The discovery of new 2D materials is vital for advancing electronics and quantum technologies. As most 2D materials originate from layered bulk structures, identifying exfoliable crystals and estimating the energy required to isolate a single layer are critical steps. To address this issue, we developed a robust and computationally cheap approach based on the crystal graph construction via Voronoi partition, interaction strength estimation via bond valence theory, and the iterative removal of weak links while tracing the periodicity changes. We validated our method against literature and ab initio results proving that it can reliably identify layers and provide an approximate estimate of the interlayer binding energy suitable as a screening parameter. We subsequently applied it to analyze a large set of 48,504 preselected experimental crystal structures, uncovering 694 previously unreported 2D materials belonging to 530 different structural prototypes. Finally, we used ab initio simulations to offer an overview the structural and electronic properties of the isolated layers.

Understanding how ordered crystals emerge from disordered precursors is a grand challenge in materials science. Resolving this question is critical for tailoring the properties of network materials, particularly zeolitic silicates, which are indispensable in energy and environmental catalysis. This challenge has been particularly acute as the key three-dimensional (3D) ordering of the silicate network has remained experimentally inaccessible. However, detailed knowledge of crystallization would enable tailoring of key properties for improved applications. Here, we provide direct observation of this process using O 1s X-ray emission spectroscopy (XES) as a spectroscopic ruler for the 3D network geometry. Supported by ab initio simulations, our results reveal the systematic evolution of the O-Si-O-Si torsion angles during the crystallization of an MWW-type zeolite. We discovered that the progressive ordering of these angles into a staggered conformation is a decisive early-stage event that precedes the formation of the crystalline framework. This work establishes O 1s XES as a powerful tool for probing local network topology, enabling quantitative access to key geometric parameters of 3D silicate networks, including bond distances, bond angles, and torsion angles. This reveals a fundamental principle: topological ordering is a distinct, precursorial event that occurs prior to the emergence of long-range crystalline order. This "topology-first" insight fundamentally advances the rational design of zeolites and other complex network materials with atomic-level precision.

The solvation properties of electrolytes are one of the primary drivers of the performance of concentrated aqueous-based batteries. In this study, we provide an understanding of the lithium solvation and its dynamics in an electrolyte derived from a deep eutectic solvent (comprising LiClO4, urea and water) by effectively correlating multinuclear (1H, 17O, 35Cl, and 7Li) diffusion nuclear magnetic resonance (NMR) spectroscopy with molecular dynamics (MD) simulations. The freely accessible static field gradient from a commercial NMR magnet was used to determine diffusion coefficients for all population-averaged constituents─Li+, ClO4-, urea, and water─at various concentrations. The diffusion ratios DLi+/Durea ≈ 1 and DLi+/DClO4- ≈ 1 for the concentrated 1:3:2 electrolyte suggest correlated ionic motion and transport dominated by a vehicular character rather than hopping mechanisms. Ab initio MD simulations reveal mixed Li solvation shell configurations, with substantial urea coordination (∼66% of Li+ with one or two urea molecules in the 1:3:2 system) and an increasing occurrence of contact ion pairs with concentration. This results in relatively slow ionic mobility with a cation transference number around 0.5. Ion cluster analysis reveals a systematic increase in the occurrence of contact ion pairs with concentration, yet cluster sizes remain far below percolation thresholds, consistent with vehicular transport. Residence time analysis further quantifies this picture: the characteristic diffusional length exceeds the inner-sphere solvation shell radius (Lc/Ls > 1) across all concentrations, providing evidence for vehicular-dominated transport. 17O diffusion NMR measurements (D17O/D1H > 1.2 in a concentrated system) provide insights into water dynamics despite significant experimental challenges from quadrupolar relaxation. These findings provide molecular-level insights into aqueous electrolytes based on deep eutectic solvents to further guide the rational design of aqueous battery systems.

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO3 and establishes a general protocol for predicting processing-structure-performance relationships.

