Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

Graphene has been regarded as a potential application material in the field of new energy conversion and storage because of its unique two-dimensional structure and excellent physical and chemical properties. However, traditional graphene preparation methods are complicated in-process and difficult to form patterned structures. In recent years, laser-induced graphene (LIG) technology has received a large amount of attention from scholars and has a wide range of applications in supercapacitors, batteries, sensors, air filters, water treatment, etc. In this paper, we summarized a variety of preparation methods for graphene. The effects of laser processing parameters, laser type, precursor materials, and process atmosphere on the properties of the prepared LIG were reviewed. Then, two strategies for large-scale production of LIG were briefly described. We also discussed the wide applications of LIG in the fields of signal sensing, environmental protection, and energy storage. Finally, we briefly outlined the future trends of this research direction.

An open-cell metal foam has excellent characteristics such as low density, high porosity, high specific surface area, high thermal conductivity, and low mass due to its unique internal three-dimensional network structure. It has gradually become a new material for enhanced heat transfer in industrial equipment, new compact heat exchangers, microelectronic device cooling, etc. This research established a comprehensive three-dimensional structural model of open-cell metal foams utilizing Laguerre–Voronoi tessellations and employed computational fluid dynamics to investigate its flow dynamics and coupled heat transfer performance. By exploring the impact of foam microstructure on flow resistance and heat transfer characteristics, the study provided insights into the overall convective heat transfer performance across a range of foam configurations with varying pore densities and porosities. The findings revealed a direct correlation between convective heat transfer coefficient (h) and pressure drop (ΔP) with increasing Reynolds number (Re), accompanied by notable changes in fluid turbulence kinetic energy (e) and temperature (T), ultimately influencing heat transfer efficiency. Furthermore, the analysis demonstrated that alterations in porosity (ε) and pore density significantly affected unit pressure drop (ΔP/L) and convective heat transfer coefficient (h). This study identified an optimal configuration, highlighting a metal foam with a pore density of 20 PPI and a porosity of 95% as exhibiting superior overall convective heat transfer performance.

Hydrogen energy is viewed as a promising green energy source because of its high energy density, abundant availability, and clean combustion results. Hydrogen storage is the critical link in a hydrogen economy. Using first-principles density functional theory calculations, this work explored the role of B and N in modulating the binding properties of transition metal-modified graphene. The hydrogen storage performance of Sc-, Ti-, and V-modified B-doped graphene was evaluated. Boron doping induces an electron-deficient state, enhancing interactions between transition metals and graphene. Sc, Ti, and V preferentially adsorbed at the carbon ring’s hollow site in B-doped graphene, with their binding energies being 1.87, 1.74, and 1.69 eV higher than those in pure graphene, respectively. These systems can stably adsorb up to 5, 4, and 4 H₂ molecules, with average adsorption energies of −0.528, −0.645, and −0.620 eV/H₂, respectively. The hydrogen adsorption mechanism was dominated by orbital interactions and polarization effects. Among the systems studied, Sc-modified B-doped graphene exhibited superior hydrogen storage characteristics, making it a promising candidate for reversible applications.