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Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber–Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well‐established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state‐of‐the‐art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three‐step H2O‐CL as an example, a new metric is introduced to determine bicationic redox pairs that are “cooperatively enhanced” compared to their corresponding monocationic counterparts. It is found that bicationic compounds containing a combination of alkali/alkaline‐earth metals and transition metal (TM)/post‐TM/metalloid elements are compounds that are particularly promising in this respect. The reaction thermodynamics of more than half a million bicationic solids are computationally screened to systematically access their potential in driving chemical looping ammonia synthesis. It is found that many candidates with new chemical compositions can facilitate these processes spontaneously. A “cooperativity” metric is further proposed to provide critical insights on whether these complex solids outperform their corresponding monocationic counterparts.

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.

Highlights The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed. The achievements of various ML algorithms in predicting different performances of the battery management system are discussed. Future challenges and perspectives of artificial intelligence in solid-state battery are discussed. Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.

The competition between the electron-hole Coulomb attraction and the 3D dielectric screening dictates the optical properties of layered semiconductors. In low-dimensional materials, the equilibrium dielectric environment can be significantly altered by the ultrafast excitation of photo-carriers, leading to renormalized band gap and exciton binding energies. Recently, black phosphorus emerged as a 2D material with strongly layer-dependent electronic properties. Here, we resolve the response of bulk black phosphorus to mid-infrared pulses tuned across the band gap. We find that, while above-gap excitation leads to a broadband light-induced transparency, sub-gap pulses drive an anomalous response, peaked at the single-layer exciton resonance. With the support of DFT calculations, we tentatively ascribe this experimental evidence to a non-adiabatic modification of the screening environment. Our work heralds the non-adiabatic optical manipulation of the electronic properties of 2D materials, which is of great relevance for the engineering of versatile van der Waals materials. Here, the authors investigate the optical response of bulk black phosphorus to mid-infrared pulses, and find that while above-gap excitation leads to a broadband light-induced transparency, sub-gap pulses drive an anomalous response, peaked at the single-layer exciton resonance.

Screening for potential new materials with experimental and theoretical methods has led to the discovery of many promising candidate materials for p-type transparent conducting oxides. It is difficult to reliably assess a good p-type transparent conducting oxide (TCO) from limited information available at an early experimental stage. In this paper we discuss the influence of sample thickness on simple transmission measurements and how the sample thickness can skew the commonly used figure of merit of TCOs and their estimated band gap. We discuss this using copper-deficient CuCrO 2 as an example, as it was already shown to be a good p-type TCO grown at low temperatures. We outline a modified figure of merit reducing thickness-dependent errors, as well as how modern ab initio screening methods can be used to augment experimental methods to assess new materials for potential applications as p-type TCOs, p-channel transparent thin film transistors, and selective contacts in solar cells.

Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers encoding correlations in time and space, we were able to predict the evolution of the dominant component of the stress field given only the initial microstructure and external loading. In comparison to our recent work, we were able to predict not only the spatial average of the stress response but the evolution of the field itself. We show that the stress fields and their rates are in good agreement with the two dimensional crystal plasticity data and have no visible artifacts. Furthermore the distribution of stress throughout the elastic to fully plastic transition match the truth provided by held out crystal plasticity data. Lastly we demonstrate the efficacy of the trained model in material characterization and optimization tasks.

The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising photovoltaic candidates. A dataset of 1053 double perovskites was extracted from the Materials Project database, with 50 feature descriptors generated. Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. Four algorithms, including gradient-boosting regression (GBR), random forest regression (RFR), LightGBM, and XGBoost, were evaluated, with XGBoost demonstrating the best performance (R2 = 0.934 for bandgap, R2 = 0.959 for formation energy; MAE = 0.211 eV and 0.013 eV/atom). The SHAP (Shapley Additive Explanations) analysis revealed that the X-site electron affinity positively influences the bandgap, while the B″-site first and third ionization energies exhibit strong negative effects. Formation energy is primarily governed by the X-site first ionization energy and the electronegativities of the B′ and B″ sites. To identify optimal photovoltaic materials, 4573 charge-neutral double perovskites were generated via elemental substitution, with 2054 structurally stable candidates selected using tolerance and octahedral factors. The XGBoost model predicted bandgaps, yielding 99 lead-free double perovskites with ideal bandgaps (1.3~1.4 eV). Among them, four candidates are known compounds according to the Materials Project database, namely Ca2NbFeO6, Ca2FeTaO6, La2CrFeO6, and Cs2YAgBr6, while the remaining 95 candidate perovskites are unknown compounds. Notably, X-site elements (Se, S, O, C) and B″-site elements (Pd, Ir, Fe, Ta, Pt, Cu) favor narrow bandgap formation. These findings provide valuable guidance for designing high-performance, non-toxic photovoltaic materials.