Abstract The time-dependent Schrödinger equation (TDSE) in real space is fundamental to understanding the dynamics of many-electron quantum systems, with applications ranging from quantum chemistry to condensed matter physics and materials science. However, solving the TDSE for complex fermionic systems remains a significant challenge, particularly due to the need to capture the time-evolving many-body correlations, while the antisymmetric nature of fermionic wavefunctions complicates the function space in which these solutions must be represented. We propose a general-purpose neural network framework for solving the real-space TDSE, Fermionic Antisymmetric Spatio-Temporal Network, which treats time as an explicit input alongside spatial coordinates, enabling a unified spatiotemporal representation of complex, antisymmetric wavefunctions for fermionic systems. This approach formulates the TDSE as a global optimization problem, avoiding step-by-step propagation and supporting highly parallelizable training. The method is demonstrated on five benchmark problems: a 1D harmonic oscillator, interacting fermions in a time-dependent harmonic trap, 3D hydrogen orbital dynamics, a laser-driven hydrogen atom, and a laser-driven H$_2$ molecule, achieving excellent agreement with reference solutions across all cases. These results demonstrate the method's accuracy and flexibility within the bound-state manifold across various dimensions and interaction regimes. While the current localized Ansatz inherently restricts the description of extensive ionization and continuum states, the method demonstrates the capability to stably simulate coherent multi-electron dynamics over extended time windows. Our framework offers a highly expressive alternative to traditional basis-dependent or mean-field methods, opening new possibilities for ab initio simulations of time-dependent quantum systems, with applications in quantum dynamics, molecular control, and ultrafast spectroscopy.

As a novel type of high-energy-density, environmentally friendly, and low-sensitivity energetic materials (EMs), cyclo-pentazolate salts are being extensively studied. However, their detonation mechanism remains unclear. This study developed a neural network potential (NNP) to simulate the shock-induced detonation process of NH3OH+N5-, a representative salt of the pentazolate anion (N5-). The well-trained NNP exhibits high precision comparable to DFT, as well as high efficiency. The NNP-based large-scale molecular dynamics (MD) simulations for NH3OH+N5- produced an ideal C-J detonation velocity of 9.4 km/s, which is in agreement with the value estimated by the Cheetah 7.0 program (9.93 km/s). The simulation demonstrates that the proton transfer from NH3OH+ to N5- is the initial reaction, while the primary decomposition pathway of N5- is a ring-opening reaction, or the bimolecular reactions with its initial decomposition intermediate azide anion N3- resulting in the formation of N8 ring. Quantum chemical calculations show that these pathways possess low activation barriers. The influence of nuclear quantum effects on shock-induced chemical reactions was also studied, which shows that nuclear quantum corrections not only improve the accuracy of predicted ideal detonation velocity but also improve temperature in simulations, which results in the different reaction mechanism of shock-induced detonation reaction of NH3OH+N5-, facilitating the ring-opening reaction of N5- ring and preventing its reaction with N3. This study enhances the understanding of the detonation mechanism of cyclo-pentazolate salts. In this work, NNP potential was trained by the DeePMD-kit package and homemade FORTRAN code. The density functional theory (DFT) calculation of structural energies and atomic forces, as well as ab initio molecular dynamics (AIMD), was conducted using the Vienna Ab initio Simulation Package (VASP) software. The PAW method and the GGA-PBE functional were adopted. The shock wave response MD simulations were conducted by LAMMPS with the multiscale shock technique (MSST) and QB-MSST methods. Quantum chemical calculations were carried out at the M06-2X/TZVP level using the Gaussian 09 program.

Using glycine as a model system, this study examines the surface assisted reaction between methylamine and formic acid on an olivine (010) surface. A comparative analysis of co-adsorbed, mixed phase, and gas phase pathways of methyl amine and formic acid to form glycine shows that a surface assisted pathway exists that connects adsorbed formic acid and methylamine to a surface stabilized glycine configuration with significantly reduced reaction energetics relative to gas phase, suggesting a plausible role for silicate minerals in facilitating complex organic synthesis under astro-physically relevant conditions. First-principles density functional theory calculations were performed using the Vienna Ab initio Simulation Package (VASP) with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional and the projector augmented wave (PAW) method. Minimum energy reaction pathways and activation barriers were determined using the nudged elastic band (NEB) method, with all energies referenced to clearly defined initial states. Short time thermal stability of surface-bound glycine was assessed using ab initio molecular dynamics (AIMD) simulations at 1200K to probe high temperature persistence on picosecond timescales.

