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DOE OSTI2025

Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models

We develop a new interatomic force field for Silicene, a 2D material with a buckled hexagonal lattice structure with high polymorphism. We introduce new parameterizations of a Tersoff model using a hierarchical multi-reward reinforcement learning (RL) methodology coupled with a continuous Monte Carlo Tree Search optimization. Our model significantly outperforms existing methods by enhancing the accuracy of predictions for the structural and thermodynamic properties of seven silicene polymorphs-including structure, energy, equation of state, elasticity, and phonon dispersion-when compared to established models. We further make a comprehensive comparison of the various models in predicting the mechanical and thermal properties of silicene. We trace the origin of the improved performance to the description of the angular dependence in the bond-order term, suggesting that modifying the angular terms in short-range models is essential to capture the structural diversity in low dimensional systems.

2D materials↗

DOE OSTI2025

A combined experimental and machine learning exploration of Ti 2-x Zr x MnCrFeNi high entropy Laves hydrides

A series of high entropy AB 2 -type Ti 2-x Zr x MnCrFeNi alloys (x = 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 and 1.2) were synthesized to investigate their potential for hydrogen storage and chemical compression. The influence of the Ti/Zr ratio was explored in terms of structural, microstructural and thermodynamic properties. The storage capacity together with the reaction enthalpy and entropy changes of the synthesized high entropy alloys were compared to predictions from Machine Learning (ML) to investigate changes in these properties across the explored composition space. The results revealed that a decreasing Zr content consistently lowered the hydride formation enthalpy and increased the plateau pressure from 8 to >90 bar H 2 at 25 °C, in good agreement with ML predictions. Selected compositions (x = 1.0 and 1.2) demonstrated reversible hydrogen storage capability over 150 cycles, with capacities of 1.34–1.40 wt % H 2 and remarkable reaction kinetics (<4 min) at ambient temperature. These experimental and computational findings highlight the potential of this Laves-HEA system as tuneable, stable, and cost-effective materials suitable for long-term operations in stationary hydrogen storage and compression applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Benefits and challenges in deployment of low global warming potential R290 refrigerant for room air conditioning equipment in California

High global warming potential gases (“high GWP”) are the fastest growing sector of greenhouse gas emissions in the world and in California and are primarily used as refrigerant gases in refrigeration and cooling equipment. Hydrofluorocarbons (HFCs) refrigerants are the dominant type of high GWP gases with GWP values thousands of times larger than CO 2 on a 100-year timescale. Refrigerant-grade propane (“R290”) has a very low GWP (GWP = 3.3) with good thermodynamic properties and good cooling equipment performance but the flammability of any leaked refrigerant makes equipment design, handling, and maintenance critical factors to manage. This paper focuses on the potential climate benefits and costs of transitioning to R290 refrigerant in small room air conditioning (AC) units, specifically window AC, packaged terminal AC/heat pumps (PTAC/PTHP), and mini-split heat pumps. Overall climate impact for a transition to all three types of air conditioning units in the 2022–2051 timeframe is found to be from 15 to 64 million metric tons of greenhouse gas (GHG) savings in California with a cost of saved CO 2 eq that ranges from $14.50 per ton of CO 2 eq saved to -$50.30 per ton of CO 2 eq saved (net savings) depending on whether the baseline refrigerant is R32 or R410A and depending on the relative energy efficiency for R290 units compared to baseline units.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Nonadiabatic Force Matching for Alchemical Free-Energy Estimation

We propose a method to compute free-energy differences from nonadiabatic alchemical transformations by using flow-based generative models. The method, nonadiabatic force matching, hinges on estimating the dissipation along an alchemical switching process in terms of a nonadiabatic force field that can be learned through stochastic flow matching. The learned field can be used in conjunction with short-time trajectory data to evaluate upper and lower bounds on the alchemical free energy that variationally converge to the exact value if the field is optimal. Applying the method to evaluate the alchemical free energy of atomistic models shows that it can substantially reduce the simulation cost of a free-energy estimate at a negligible loss of accuracy when compared with thermodynamic integration.