Polymeric materials undergo photo-thermal-oxidative aging, which adversely affects their appearance and functional properties. The aging mechanisms involve complex chemical processes, including molecular chain scission, crosslinking, and oxidation of functional groups. Fourier-transform infrared (FTIR) spectroscopy, capable of precisely detecting changes in characteristic functional groups and chemical bonds, has become a vital tool for investigating polymer aging mechanisms; however, its application in the field of automotive components remains limited. In this study, four representative automotive exterior polymer materials from different manufacturers were subjected to xenon-arc accelerated aging to simulate service conditions. Combined analyses using FTIR spectroscopy, colorimetric measurements, and gloss assessments were systematically employed to elucidate the structural evolution during photo-oxidative aging. The results demonstrated significant redox reactions, alterations in functional groups, and the formation of new species, with molecular structural changes closely correlated to macroscopic performance degradation. This study confirms that FTIR analysis provides an effective approach for revealing aging mechanisms and guiding material selection, thereby offering important insights for weatherability evaluation and material development in automotive components.

Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation to their bonds. As a fundamental property, OS has been widely used in charge‐neutrality verification, crystal structure determination, and reaction estimation. Currently, only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition‐based oxidation state prediction still remains elusive so far, which has significant implications for the discovery of new materials for which the structures have not been determined. This work proposes a novel deep learning‐based BERT transformer language model BERTOS for predicting the oxidation states for all elements of inorganic compounds given only their chemical composition. This model achieves 96.82% accuracy for all‐element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61% accuracy for oxide materials. It is also demonstrated how it can be used to conduct large‐scale screening of hypothetical material compositions for materials discovery.

The substantial exploration of novel lead-free, non-toxic double perovskite halide (DPH) materials with suitable band gaps and high stability is desirable for modern perspective applications. In this work, we report density functional theory (DFT) calculations to screen the structural, dynamical, and mechanical stability, tuning the band gap, and optical properties of inorganic Cs 2 RbABr 6 (A = Tl and Bi) DPH materials using WIEN2K quantum mechanical simulation code. The computation of tolerance factor, octahedral factor, and phonon band structures shows that the interested materials possess dynamic and structural stability in cubic symmetry. The computed cubic elastic constants C ij , bulk modulus B , Young’s modulus Y , shear modulus G , Cauchy pressure C P , Pugh ratio B G , anisotropy factor A , and Poisson ratio ʋ confirm that both ternary Cs 2 RbTlBr 6 and Cs 2 RbBiBr 6 DPH materials are anisotropic, ductile, tough, and mechanically stable. Electronic properties depict that the tuning of the direct band gap (2.26 eV) from Γ–Γ in the Cs 2 RbTlBr 6 to the direct band gap of 4.09 eV from L–L occurs in Cs 2 RbBiBr 6 when the transition element Thallium (Tl) is replaced by the non-metallic Bismuth (Bi) element. The electronic properties show that Cs 2 RbTlBr 6 is a semiconductor and Cs 2 RbBiBr 6 is an insulator. Our investigations of optical properties show that inorganic Cs 2 RbABr 6 (A = Tl and Bi) DPH possess better absorption coefficient, optical conductivity, and refractive index in the visible light spectrum, and thus Cs 2 RbTlBr 6 and Cs 2 RbBiBr 6 are proposed to be efficient materials for photosensitive and renewable energy applications.