We present an ab initio framework forsimulatingpolariton transport dynamics based on the classical path approximation(CPA). The quantum dynamics of polariton transport involves simulatingmany electronic degrees of freedom, making a fully ab initio dynamics simulation computationally expensive. We demonstrate thatthe CPA, which removes the need for excited-state nuclear gradients,is well-suited for polaritonic systems because collective light–mattercoupling leads to vanishing excited-state forces. Benchmark comparisonsbetween CPA and full evaluation of the excited-state forces show excellentagreement for polariton transport results in model light–mattersystems such as polariton group velocities and mean-squared displacements. Ab initio simulations of polariton transport using CPAreproduce key physical trends that are observed in experiments withBODIPY molecules. Our work establishes the CPA as a highly efficienttool for ab initio investigations of transport andenergy flow in hybrid light–matter systems.

High-entropy alloys (HEAs) are emerging as next-generation structural materials due to their outstanding mechanical and functional properties. However, their vast compositional and configurational complexity poses major challenges for conventional trial-and-error approaches and ab initio simulations, which struggle with high computational costs and limited predictive accuracy. Existing machine learning approaches, while promising, remain constrained by data scarcity, limited interpretability, and the lack of effective human-AI interaction. To address these limitations, we introduce an integrated human-computer interactive HEA design platform that incorporates emotional feedback. By combining natural language processing (T5 model), machine learning (XGBoost), and multi-objective optimization (NSGA-II), the platform establishes a closed-loop "perception-decision-optimization" workflow. Real-time emotion recognition dynamically adjusts optimization weights, enabling efficient human-AI collaboration. The model achieves high accuracy in predicting yield strength and Young's modulus, with SHAP analysis revealing the underlying physical mechanisms. Emotion-driven optimization guides Pareto front convergence, with results showing <1.4% deviation from experimental values. This high degree of accuracy underscores the efficacy of integrating affective feedback into the optimization loop, enabling a more responsive and user-aligned design process that effectively bridges subjective expert preferences with quantitative multi-objective optimization. The multiscale modeling further validates the platform's reliability for complex dual-phase alloys. This work establishes a novel paradigm for interpretable and efficient AI-driven material design, highlighting the transformative potential of integrating artificial intelligence with expert knowledge.

Abstract Using ab initio simulations, we investigate high-harmonic generation in hydrogen atoms driven by a near-infrared (NIR) laser pulse combined with an attosecond extreme-ultraviolet (XUV) pulse. The efficiency of high-harmonic generation is significantly enhanced when the XUV pulse drives atoms from the ground state to excited p state. Furthermore, by varying the time delay between the two laser pulses, we calculate the attosecond transient absorption spectra of hydrogen atoms based on numerical solutions of the time-dependent Schrödinger equation (TDSE). Combining attosecond transient absorption spectra with high-harmonic spectra provides a powerful approach to revealing laser-induced energy-level shifts and the splitting structure of resonant harmonic emission.

We report on a joint experimental and theoretical investigation of the metal 2p X-ray photoelectron spectra of transition-metal phthalocyanines MnPc, FePc, and CoPc. Using a multiconfigurational approach including spin-orbit coupling, we obtain nearly quantitative agreement with experiment. In all investigated systems, we found an unexpected and robust physical effect induced by core ionization, namely a drastic reorganization of the valence electronic structure. This reorganization is characterized by a pronounced stabilization of doubly excited ligand-to-metal charge-transfer configurations, which become energetically favored in the final states. This ionization-induced double charge transfer is found to be common to all metallophthalocyanines studied. Furthermore, we identify a nearly linear correlation between the stabilization energies obtained from restricted active space configuration interaction (RAS-CI) calculations and a simple hydrogenic/Slater screening estimate, providing an intuitive and practical descriptor to assess when such charge-transfer states are expected to dominate after core ionization.