Computational chemistry↗

DOE OSTI2026

Rate constants and third-body collision efficiencies for recombination of Na with OH and O2: Implications for flame inhibition by alkali salts

Flame inhibition by alkali metals has implications for solid fuel combustion, fire safety, and a number of industrial processes. While the mechanism of inhibition is fairly well understood, details related to thermodynamic properties and rate constants are still in question. In the present work, the recombination of Na with OH (R5) and O2 (R7), respectively, was characterized theoretically, and the implications for modeling laminar premixed hydrogen flames doped with sodium species were examined. Third-body collision efficiencies and low-pressure-limit rate constants were obtained using newly fitted ab initio-based potential energy surfaces, classical trajectories, and one-dimensional master equation calculations. The results are consistent with available experimental results and aid in the present modeling study by providing rate information for bath gases and conditions that remain unexplored experimentally. Chemical kinetic modeling of relative Na and absolute H and OH profiles in H2-fueled laminar, premixed flames doped with a sodium salt shows that the most important radical removal cycle is the sequence Na + OH (+M) → NaOH (+M) (R5), NaOH + H → Na + H2O (R12), even under oxidizing conditions. A secondary cycle, Na + O2 (+M) ⇄ NaO2 (+M) (R7), NaO2 + OH → NaOH + O2 (R14), is less important due to the low thermal stability of NaO2. In most flames, reaction R7 is partially equilibrated, and reaction R14 becomes rate-limiting for the second cycle. The flame analysis supports a lower value of k14 than indicated by recent work on KO2 + OH, but more work is required to confirm this.

Jasper, Ahren W.↗

DOE OSTI2025

Inverse Thermodynamics: Designing Interactions for Targeted Phase Behavior

The traditional goal of inverse self-assembly is to design interactions that drive particles toward a desired target structure. However, achieving successful self-assembly also requires tuning the thermodynamic conditions under which the structure is stable. In this work, we extend the inverse design paradigm to explicitly address this challenge by developing a framework for inverse thermodynamics, i.e., the design of interaction potentials that realize specific thermodynamic behavior. As a step in this direction, using patchy particle mixtures as a model system, we demonstrate how precise control over both bonding topology and bond energetics enables the programming of targeted phase behavior. In particular, we establish design principles for azeotropic demixing and show how to create mixtures that exhibit azeotropy at any prescribed composition. Our predictions are validated through Gibbs-ensemble simulations [Panagiotopoulos, Mol. Phys. 1987, 61, 813−826]. These results highlight the necessity of coupling structural design with thermodynamic engineering, and provide a blueprint for controlling complex phase behavior in multicomponent systems.

Azeotropes↗

DOE OSTI2025

Probing the Solvation Shells of Lithium Ions in Glyme-Based Electrolytes

Glymes have been extensively studied as solvents for Li-battery electrolytes, most recently in equimolar mixtures with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), due to their ability to form stable solvates. However, directly quantifying free and coordinated glyme molecules in the liquid state has been challenging due to several experimental limitations. Here, in this work, new vibrational probes are demonstrated for studying the solvation structures of diglyme and triglyme in LiTFSI electrolytes. These IR probes make use of an amine group to report the solvation state of glymes at salt-to-solvent molar ratios ranging from 1:5 to 1:10. Characterization of the thermodynamic properties of the solvent exchange occurring in the first solvation shell of lithium ions (Li + ) showed an equilibrium constant for these probes close to unity at room temperature. This result demonstrates that the probes exhibit a similar solvation behavior to their glyme analogue. Concentration dependence studies also revealed a lack of significant amounts of contact ion pairs at the studied concentrations. Moreover, the first solvation shell of Li + appears to be formed by two partially chelating glyme molecules, establishing that even triglyme with multiple chelation sites does not fully coordinate the cation. Complementary molecular dynamics (MD) simulations agree with the experimental results and suggest that at these concentrations, TFSI – predominantly forms solvent-separated ion pairs. However, the simulations do not properly capture the partial solvation structure of the glyme molecules in the solvation shell of Li + as derived from the experiments.

