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DOE OSTI2025

A new biogeochemical modelling framework (FLaMe-v1.0) for lake methane emissions on the regional scale: development and application to the European domain

This study presents a new physical-biogeochemical modelling framework for simulating lake methane (CH 4 ) emissions at regional scales. The new model, FLaMe-v1.0 (Fluxes of Lake Methane), rests on an innovative, computationally efficient lake clustering approach that enables the simulation of CH 4 emissions across a large number of lakes. Building on the Canadian Small Lake Model (CSLM) that simulates the lake physics, we develop a suite of biogeochemical modules to simulate transient dynamics of organic Carbon (C), Oxygen (O 2 ), and CH 4 . We first test the performance of FLaMe-v1.0 by analyzing physical and biogeochemical processes in two theoretical lakes with characteristics that can be considered representative for many lakes (an oligotrophic, deep lake driven by cold climate versus a eutrophic, shallow lake driven by warm climate). Next, we evaluate the model by comparing simulated and observed timeseries of CH 4 emissions in four well-surveyed lakes. We then apply FLaMe-v1.0 at the European scale to evaluate simulated diffusive and ebullitive lake CH 4 fluxes against in-situ measurements in both boreal and central European regions. Finally, we provide a first assessment of the spatio-temporal variability in CH 4 emissions from European lakes with a surface area comprised between 0.1–1000 km 2 (n= 108 407, total area = 1.33 × 105 km 2 ), indicating a total emission of 0.97 ± 0.23 Tg CH 4 yr −1 , with the uncertainty constrained by combining FLaMe-v1.0 and machine learning techniques. Moreover, 30 % and 70 % of these CH 4 emissions are through diffusive and ebullitive pathways, respectively. Annually averaged CH 4 emission rates per unit lake area during 2010–2016 have a South-to-North decreasing gradient, resulting in a mean over the European domain as 7.39 g CH 4 m −2 yr −1 . Our simulations reveal a strong seasonality (with ice-blocking effects accounted for) in European lake CH 4 emissions, with nearly ten times higher emissions during late summer than during winter. This pronounced seasonal variation highlights the importance of accounting for the sub-annual variability in CH 4 emissions to accurately constrain regional CH 4 budgets. In the future, FLaMe-v1.0 could be embedded into Earth System Models to investigate the feedback between climate warming and global lake CH 4 emissions.

Maisonnier, Manon [Free Univ. of Brussels (Belgium↗

DOE OSTI2020

Size- and temperature-dependent magnetization of iron nanoclusters

In this paper, the magnetic behavior of bcc iron nanoclusters, with diameters between 2 and 8 nm, is investigated by means of spin dynamics simulations coupled to molecular dynamics, using a distance-dependent exchange interaction. Finite-size effects in the total magnetization as well as the influence of the free surface and the surface/core proportion of the nanoclusters are analyzed in detail for a wide temperature range, going beyond the cluster and bulk Curie temperatures. Comparison is made with experimental data and with theoretical models based on the mean-field Ising model adapted to small clusters, and taking into account the influence of low coordinated spins at free surfaces. Our results for the temperature dependence of the average magnetization per atom M (T), including the thermalization of the transnational lattice degrees of freedom, are in very good agreement with available experimental measurements on small Fe nanoclusters. In contrast, significant discrepancies with experiment are observed if the translational degrees of freedom are artificially frozen. The finite-size effects on M (T) are found to be particularly important near the cluster Curie temperature. Simulated magnetization above the Curie temperature scales with cluster size as predicted by models assuming short-range magnetic ordering. Analytical approximations to the magnetization as a function of temperature and size are proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Review of radiation-induced defects in GaAs

Radiation-resilient optoelectronic materials are highly desired in space and nuclear applications, and understanding the relevant defects and electronic processes in those materials is crucial for both current and next-generation applications. GaAs is a prototypical semiconductor that serves as a foundational system for describing and understanding optoelectronic devices. In this review, both experimental studies and molecular dynamics (MD) simulations of irradiated GaAs are reviewed, with particular emphasis on the deep-level transient spectroscopy, irradiation-induced amorphization zones, and MD predictions of damage structures. The MD results are also compared to predictions of the non-ionized energy-loss model. Recent theoretical studies, in particular, density functional theory based calculations on the simple intrinsic defects and defect clusters in GaAs, are also reviewed. These defects have an important role in dictating the evolution of GaAs in radiation damage environments, as they impact the coupled dynamics of charge carriers. Finally, possible gaps and challenges toward the general understanding of defect evolution in GaAs are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Formation of I 1 stacking fault by deformation defect evolution from grain boundaries in Mg

I 1 stacking faults (SFs) in Mg alloys are regarded as the nucleation sites of $\langle \text{c+a} \rangle$ dislocations that are critical for these alloys to achieve high ductility. Previously it was proposed that the formation of I 1 SFs requires the accumulations of a large number of vacancies, which are difficult to achieve at low temperatures. In this study, molecular dynamics (MD) and molecular statics (MS) simulations based on empirical interatomic potentials were applied to investigate the deformation defect evolutions from the symmetric tilt grain boundaries (GBs) in Mg and Mg-Y alloys under external loading along $\langle c \rangle$-axis. The results show the planar faults (PFs) on Pyramidal I planes first appear due to the nucleation and glide of $\langle {\frac {1}{2}}c + p\rangle$ partial dislocations from GBs, where $\langle p\rangle$ = ${\frac {1}{3}} \langle10\bar{1}0\rangle$. These partial dislocations with pyramidal PFs interact with other defects, including pyramidal PFs themselves, GBs, and $\langle p\rangle$ partial dislocations, generating a large amount of I 1 SFs. Detailed analyses show the nucleation and growth of I 1 SFs are achieved by atomic shuffle events and deformation defect reactions without the requirements of vacancy diffusion. Our simulations also suggest the Y clusters at GBs can reduce the critical stress for the formation of pyramidal PFs and I 1 SFs, which provide a possible reason for the experimental observations that Y promotes the $\langle{\text{c+a}}\rangle$ dislocation activities.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Atomic Diffusion, Segregation, and Grain Boundary Migration in Nickel-Based Alloys from Molecular Dynamics Simulations