The vibrational lifetime of solute molecules is predicted to be slower at interfaces; however, ultrafast measurements show that this behavior can vary dramatically depending on interfacial structure and vibrational coupling pathways. Surprisingly, the nitrile stretch of SCN- exhibits unexpectedly rapid vibrational energy relaxation at aqueous (D2O) alumina interfaces, while there is no appreciable difference in relaxation in bulk and interfacial H2O. For interfacial D2O, the CN stretch lifetime is nearly three times shorter than in bulk D2O (T1 ∼ 22ps). Ab initio simulations reveal an increased vibrational density of states (VDOS) at the low frequency OD stretch region compared to bulk D2O, enhancing overlap between SCN- and D2O vibrational modes. Additional factors-including stronger transition dipole-transition dipole coupling arising from reduced dielectric screening and increased orientational ordering of interfacial molecules-further accelerate vibrational relaxation at the interface. To directly probe how interfacial VDOS varies with surface structure, we employed the CN stretch lifetime of SCN- as a reporter of the O-D VDOS at two model alumina surfaces: Al2O3(0001)/D2O and Al2O3(112¯0)/D2O. IR pump-vibrational sum frequency generation (vSFG) probe measurements show a shorter vibrational lifetime at the Al2O3(0001)/D2O interface (7.1ps) compared to the Al2O3(112¯0)/D2O interface (8.7ps). Molecular dynamics simulations support these findings, showing that the low-frequency O-D stretch VDOS at the Al2O3(0001)/D2O interface is approximately ∼1.2 times higher than at the Al2O3(112¯0)/D2O surface. The higher VDOS provides more available accepting states for vibrational energy transfer, thus shortening the vibrational lifetime. Together, these results demonstrate that vibrational lifetimes of interfacial solutes provide a powerful experimental probe of interfacial VDOS and solute-solvent coupling. This approach offers new insight into vibrational relaxation pathways and the microscopic origins of energy dissipation in the bulk and at interfaces.

In the pursuit of non-toxic and high-efficiency perovskite solar cell materials, this study investigates the enhancement of thermoelectric and optoelectronic properties of Zn-doped KSn1-x Zn x I3 (x = 0, 0.25, 0.5, 0.75, 1) perovskites. The study uses first-principles density functional theory (DFT) with the Vienna Ab initio Simulation Package (VASP). Structural analysis confirms a transition from orthorhombic (Pnma) to monoclinic (Pm) phases. All the compositions exhibit thermodynamic, mechanical, and dynamic stability. Electronic properties reveal a robust bandgap range of 1.47-1.96 eV (GGA-PBEsol) and 2.34-3.02 eV (HSE06), positioning these materials as promising candidates for the top cell in a tandem solar cell and UV-optoelectronics. An indirect-to-direct band structure transition occurs at 50% Zn doping, which primarily enhances the stiffness, Pugh's ratio (2.39-2.70), and Poisson's (0.316-0.335) ratio of the lattice for KSn1-x Zn x I3. The elastic modulus (E), shear modulus (G), and bulk modulus (B) in KSn1-x Zn x I3 also significantly increased upon addition of Zn in the compound. These behaviors indicate that although there is better lattice stiffness in the material, there is still very good ductility for making flexible devices. Near-perfect mechanical isotropy has been achieved in KZnI3 with a universal elastic anisotropy factor (A U) of only 0.15. This low level of anisotropic elastic behavior indicates that KZnI3 is unlikely to experience micro- fracture during or after manufacturing. Thermoelectric analysis shows that KSnI3 maintained a high Seebeck coefficient of 230 µV K-1 at low temperature, while KZnI3 showed a 225 µV K-1 Seebeck coefficient at elevated temperature. A high figure of merit (ZT) is achieved by both pristine compounds at high temperature, with values of 1.01 for KSnI3 and 1.27 for KZnI3. Furthermore, for optical properties, a high absorption coefficient of 7.32 × 105 cm-1 is observed by 25% Zn doping at UV-visible range. These findings make Zn-doped KSnI3 perovskite material suitable for efficient, non-toxic, low-cost optoelectronic and thermoelectric devices.

