materials into functional 3D objects remains limited. Herein, to bridge the gap, we demonstrate a freeform multimaterial assembly process (FMAP) by integrating 3D printing (fused filament fabrication (FFF), direct ink writing (DIW)) with freeform laser induction (FLI). 3D printing performs the 3D structural material assembly, while FLI fabricates the functional materials in predesigned 3D space by synergistic, programmed control. This paper showcases the versatility of FMAP in spatially fabricating various types of functional materials (metals, semiconductors) within 3D structures for applications in crossbar circuits for LED display, a strain sensor for multifunctional springs and haptic manipulators, a UV sensor, a 3D electromagnet as a magnetic encoder, capacitive sensors for human machine interface, and an integrated microfluidic reactor with a built-in Joule heater for nanomaterial synthesis. This success underscores the potential of FMAP to redefine 3D printing and FLI for programmed multimaterial assembly.

 

In nature, structural and functional materials often form programmed three-dimensional (3D) assembly to perform daily functions, inspiring researchers to engineer multifunctional 3D structures. Despite much progress, a general method to fabricate and assemble a broad range of materials into functional3D objects remains limited. Herein, to bridge the gap, we demonstrate a freeform multimaterial assembly process (FMAP) by integrating 3D printing(fused filament fabrication (FFF), direct ink writing (DIW)) with freeform laser induction (FLI). 3D printing performs the 3D structural material assembly, while FLI fabricates the functional materials in predesigned 3D space by synergistic, programmed control. This paper showcases the versatility of FMAP in spatially fabricating various types of functional materials (metals, semiconductors) within 3D structures for applications in crossbar circuits for LED display, a strain sensor for multifunctional springs and haptic manipulators, a UV sensor, a 3D electromagnet as a magnetic encoder, capacitive sensors for human machine interface, and an integrated microfluidic reactor with a built-in Joule heater for nanomaterial synthesis. This success under-scores the potential of FMAP to redefine 3D printing and FLI for programmed multimaterial assembly. 

Rapid analysis of materials characterization spectra is pivotal for preventing the accumulation of unwieldy datasets, thus accelerating subsequent decision-making. However, current methods heavily rely on experience and domain knowledge, which not only proves tedious but also makes it hard to keep up with the pace of data acquisition. In this context, we introduce a transferable Vision Transformer (ViT) model for the identification of materials from their spectra, including XRD and FTIR. First, an optimal ViT model was trained to predict metal organic frameworks (MOFs) from their XRD spectra. It attains prediction accuracies of 70%, 93%, and 94.9% for Top-1, Top-3, and Top-5, respectively, and a shorter training time of 269 seconds (∼30% faster) in comparison to a convolutional neural network model. The dimension reduction and attention weight map underline its adeptness at capturing relevant features in the XRD spectra for determining the prediction outcome. Moreover, the model can be transferred to a new one for prediction of organic molecules from their FTIR spectra, attaining remarkable Top-1, Top-3, and Top-5 prediction accuracies of 84%, 94.1%, and 96.7%, respectively. The introduced ViT-based model would set a new avenue for handling diverse types of spectroscopic data, thus expediting the materials characterization processes. 

De novo design of molecules with targeted properties represents a new frontier in molecule development. Despite enormous progress, two main challenges remain: (i) generating novel molecules conditioned on targeted, continuous property values; (ii) obtaining molecules with property values beyond the range in the training data. To tackle these challenges, we propose a reinforced regressional and conditional generative adversarial network (RRCGAN) to generate chemically valid molecules with targeted HOMO–LUMO energy gap (ΔEH–L) as a proof-of-concept study. As validated by density functional theory (DFT) calculation, 75% of the generated molecules have a relative error (RE) of <20% of the targeted ΔEH–L values. To bias the generation toward the ΔEH–L values beyond the range of the original training molecules, transfer learning was applied to iteratively retrain the RRCGAN model. After just two iterations, the mean ΔEH–L of the generated molecules increases to 8.7 eV from the mean value of 5.9 eV shown in the initial training dataset. Qualitative and quantitative analyses reveal that the model has successfully captured the underlying structure–property relationship, which agrees well with the established physical and chemical rules. These results present a trustworthy, purely data-driven methodology for the highly efficient generation of novel molecules with different targeted properties. 

The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e. , high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers. 

Responsive soft materials capable of exhibiting various three-dimensional (3D) shapes under the same stimulus are desirable for promising applications including adaptive and reconfigurable soft robots. Here, we report a laser rewritable magnetic composite film, whose responsive shape-morphing behaviors induced by a magnetic field can be digitally and repeatedly reprogrammed by a facile method of direct laser writing. The composite film is made from an elastomer and magnetic particles encapsulated by a phase change polymer. Once the phase change polymer is temporarily melted by transient laser heating, the orientation of the magnetic particles can be re-aligned upon change of a programming magnetic field. By the digital laser writing on selective areas, magnetic anisotropies can be encoded in the composite film and then reprogrammed by repeating the same procedure, thus leading to multimodal 3D shaping under the same actuation magnetic field. Furthermore, we demonstrated their functional applications in assembling multistate 3D structures driven by the magnetic force-induced buckling, fabricating multistate electrical switches for electronics, and constructing reconfigurable magnetic soft robots with locomotion modes of peristalsis, crawling, and rolling.