The excellent stability and adjustable porosity of thorium-based metal-organic frameworks (Th-MOFs) make them highly promising for the safe and effective storage of radionuclides in nuclear waste management. However, evaluating their effectiveness in capturing radioactive fission gases requires a thorough understanding of host-guest interactions and diffusion kinetics. This study employs a machine learning potential (MLP) trained on density functional theory (DFT) data to investigate the adsorption and diffusion behavior of Kr, Xe, and I2 within Th-MOF. The MLP demonstrates high accuracy in reproducing structural, energetic, and dynamic properties compared to first-principles calculations. The results indicate that Th-MOF provides weak confinement for noble gases (Kr, Xe), characterized by low potential energy barriers and adsorption energy, and high mobility within its channels. In contrast, I2 exhibits exceptionally strong, site-specific binding, resulting in a high potential energy barrier that severely restricts movement and promotes molecular aggregation under high loading conditions. This work establishes a machine learning framework for investigating multiscale adsorption and diffusion in actinide-based MOFs while revealing the distinct adsorption, migration, and aggregation mechanisms of different radioactive fission gases.

Understanding the mechanisms of helium-induced damage in zirconium alloys used in nuclear reactors is essential for ensuring both reactor safety and the longevity of materials. However, atomistic simulation of helium (He) in alpha-zirconium (alpha-Zr) has proven to be challenging due to the limitations of conventional empirical potentials. In this study, we developed a deep-potential (DP) model to explore the diffusion and nucleation behavior of He in alpha-Zr through molecular dynamics simulations. The DP model demonstrates strong consistency with density functional theory (DFT) results in predicting elastic constants, phonon spectra, and thermodynamic properties, significantly outperforming traditional empirical potentials. We found that helium atoms preferentially occupy the basal octahedral (BO) and tetrahedral (T) interstitial sites in alpha-Zr, with a diffusion activation energy of 0.34 eV. Increased concentrations of He in alpha-Zr lead to lattice distortions, which degrade the material's mechanical properties. The process of He bubble aggregation shows characteristics of dynamic equilibrium, and the size of the He clusters in alpha-Zr is positively correlated with temperature.

Ferroelectricity (FE) in two-dimensional (2D) materials holds considerable promise for ultrathin, low-power memory and logic applications, but its advancement remains limited by the scarcity of high-performance candidates and the generally weak out-of-plane polarization (OOP). Here, we demonstrate that metal intercalation provides a general and experimentally feasible strategy to activate and enhance sliding FE in transition metal dichalcogenide (TMD) heterobilayers. Through high-throughput calculations on 5264 pristine and intercalated configurations, systematically derived from accessible TMD monolayers and nine representative non-magnetic metal intercalants (Cu, Ag, Au, Pt, Zn, Cd, Hg, Ga, and In), we identify 234 intercalated systems with switchable OOP, including 190 that exceed the experimentally reported value for the MoS2/WS2 system, with some achieving OOP values up to 37× higher. This represents a 13-fold increase over pristine counterparts. Notably, Pt intercalation exhibits the most pronounced effect, delivering the strongest enhancement of OOP. To enable efficient exploration of this vast configuration space, we further introduce a crystal equivariant graph neural network that accurately predicts OOP directly from atomic structures (R2 = 0.98), including both its magnitude and reversible directionality, thereby bypassing the need for computationally intensive DFT calculations. Together, these results elucidate the mechanistic role of interfacial intercalation in tuning symmetry breaking and interlayer coupling, and establish a scalable, machine learning-accelerated framework for the discovery of next-generation 2D sliding ferroelectrics with enhanced functional performance and broad technological relevance.