We provide an updated scattering library for the simulation of thermal neutron transport in air, including the effects of rotational and vibrational modes in N2 and O2 and neutron magnetic scattering in O2, showing their significance compared to smaller effects related to water humidity. The modeling is based on the Young-Koppel treatment of thermal neutron scattering from diatomic molecules, as well as abinitio simulations of the electronic density of O2. The theoretical predictions are benchmarked against experimental measurements of the total scattering cross section of air under monitored thermophysical conditions, in the neutron energy range between 0.6meV and 10keV. The updated scattering library is used to calculate excess neutron scattering in air, compared to traditional approaches, where only nuclear scattering from gases of free nuclei is implemented in Monte Carlo transport codes.

Classical molecular dynamics simulations of hydrogen plasma, employing quantum pseudopotentials such as the Kelbg pseudopotential, provide a computationally efficient alternative to fully quantum methods. This approach avoids the high cost of ab initio quantum simulations while enabling the study of dynamic properties and time evolution in dense partially ionized plasmas. However, classical methods often fail to properly account for the quantum exchange correlations that govern the behavior of electrons. In this work, we systematically investigate the stability of classical hydrogen plasma using the standard and improved Kelbg pseudopotentials and molecular dynamics across the range of coupling parameters Γ=0.25–2.5. We demonstrate that both pseudopotentials lead to unphysical clustering of electrons with identical spin projections at Γ&amp;gt;0.5, resulting in a complete system collapse at Γ≥1.0. This artifact, clearly visible in radial distribution functions and energy evolution, manifests itself as an unphysical peak at distances below the thermal de Broglie wavelength, reflecting the failure of the pairwise pseudopotential to reproduce fermionic exchange effects. Our analysis establishes Γ≈0.5 as the upper limit of applicability for these classical approaches. The results highlight a critical gap in current methodologies and provide a clear motivation for the development of physically justified corrections that explicitly emulate quantum exchange effects.

Abstract Two-dimensional (2D) topological insulator, or quantum spin Hall (QSH) insulator, holds topologically protected edge states with significant implications for diverse fields, yet the robustness against realistic structural defects remains elusive. Here, by combining first-principles calculations, ab initio molecular dynamics (AIMD) and Monte Carlo simulations, we consider Bi(111) bilayera large-gap QSH insulatorunder ion irradiation, and explore the topological robustness with the generated defect structures. Systematic Ar-ion irradiation simulations allow the determination of defect types and concentrations over a broad range of incident ion energies and fluences, with single and double vacancies identified as the dominant defects. Analyses on the band structures of irradiated Bi(111) samples reveal that the spin-orbit coupling induced band inversion would be destroyed by the electronic defect states when the defect concentration reaches ~3%. This is confirmed by direct visualization of the real-space topological edge states and defect states in 1D nanoribbons, exhibiting strong hybridization. Our work not only provides fundamental insights into the topological robustness of QSH insulators, but also sheds light on their practical implementation in quantum information processing and spintronic devices under extreme conditions.

Highly sensitive and selective detection of L -arginine is essential for probing its involvement in cellular metabolism, nitric oxide synthesis, and amino acid regulation, thereby facilitating clinical diagnostics and translational biosensing applications. In this study, a graphene oxide–fluorescein (GOFC) fluorescence sensor was developed for the selective and sensitive detection of L -arginine via a fluorescence “turn-on” response. Graphene oxide (GO) synthesized via the modified Hummers’ method was functionalized with fluorescein to form the GOFC composite. The sensor exhibited a concentration-dependent fluorescence enhancement toward L -arginine, enabling detection down to 1 × 10⁻⁶ M using a spectrofluorometer and 1 × 10⁻⁴ M by naked-eye observation. Density functional theory (DFT) calculations using the Vienna Ab initio Simulation Package (VASP) revealed that the interaction between fluorescein and L -arginine is stronger than their respective adsorption on GO, leading to fluorescein release and a fluorescence turn-on response. Using a low-concentration linear calibration, the limits of detection and quantification were determined to be 4.38 × 10⁻⁶ M and 1.46 × 10⁻⁵ M, respectively. Structural and morphological characterization of GO and GOFC was confirmed by FTIR, Raman spectroscopy, powder XRD, particle size analysis, and FE-SEM. These findings establish the GOFC sensor as a sensitive and practical platform for rapid L -arginine biosensing.

