Cold metals possess an intrinsic energy gap located close to the Fermi level, which enables cold-carrier injection for steep-slope transistors and is therefore promising for low-power electronics. High-throughput screening has revealed 252 three-dimensional (3D) cold metals in the Materials Project database, but database searches are inherently limited to known compounds. Here we present an inverse-design workflow that generates 3D cold metals using MatterGPT, a conditional autoregressive Transformer trained on SLICES, an invertible and symmetry-invariant crystal representation. We curate a training set of 26,309 metallic structures labeled with energy above hull and a unified band-edge distance descriptor that merges p-type and n-type features to address severe label imbalance. Property-conditioned generation targeting thermodynamic stability and 50–500  meV band-edge distances produces 148,506 unique candidates; 92.1 % are reconstructed into 3D structures and down-selected by symmetry, uniqueness and novelty filters, followed by high-throughput DFT validation. We identify 257 cold metals absent from the Materials Project database, all within the targeted 50–500  meV window. First-principles phonon, electronic-structure, and work-function calculations for representative candidates confirm dynamical stability and contact-relevant work functions. Our results demonstrate that SLICES-enabled generative transformers can expand the chemical space of cold metals beyond high-throughput screening, providing a route to low-power electronic materials discovery.

Inverse design of solid-state materials with desired properties remains a central challenge in materials science, requiring exploration of vast chemical spaces containing potentially 10100 possible structures. Current generative approaches face limitations in computational efficiency, multi-property targeting precision and mechanistic interpretability. Here, we introduce MatterGPT, an autoregressive Transformer-decoder architecture that leverages SLICES (Simplified Line-Input Crystal-Encoding System) representation to generate novel crystals through conditional next-token prediction. Trained on 306,533 crystal structures, MatterGPT achieves > 99% structural validity, > 99% structural uniqueness and > 50% novelty rates while targeting both specific lattice-insensitive and lattice-sensitive properties. Critically, MatterGPT enables direct multi-property generation without post-generation filtering. Interpretability analysis reveals clear property-guided generation mechanisms and systematic chemical space exploration. The comprehensive open-source release, including MatterGPT Hub integration platform, establishes sequence-based autoregressive generation as a computationally efficient and interpretable paradigm for inverse crystal design, accelerating materials discovery across energy storage, electronics, and functional applications.

Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science. Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficiency, architectural constraints and restricted open-source availability. The representation of crystal structures using the SLICES (Simplified Line-Input Crystal-Encoding System) notation as a string of characters enables the use of state-of-the-art natural language processing models, such as Transformers, for crystal design. Drawing inspiration from the success of GPT models in generating coherent text, we trained a generative Transformer on the next-token prediction task to generate solid-state materials with targeted properties. We demonstrate MatterGPT's capability to generate de novo crystal structures with targeted single properties, including both lattice-insensitive (formation energy) and lattice-sensitive (band gap) properties. Furthermore, we extend MatterGPT to simultaneously target multiple properties, addressing the complex challenge of multi-objective inverse design of crystals. Our approach showcases high validity, uniqueness, and novelty in generated structures, as well as the ability to generate materials with properties beyond the training data distribution. This work represents a significant step forward in computational materials discovery, offering a powerful and open tool for designing materials with tailored properties for various applications in energy, electronics, and beyond.

The zinc-air batteries (ZABs) are regarded as the most potential energy storage device for the next generation. However, the zinc anode passivation and hydrogen evolution reaction (HER) in alkaline electrolyte situations inhibit the zinc plate working efficiency, which needs to improve zinc solvation and better electrolyte strategy. In this work, we propose a design of new electrolyte by using a polydentate ligand to stabilize the zinc ion divorced from the zinc anode. The formation of the passivation film is suppressed greatly, compared to the traditional electrolyte. The characterization result presents that the quantity of the passivation film is reduced to nearly 33% of pure KOH result. Besides, triethanolamine (TEA) as an anionic surfactant inhibits the HER effect to improve the efficiency of the zinc anode. The discharging and recycling test indicates that the specific capacity of the battery with the effect of TEA is improved to nearly 85 mA h/cm2 compared to 0.21 mA h/cm2 in 0.5 mol/L KOH, which is 350 times the result of the blank group. The electrochemical analysis results also indicate that zinc anode self-corrosion is palliated. With density function theory, calculation results prove the new complex existence and structure in electrolytes by the data of the molecular orbital (highest occupied molecular orbital-lowest unoccupied molecular orbital). A new theory of multi-dentate ligand inhibiting passivation is elicited and provides a new direction for ZABs' electrolyte design.

Perovskite materials have recently attracted extensive attention since tailoring their chemical compositions has led to remarkable activity toward oxygen reduction reaction. However, the desired electrocatalytic activity is limited by the morphological effect, and lack of methods to achieve large surface area. Herein we report an effective strategy to synthesize three-dimensional ordered macroporous (3DOM) perovskite oxides, where La0.75Sr0.25MnO3 (3DOM LSMO) displays excellent ORR activity and durability with considerable specific surface area (43.1 m2 g-1). The electrochemical results exhibit that the electron transferred numbers (n) is close to 4 and the H2O2 yield (% H2O2) is as low as 10% for 3DOM LSMO, which mainly attributes to comprehensive effect of the reduced Mn valence state, the increased specific surface, and the exposed high activity crystal planes. First-principles study confirms that the lowest overpotential obtained by LSMO is in good agreement with the experimental results. Our work demonstrates perovskite oxides with larger surface area could be advanced oxygen catalysts with wide applications.