Improper disposal of large quantities of radionuclides can lead to environmental pollution. This study has investigated the adsorption and diffusion behaviors of actinyl(VI) cations, uranyl(VI), and plutonyl(VI) on the (010) surfaces of gamma-AlOOH and alpha-FeOOH, both in vacuum and explicit solvation environments, using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. Our results show that actinyl(VI) is likely to be adsorbed on the (010) surfaces of gamma-AlOOH and alpha-FeOOH via the inner-sphere (IS) binding mode, with adsorption energies ranging from-225.39 kJ/mol to-251.22 kJ/mol in a vacuum environment. Hydrogen bonding and van der Waals interactions, alongside ligand interactions, significantly influence the binding mode, as revealed by various chemical bonding analysis methods. The diffusion of actinyl(VI) on the (010) surfaces of gamma-AlOOH and alpha-FeOOH is unlikely due to relatively high energy barriers, ranging from 79.71 kJ/mol to 159.11 kJ/mol. AIMD simulations at ambient temperature demonstrate that actinyl(VI) can be effectively anchored to the (010) surface of alpha-FeOOH in an explicit solvation environment. This study quantifies the mechanisms of actinyl(VI) immobilization on key mineral phases at an atomic scale under environmentally relevant conditions, providing crucial molecular-level insights for predicting the long-term fate of actinides in geochemical environments.

Thorium (Th) is an important actinide element for advanced nuclear energy systems, but it undergoes complex phase transitions during heating. Currently, there is no unified force field that adequately describes its various phases, which poses challenges for atomic-scale simulations. This study addresses these challenges by developing and validating a high-fidelity deep potential (DP) model for two phases of Th, including the face-centered cubic phase (α-Th) and the body-centered cubic phase (β-Th). The DP model demonstrates exceptional accuracy in reproducing the fundamental properties of these two phases, such as equations of state, elastic constants, and phonon dispersion curves. Notably, the DP successfully predicts the temperature-induced solid-solid phase transition at 1651 K and the solid-liquid melting transition at 2056 K, showing excellent agreement with experiments. Extensive molecular dynamics simulations further reveal the evolution of key properties across these phases, including behavior related to vacancy formation energy, abrupt changes in self-diffusion coefficients at transition points, and characteristic structural changes highlighted through radial distribution function analysis. Our work establishes a robust and efficient DP-based simulation framework for Th, providing a powerful tool for future molecular dynamics investigations of Th-based materials under a variety of conditions.

As the reliance on nuclear energy increases, so does the need to address the environmental risks posed by radioactive iodine isotopes, making the development of efficient adsorbents critical. This study systematically investigates the adsorption mechanisms of radioactive iodine on two−dimensional covalent organic frameworks (2D COFs) using a combined approach of density functional theory (DFT) and machine learning (ML). We successfully predicted adsorption energies across a diverse range of COF structures and identified important electronic and structural factors that influence adsorption strength. The random forest model proved to be the most reliable predictor, while equations derived from the sure independence screening and sparsifying operator (SISSO) provided clear and interpretable structure–energy relationships. Our results indicate that strong adsorption is associated with reduced molecular orbital levels and activation of I2 molecule, which is facilitated by orbital hybridization and charge transfer. Additionally, we clarified the layer−dependent effects and the transition from physical to chemical adsorption through detailed analyses of electronic structures and ab initio molecular dynamics (AIMD). These results not only enhance our fundamental understanding of COF–iodine interactions but also establish a predictive framework to accelerate the discovery and design of highly efficient COF–based adsorbents for nuclear waste management.

Actinide-based metal-organic frameworks (An-MOFs) possess unique properties that underscore their significant potential in various fields. In this study, the density functional theory (DFT) calculations, incorporating the Hubbard U correction were utilized to systematically investigate the electronic structure and gas adsorption behavior of 12-coordinate An(IV)-ADC (An = Th and U) frameworks. The optimal Hubbard U values necessary for accurate structural predictions were determined by comparing the experimental lattice parameters of An(IV)-ADC. The Th(IV)-ADC exhibits semiconductor characteristics in PBE+U calculations, although the computed band gaps are smaller than that observed in an experiment. The introduction of the Hubbard U correction has a more pronounced impact on the electronic structure of U(IV)-ADC, and the energy of different magnetic states of U(IV)-ADC heavily dependent on the Ueff values. Chemical bond analysis methods reveal that the interactions between actinide and oxygen (An-O) are predominantly ionic, but also exhibit some partial covalent characteristics. Notably, Th(IV)-ADC demonstrates an exceptional affinity for iodine (I2) molecules compared to other gases such as CH4, CO2, H2, Kr, and Xe. This research enhances our understanding of the fundamental properties of An-MOFs and their potential applications in gas capture.