Abstract Tritium (T) self-sustainability in fusion reactors demands minimal T retention in plasma-facing materials (PFMs) tungsten (W). While vacancy clusters in W act as strong trapping sites for hydrogen (H) isotopes T, their behavior is significantly altered in a fusion environment where transmutation elements rhenium (Re) and osmium (Os) segregate around these defects. Using systematic ab initio and Object Kinetic Monte Carlo simulations, we investigate the segregation behavior of Re/Os near vacancy clusters and their synergistic impact on H retention in W. Our results reveal that both Re and Os energetically favor segregation around vacancy clusters, with Os exhibiting stronger binding and a more persistent segregation tendency. Crucially, Re/Os decoration significantly reduces the capacity of vacancy clusters to trap H atoms, lowering both the total and incremental binding energies of H. This effect is concentration-dependent, becoming more pronounced at higher Re/Os levels. Moreover, Re/Os segregation reduces the desorption temperature of H from vacancy clusters, facilitating H release and thereby decreasing overall H retention in W. These findings explain the experimental phenomena why H retention in W–Re alloys is orders-of-magnitude lower than in pure W after high-temperature irradiation. These results provide atomic-scale insights into the H retention mechanism in W and offer valuable guidance for designing advanced W-based PFMs with low H inventory.

All-solid-state batteries have great potential to outperform conventional lithium-ion batteries in both safety and energy density, as the solid electrolyte can potentially accommodate high-energy-density anodes such as metallic lithium or silicon more safely. However, the high-valence cations present in most highly conductive solid electrolytes facilitate reductive decomposition at low potentials, leading to significant irreversible lithium inventory loss. Preventing this requires the development of solid electrolytes that are thermodynamically stable at low operating potentials while providing high ionic conductivity and sufficient oxidative stability. To realize this, we explored a new family of Li-rich antifluorite irreducible solid electrolytes, Li2.65S0.35NxP0.65-x, the first reported nitrido-phosphido-sulfide, and investigated their application in all-solid-state batteries. The optimized composition Li2.65S0.35N0.15P0.5 possesses a remarkably high ionic conductivity of 1.05 mS cm-1, as well as a relatively high oxidative stability of 1.15 V vs Li+/Li for this class of materials. Ab initio molecular dynamics and density functional theory simulations reveal that enhanced Li diffusion is the result of enlarged diffusion bottleneck sizes. These are a consequence of (i) substitution with smaller anions or (ii) increased electrostatic repulsion from the substitution with high-valence anions. Importantly, the oxidative stability makes Li2.65S0.35N0.15P0.5 exhibit good compatibility with Si anodes, and in conjunction with the high ionic conductivity, this enables a high initial Coulombic efficiency of 94.2% as well as a stable cycle life of a full cell with a micron silicon-Li2.65S0.35N0.15P0.5 anode and a LiCoO2-Li3InCl6 cathode. This work highlights the potential of irreducible solid electrolytes for the design of all-solid-state batteries with low-potential and high-energy-density anodes.

The complete active space self-consistent field (CASSCF) method is essential for describing complex photochemical processes, but its application in ab initio molecular dynamics is often limited by the computational cost associated with four-center two-electron repulsion integrals (ERIs). We implement the atomic orbital (AO)-based GPU-accelerated density fitting (DF) approximation for CASSCF within the TeraChem software package. Validation on salicylaldimine demonstrates that the DF approximation introduces negligible errors in relative energies, yielding excitation energies accurate to within 10 microHartrees of the integral-direct reference. The DF-CASSCF implementation achieves significant computational speedups, accelerating total energy and gradient calculations by more than an order of magnitude for small- to medium-sized systems with large AO basis sets. We demonstrate the practical utility of this approach through ab initio multiple spawning dynamics simulations of excited-state intramolecular proton transfer (ESIPT). The DF-CASSCF trajectories reproduce the photodynamics of the reference simulations while reducing the total wall time (for a single GPU) by a factor of 3-30, depending on the choice of the basis set. This work significantly lowers the barrier for high-throughput, high-accuracy multireference simulations on modern GPU architectures.