electrolytes↗

DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗

DOE OSTI2025

Tuning the Conformations of an M4L4 Cage and Their Impact on Catalysis

Enzymes catalyze chemical reactions with remarkable rate enhancements and selectivity. Supramolecular catalysis seeks to understand and emulate these outcomes, leveraging noncovalent interactions, electric fields, and controlled active site microenvironments to enhance catalysis in an enzyme-like fashion. The effects of conformational dynamics on supramolecular catalysts and assemblies are, however, relatively unexplored, despite their crucial role in enzyme rate enhancement. Here, we elucidate the conformational landscape of a model M4L4 supramolecular host through a rational approach: stabilizing a high-energy conformer through distal ligand modification and a transient intermediate state through symmetry-matched guest encapsulation, as well as tuning the conformer distribution through same-charge metal exchange at the host vertices. Each of these structural modifications induces a substantial shift in the host's conformational landscape, offering insights into the rational design of conformationally dynamic cages and enzymes. Although the thermodynamic properties of the dynamic Ga4L412- cage can be influenced by temperature, solvent, and guest binding, we find that conformational change occurs on a time scale that renders it rate-limiting in a model catalytic reaction, precluding rate enhancement through conformational selection. This concept is illustrated by locking the catalytically inactive conformer to a high-energy conformer that is catalytically competent. These findings demonstrate that precise modulation of the conformational landscape of supramolecular hosts provides an effective strategy for controlling their catalytic activity and binding.

Catalysis↗

DOE OSTI2024

The Role of the Toroidal Vortex in Cumulus Clouds' Entrainment and Mixing

Abstract Shallow convective clouds play a crucial role in Earth's energy budget, as they modulate the radiative transfer in the atmosphere and participate in the vertical transport of aerosols, energy, and humidity. The parameterizations representing these complex, vital players in weather and climate models are mostly based on a description of steady‐state plumes and are a source of major uncertainty. Recently, several studies have shown that buoyant thermals are inherent in atmospheric convection and contain a toroidal (ring) vortex. This work studies those vortices in growing shallow cumulus (Cu) clouds using high‐resolution (10 m) Large Eddy Simulations that resolve these vortices in much detail. Recent analysis of such data showed that small‐scale turbulent diffusion is unable to explain the large diluted portion of the cloud. Here we advocate for the important role of the Cu toroidal vortex (TV) in cloud dilution and present the complex dynamics and structure of a Cu TV. Nevertheless, since the vortex dominates the cloud's dilution, simplicity emerges when considering the cloud's lateral mass flux profile. The cloud mixing is quantified using direct flux calculations and Eulerian tracers. In addition, Lagrangian tracers are used to identify the origin of the entrained air and its thermodynamic properties. It shows that most of the air entrained by the vortex is not recycled by the vortex, yet is significantly more humid than the environment. We suggest that the development of new models describing thermals, together with their toroidal vortices, might improve cloud parameterizations in weather and climate models.

Meteorology & Atmospheric Sciences↗

DOE OSTI2025

Flower‐Type Organized Trade‐Wind Cumulus: A Multi‐Day Lagrangian Large Eddy Simulation Intercomparison Study

Shallow cumulus cloud fields in subtropical marine trade wind environments, particularly over the tropical Atlantic Ocean, show distinct organizational patterns. Among these, Flower‐type clouds are characterized by expansive stratiform cloud patches surrounded by regions of scattered convection. The objectives of this study were (a) to construct a case study of a time period during the EUREC 4 A/ATOMIC field campaign when Flower‐type organization was observed, (b) to evaluate the fidelity of a multi‐model ensemble of large eddy simulations of that case, and (c) to analyze the interaction between cloud and precipitation processes and mesoscale organization in the simulations. The simulations follow a quasi‐Lagrangian trajectory, allowing mesoscale features to develop over time in a domain that follows the boundary‐layer airmass. The results show a broad agreement in simulated thermodynamic properties across different LES codes, with Flower‐type cloud patches appearing within hours of each other. The consensus among models is consistent with observations made during the EUREC 4 A/ATOMIC field campaign on the specific day of interest. The cloud structure reveals three distinct peaks in the joint probability densities of cloud base and cloud top height, with the dominant peak at any given time influenced by the stage of cloud organization. The simulated cloud system evolution reveals consistent occurrence of maxima in liquid water path and rain rate before Flower reaches its maximum length scale. Targeted sensitivity tests reveal a weak relationship between Cloud Droplet Number concentration and the extent/degree/type of organization.