Abstract Due to the partially filled 4f shell, lanthanide atoms exhibit highly complex energy level structures. Their atomic structure calculations are typically carried out using the Hartree-Fock with relativistic corrections (HFR) method implemented in the Cowan code, with parameters optimized through fitting to experimental energy levels in a semi-empirical manner. However, the fitting procedure for lanthanide atoms is cumbersome and relies heavily on manual adjustments. Based on this, an automated energy level fitting program is developed within the framework of Cowan atomic structure calculations. By automatically selecting experimental data in an iterative manner, the program progressively fits experimental energy levels to update the parameters and evaluates the errors until convergence is achieved. Using this method, the energy levels and Landé g-factors of the Nd I ( Z = 60) atom are calculated. The results are systematically compared with ab initio results, experimental data, and other methods, demonstrating the efficiency and reliability of the present approach. Finally, based on the calculated results, preliminary term assignments are carried out for some energy levels that have not yet been clearly assigned.

Knowledge of thermal transport is fundamental to the development of materials such as low thermal conductivity thermoelectrics, heat-resistant coatings, and refractories. In this work, the lattice thermal conductivity (kl), phonon, heat transport, and electric transport properties in monolayer Mg3SbBi are studied systematically by employing first-principles calculations coupled with the Boltzmann transport equation. The monolayer Mg3SbBi exhibits extremely low kl with 0.16 Wm-1 K-1 at 300 K compared to monolayer Mg3Sb2 with 1.2 Wm-1 K-1, which is attributed to the acoustic phonon softening in monolayer Mg3SbBi by substituting the lighter Sb atoms with the heavy Bi atom. The soft acoustic phonon modes primarily arise from the weakness of the ionic bonds (Mg2-Sb, Mg3-Sb, and Mg3-Bi bonds), which reduces the phonon group velocity and enhances scattering, and thus leads to the lower kl. Besides, the maximum thermoelectric figures of merit (ZTmax) of n-type monolayer Mg3SbBi at 800 K is 0.21, which is higher than that of the similar binary monolayer Mg3Sb2 and Mg3Bi2. The study unveils that soft phonon modes can be constructed by tailoring chemical bonds to decrease kl, which provides critical guidance for searching for high-performance advanced thermoelectric and novel low thermal conductivity materials.

This review summarizes recent advances in COF-based separator modifiers for LSBs, covering synthesis, linkage chemistry, pore engineering, functional groups, ionic/conductive COFs, and composites.

The integration of artificial intelligence (AI) with materials science is driving a paradigm shift in how functional materials are discovered and designed. In this work, we present an AI-Agent platform that leverages large language model-driven reasoning to assist users in designing and executing computational workflows for materials research. Rather than relying on rigid pipelines, the Agent interprets natural language prompts, dynamically assembles task-specific workflows from existing simulation tools, and executes calculations accordingly. To illustrate its capabilities, we show two representative cases: (i) a goal-driven electronic structure calculation for periodic monolayer transition metal dichalcogenides, and (ii) an inverse design of battery electrolyte additives based on user-defined targets for molecular weight and frontier orbital energies. These examples illustrate the Agent’s capacity to translate high-level design intent into coordinated multi-tool operations, thereby streamlining complex workflows and lowering the entry barrier for non-expert users. As AI continues to advance, the Agent is poised to become an increasingly valuable partner in materials research, enhancing efficiency and improving design quality, and enabling broader access to materials discovery.