Aluminum-air batteries are promising electronic power sources because of their low cost and high energy density. However, traditional aluminum-air batteries are greatly restricted from being used in the field of flexible electronics due to the rigid battery structure, and the irreversible corrosion of the anode by the alkaline electrolyte, which greatly reduces the battery life. To address these issues, a three-dimensional dual-network interpenetrating structure PVA/LiCl/PEO composite gel polymer electrolyte (GPE) is proposed. The gel polymer electrolyte exhibits good flexibility and high ionic conductivity (σ = 6.51 × 10-3 S cm-1) at room temperature. Meanwhile, benefiting from the high-performance GPE, an assembled aluminum-air coin cell shows a highest discharge voltage of 0.73 V and a peak power density (P max) of 3.31 mW cm-2. The Al specific capacity is as high as 735.2 mA h g-1. A flexible aluminum-air battery assembled using the GPE also performed stably in flat, bent, and folded states. This paper provides a cost-effective and feasible way to fabricate a composite gel polymer electrolyte with high performance for use in flexible aluminum-air batteries, suitable for a variety of energy-related devices.

In this study, a silica gel/liquid nanoporous energy absorption system (NEAS) is developed and its infiltration behaviors are experimentally studied. The relation between the compressive pressure and volume of the silica gel/liquid NEAS is theoretically derived by assuming the accumulative infiltration of liquid to silica gel from large pores to small pores under quasi-static compression, which agrees with the experimental results very well. Besides, the infiltration behaviors of silica gel/liquid NEAS can be further tuned by sodium chloride (NaCl) concentration and the infiltration pressure increases almost linearly with NaCl concentration. This work is the first study to quantitatively correlate the compressive pressure and nanoporous structures of silica gel during infiltration. The results presented herein show that not only the infiltration pressure of silica gel/liquid NEAS can be adjusted by NaCl concentration, but also the compressive pressure-volume curve can be tuned by the distribution of the size of the nanopore in silica gel, which may be beneficial for some applications of NEAS with special requirements of stress-strain relation, such as personal protection, vibration absorber, and volume memory materials.

Pimple is one of the most common skin diseases for humans, whose growth cause pain yet the corresponding mechanical analysis is lacking. A finite element model is developed to quantify the deformation field with the expansion of follicle, and then the mechanical stimulus is related to the sensation of pain during the development of pimple. Parametric studies show the dependence of mechanical stimulus and pain level on the pimple-surrounded structures, follicle depth and mechanical properties of the epidermis. The findings in this paper may provide useful insights on prevention or pain mitigation of pimples, as well as those related to other tissue growth and respective cosmetic concerns.

The ballistic protection for body armor usually requires both of high strength and high energy mitigation. In this work, we introduce and evaluate a new kind of body armor, i.e. a hybrid panel of ultra-high molecular weight polyethylene (UHMWPE) fabrics and soft energy absorption materials and structures (EAMS), by combing the advantages of bullet-proof and energy absorption of the respective material structures. A combined experimental and numerical study is conducted to evaluate the ballistic performance of the UHMWPE fabrics/EAMS hybrid panel. The resulting back-face signature (BFS) values of the hybrid panel are reduced by 6–17%, compared to the pure UHMWPE fabrics panel with the same areal density. If the EAMS is simply superimposed onto the UHMWPE fabrics, the reduction of BFS can be 50% or more with respect to the pure UHMWPE one. The effects of the geometrical factors of EAMS and mass ratio of UHMWPE fabrics to EAMS on the BFS values are studied using comprehensive finite element method (FEM) analyses. The strategies for optimal design of the UHMWPE fabrics/EAMS composite armor are proposed. The results presented herein shed useful insights for the design for high performance and energy mitigating body armors.

In this work, the crush behaviors of hemi-ellipsoid polyvinyl chloride cellular structures (PVC-CS) with liquid filler (i.e. water) are experimentally studied. The load capacity and energy absorption characteristics of the liquid filled PVC-CS are significantly enhanced due to the additional support of liquid filler and the lateral expansion of PVC-CS, compared with the hollow PVC-CS without sealing. Besides, both the hollow and liquid filled PVC-CS are flexible and their deformations are fully reversible upon compression (for compressive strain up to 0.8) owing to the superelasticity of PVC. The effects of PVC hardness and strain rate on the crush behaviors of hollow and liquid filled PVC-CS are also explored. A homogeneous material model is developed by incorporating viscosity into the hyperelastic Yeoh model, which can describe the impacting behaviors of the liquid filled PVC-CS. The results presented in this work provide guidelines for designing and engineering high-performance energy absorption structures that are resilient, flexible, and of high energy absorption density.

Pimple is one of the most common skin diseases for humans. The mechanical modeling of pimple growth is very limited. A finite element model is developed to quantify the deformation field with the expansion of follicle, and then the mechanical stimulus is related to the sensation of pain during the development of pimple. Through these models, parametric studies show the dependence of mechanical stimulus and pain level on the pimple-surrounded structures, follicle depth and mechanical properties of the epidermis. The findings in this paper may provide useful insights on prevention or pain relief of pimples, as well as those related to cosmetics and other tissue growth.

As a promising candidate for energy absorption and resilient system, a polymer encased sand structure is studied experimentally. The polyurethane (PU) cellular structures are consisted of periodically arranged hollow truncated hemi-ellipsoids with sand particles filled inside. The resilience of PU and dissipation of sand are combined to construct a high performance energy absorption material and structure (EAMS) which exhibit superelasticity with reversible compression strain up to 0.9 and have recoverable energy absorption capability of about 3.4 MJ/m3 per loading cycle. The compressive stress of the sand filled PU cellular structures is also significantly enhanced (thanks for the sand filler) for compressive strain larger than 0.4. The sand filled PU cellular structures are soft and flexible to stretch, bend and twist, thus compatible for personnel protection. The results presented in this work provide guidelines for designing and engineering high performance EAMS that are resilient, flexible, and of high energy absorption density.