EUREC4A↗

DOE OSTI2025

Investigation of Isobaric Mixing as a Mechanism for Boundary‐Layer Cloud Formation

This study investigates the potential role of isobaric mixing in the formation of marine boundary layer clouds. Cloud formation theory emphasizes uplift and adiabatic cooling, but recent observations support the existence of small clouds forming at various altitudes, even below the lifting condensation level. Isobaric mixing of air with different thermodynamic properties can generate localized supersaturation. A Gaussian mixing model is employed to simulate this process, considering the correlation between temperature and water vapor. Cloud droplet size distributions from aircraft measurements show a persistent and prominent mode of small droplets at 9 m, and the size of this mode compares favorably with predictions from the model. The results suggests that isobaric mixing plausibly contributes to the formation of clouds, particularly those observed at multiple altitudes with narrow droplet size distributions. This finding highlights the importance of considering isobaric mixing processes in understanding and modeling cloud formation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Unconventional gapping behaviour in a kagome superconductor

Determining the types of superconducting order in quantum materials is a challenge. This is especially true when several degrees of freedom contribute to the fermiology and when superconductivity competes with other symmetry-breaking orders. One example is the kagome-lattice superconductor CsV 3 Sb 5 , in which multiband superconductivity coexists with a charge order that substantially reduces the space group symmetries of the compound. Here we demonstrate the presence of two superconducting regimes in CsV 3 Sb 5 that are characterized by distinct transport and thermodynamic properties. Our results reveal a substantial quasiparticle weight in a high-temperature regime. At lower temperatures, this weight is removed through the formation of a second gap. We argue that the band with a gap opening at lower temperatures continues to host low-energy quasiparticles, possibly due to the nodal structure of the gap. Taken together, our results present evidence for band-selective superconductivity with uncoupled superconducting gaps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br

Abstract We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (p-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT (ωB97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models.

Science & Technology - Other Topics↗

DOE OSTI2026

Two-component dynamics in supercritical $\text {CO}_2$ from inelastic X-ray scattering

Supercritical fluids are characterized by unique thermodynamic properties. One of these properties is the existence of two-component dynamics that is associated with distinct low-frequency and high-frequency vibrational responses of the fluid. However, the origin of this behavior remains unknown. By combining inelastic X-ray scattering and molecular dynamics simulations, we show that this behavior can be connected to density heterogeneities arising from molecular clusters. Analyses of measurements and molecular trajectories suggest that the two-component dynamics emerges due to distinct momentum fluctuations of clustered and unbound molecules. This connection between clusters and two-component dynamics highlights the importance of molecular-structural heterogeneities in supercritical fluids, colloids, and condensed-matter systems.

biophysics↗

DOE OSTI2024

57 Fe nuclear resonance vibrational spectroscopic studies of tetranuclear iron clusters bearing terminal iron( III )–oxido/hydroxido moieties

57 Fe nuclear resonance vibrational spectroscopy (NRVS) has been applied to study a series of tetranuclear iron ([Fe 4 ]) clusters based on a multidentate ligand platform (L 3− ) anchored by a 1,3,5-triarylbenzene linker and pyrazolate or (tertbutylamino)pyrazolate ligand (PzNH t Bu − ). These clusters bear a terminal Fe(III)–O/OH moiety at the apical position and three additional iron centers forming the basal positions. The three basal irons are connected with the apical iron center via a μ 4 -oxido ligand. Detailed vibrational analysis via density functional theory calculations revealed that strong NRVS spectral features below 400 cm −1 can be used as an oxidation state marker for the overall [Fe 4 ] cluster core. The terminal Fe(III)–O/OH stretching frequencies, which were observed in the range of 500–700 cm −1 , can be strongly modulated (energy shifts of 20–40 cm −1 were observed) upon redox events at the three remote basal iron centers of the [Fe 4 ] cluster without the change of the terminal Fe(III) oxidation state and its coordination environment. Therefore, the current study provides a quantitative vibrational analysis of how the remote iron centers within the same iron cluster exert exquisite control of the chemical reactivities and thermodynamic properties of the specific iron site that is responsible for small molecule activation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage

Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.

Cho, YongJun [Korea Advanced Institute Science and↗

DOE OSTI2024

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