The research aims to predict the thermodynamic stability of rare-earth compounds using machine learning (ML) models, providing crucial data support for advanced materials design and facilitating the discovery of new rare-earth compounds. In terms of methods, this study is based on a dataset consisting of 280,569 compounds. The formation energies of these compounds were obtained through density functional theory (DFT) calculations. A system of 145 feature descriptors was constructed, covering stoichiometric properties, statistical properties of elements, electronic structure properties, and properties of ionic compounds, to comprehensively describe the characteristics of rare-earth compounds. Two ML models, random forest (RF) and neural network (NN), were selected to perform classification and regression tasks respectively. The 5-fold cross-validation was used to improve the reliability of the models. The min-max scaling technique was applied for data preprocessing, and an ensemble learning architecture was constructed to address the limitations of single model. In the classification task, the RF and NN algorithms performed remarkably well. With 5-fold cross-validation, the accuracy reached approximately 0.97, and the F1 score was around 0.98, enabling the precise classification of compounds into stable or unstable categories. In the regression task, the mean absolute errors (MAE) of the formation energy predictions by the RF and NN models were 0.055 eV/atom and 0.071 eV/atom, respectively. This indicates that the model predictions are highly accurate and can, to a certain extent, replace complete DFT calculations. In the prediction analysis of systems outside the test set, six representative components were selected from the Materials Project database, covering binary, ternary, and quaternary systems. The prediction errors of all compositions were controlled within 0.5 eV/atom, and the error percentages were lower than 25%, demonstrating the strong extrapolation prediction ability of the models. When predicting the binary phase diagrams of rare-earth compounds La-Al and Ce-H using the trained models, the convex hull phase diagrams constructed through the ensemble learning architecture, which combines the prediction results of the RF and NN models, were highly consistent with those constructed from the Open Quantum Materials Database. The models successfully captured several metastable phases that were not present in multiple databases. Moreover, the convex hull distances of the predicted phases were mostly less than 0.1 eV/atom, with the maximum not exceeding 0.2 eV/atom. In conclusion, this study successfully used ML models to predict the thermodynamic stability of rare-earth compounds. The constructed models demonstrated strong capabilities in classification and regression tasks. The ensemble learning architecture effectively improved the model performance, providing a promising tool for materials discovery in the field of rare-earth science and contributing to the research and development of new rare-earth compounds and the design of advanced materials.

The unique electronic properties of cyclo[N]carbon have attracted considerable attention due to their potential applications in gas storage and sensing technologies. This work employed density functional theory (DFT) and DLPNO-CCSD(T) to investigate the molecular adsorption characteristics of cyclo[N]carbon (N = 12, 14, and 16) with various gas molecules. It is interesting that the adsorption strength of cyclo[N]carbon for gases increases as the size of cyclo[N]carbon increases, with polar molecules demonstrating stronger interactions than nonpolar ones. Local energy decomposition analysis at the high-end DLPNO-CCSD(T) level of theory reveals that London dispersion forces significantly contribute to adsorption stability. By incorporating the Mg2 dimer in two-layer C16, a stable Mg22+@(C16)22- complex is formed, and the encapsulation of divalent cations considerably enhances the gas molecule adsorption performance. This study provides valuable insights into the adsorption properties of cyclo[N]carbon, which could be crucial for advancements in next-generation molecular devices.

Although a substantial amount of research has been conducted to unravel the structural configurations of selenium under pressure, the exquisite sensitivity of selenium's p-orbital electrons to this external force, leading to a plethora of structural variations, leaves several intermediary phases still shrouded in mystery. We, herein, systematically identify the structural and electronic transformations of selenium under high pressure up to 300 GPa, employing crystal structure prediction in conjunction with first-principles calculations. Our results for the transition sequence (P3121 -> C2/m -> R3m -> Im3m) of selenium are in good agreement with experimental ones. In particular, we first clarified the knowledge pertaining to the atomic arrangement within the monoclinic C2/m phase of selenium. Electron-phonon coupling calculations indicate that the superconductivity observed in this material, akin to that in tellurium, is realized via a phase transition. Furthermore, the superconducting critical temperature (Tc) displays a consistent rise as the material experiences high-pressure phase transitions from C2/m to R3m and then to Im3m, achieving a maximum Tc of 13.06 K in the Im3m phase at 97.5 GPa. Our findings illuminate the path towards a deeper comprehension of the high-pressure structure and physics of selenium, prompting the need for innovative experimental and theoretical research.

Rechargeable lithium-sulfur (Li-S) batteries have been considered as a potential energy storage system due to their high theoretical specific energy. However, their practical commercial application has been hindered by unresolved key issues. One promising approach to overcoming these challenges is the development of anchoring materials with exceptional performance. In this work, we conducted detailed evaluations of twelve types of MA(2)Z(4) (M = Ti, Zr, or Hf; A = Si or Ge; and Z = P or As) monolayers as potential Li-S battery electrodes through first-principles calculations. Our results indicate that these monolayers can effectively immobilize Li2Sn species, preventing them from dissolving into the electrolyte and preserving intact Li2Sn conformations. The high electrical conductivity of these monolayers can be perfectly retained after S-8/L2Sn clusters adsorption. Furthermore, the MA(2)P(4) monolayers demonstrate superior catalytic performance for the sulfur reduction reaction (SRR) compared to the MA(2)As(4) counterparts, whereas the MA(2)As(4) monolayers exhibit lower decomposition energy barriers. Our current work indicates that these MA(2)Z(4) monolayers hold significant promise as electrode materials for Li-S batteries.

Transition metal W-P compounds with excellent mechanical and superconducting properties have attracted tremendous research interest. Herein, the mechanical, phonon, and superconducting properties of five W-P compounds are studied by first-principles and electron-phonon calculations. The phonon and elastic constants calculated results reveal that the five W-P compounds are dynamically and mechanically stable. Simultaneously, these compounds are elastic anisotropic. The Cmc21-WP2 has high hardness of 21.04 GPa, which is harder than W2C (15.5 GPa) [Int. J. Refract. Met. H. 2024, 123, 106,745]. Furthermore, the P-6 m2-WP not only has excellent mechanical properties with the larger stiffness and hardness (16.66 GPa), but also has superconducting critical temperature of 0.62 K mainly due to the low-frequency (below 6THz) acoustic phonon modes originated from the motion of W-5d atoms.

Sliding ferroelectricity (FE) in two-dimensional (2D) materials offers a promising route to ultrathin, low-power, nonvolatile devices, yet in pristine homobilayers it is typically weak and confined to a small subset of known 2D materials. This limitation prompts a central question: Can sliding FE be reliably induced and substantially enhanced in otherwise nonferroelectric homobilayers through a controllable, experimentally feasible intervention? Here, we combine high-throughput first-principles calculations with machine learning to establish atomic intercalation as a versatile strategy for activating and amplifying sliding FE. Notably, we identify pristine InP and GaP as record performers, exhibiting out-of-plane polarizations up to 21.0 pC/m, approximately ten times greater than experimentally synthesized BN and over 45 times higher than MoS2, while maintaining moderate switching barriers of just 13.6 and 32.0 meV/f.u. Intercalation with Cu, Ag, and Au expands the pool from 106 pristine sliding FE systems to 591, including 212 activated from initially nonferroelectric states. To accelerate exploration of this enlarged configurational space, we develop a descriptor-free graph neural network incorporating message passing and global attention, enabling direct polarization prediction from atomic configurations. This integrated computational-machine learning framework delivers a robust and tunable pathway for designing high-performance sliding ferroelectrics and a blueprint for targeted discovery of functional 2D materials.