Grain boundary diffusion and metal mobility in alloys control material performance in many applications and yet remain poorly understood at a mechanistic level. With advances in accessible time and length scales for computational molecular simulations, and recent force field developments, we now possess tools to help unravel those mechanisms. Using large-scale molecular dynamics simulations, here we examined vacancy-mediated diffusion processes in Ni-5Cr alloy with low and high-energy grain boundaries. We show that atomic diffusion inside the grain boundary plane is about four times higher than bulk diffusion, at any temperature, and exhibits a typical Arrhenius behavior with a very small energy barrier (0~.8 eV for Cr and 0.7 eV for Ni within 1300-1600 K). Additionally, the fastest diffusing species inverts; Cr diffusion was faster than Ni in the bulk but slower in the grain boundaries. This is attributed to the creation of high cohesive energy clusters of Cr at the grain boundary. Grain boundary migration was also observed to be temperature dependent and appears to be two times higher in the 5% Cr alloy than in pure Ni, highlighting the important role of the alloying element on grain boundary motion.

Simonnin, Pauline GN↗

DOE OSTI2025

Qubit-Efficient Quantum Chemistry with the ADAPT Variational Quantum Eigensolver and Double Unitary Downfolding

Here, in this work, we combine the recently developed double unitary coupled cluster (DUCC) theory with the adaptive, problem-tailored variational quantum eigensolver (ADAPT-VQE) to explore the accuracy of unitary downfolded Hamiltonians for quantum simulation of chemistry. We benchmark the ability of DUCC effective Hamiltonians to recover dynamical correlation energy outside of an active space. We consider the effects of strong correlation, commutator truncation, higher-body terms, and approximate external amplitudes on the accuracy of these effective Hamiltonians. When combining these DUCC Hamiltonians with ADAPT-VQE, we observe similar convergence of the ground state as compared with bare active space Hamiltonians, demonstrating that DUCC Hamiltonians provide increased accuracy without increasing the load on the quantum processor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

CephFS experiments on stria.sandia.gov

This report is an institutional record of experiments conducted to explore performance of a vendor installation of CephFS on the SNL stria cluster. Comparisons between CephFS, the Lustre parallel file system, and NFS were done using the IOR and MDTEST benchmarking tools, a test program which uses the SEACAS/Trilinos IOSS library, and the checkpointing activity performed by the LAMMPS molecular dynamics simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

CephFS experiments on stria.sandia.gov

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Nature of molybdenum carbide surfaces for catalytic hydrogen dissociation using machine-learned potentials: an ensemble-averaged perspective

Molybdenum carbides with an electronic structure similar to noble metals have gained attention as a promising low-cost catalyst for biomass valorization and the hydrogen evolution reaction. However, our fundamental understanding of the catalyst surface and how different phases of these catalysts behave at varying reaction conditions is limited to ground state density functional theory calculations as ab initio molecular dynamics (AIMD) is computationally prohibitive at relevant length and time scales. Here, in this work, we train a multi-atomic cluster expansion (MACE) machine-learned interatomic potentials (MLIP) to study hydrogen dissociation and dynamics over Mo, δ-MoC, α-Mo 2 C, and β-Mo 2 C surfaces at varying temperatures and hydrogen partial pressures. Our simulations identify unique and different molecular and atomic hydrogen adsorption sites on different surfaces that do not depend on the temperature. At low hydrogen pressures, the surface coverage is monolayer, which transitions to two-layer adsorption at higher pressures. We find that atomic hydrogen diffusion and recombinations are preferred over molybdenum atom hollow sites, while the diffusion over carbon-terminated facets was negligible, signifying particularly strong C–H interactions. In contrast, molecular hydrogen adsorption occurs mostly atop Mo or the bridging sites. At a comparable hydrogen loading, β-Mo 2 C (001) is the most active surface for hydrogen dissociation reaction. This work provides insights into the dynamic nature of the hydrogen dissociation chemistry and the diversity of hydrogen adsorption sites on molybdenum carbides.

08 HYDROGEN↗

DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗

DOE OSTI2026

Structure and Flow-Viscosity of Filled-Polymer-Based 3D Printing Ink: Exploration through Coarse-Grained Molecular Dynamics

The addition of nanofiller particles to a polymer matrix has long been known to enhance or modify the composite’s mechanical and rheological properties. However, quantitatively capturing such changes with molecular level simulations remains computationally challenging. Toward that goal, we performed coarse-grained molecular dynamics of a nanocomposite system at a fixed (25 vol %) filler loading under nonspecific, weak polymer–filler interactions representative of a broad class of technologically important materials. We report several interesting results, including: (1) the equilibrium chain-configuration remains Gaussian-like as in an unfilled melt; (2) smaller filler particles display a stronger tendency to cluster; (3) larger fillers act as plasticizers by reducing the entanglement density and accelerating the chain mobility; and (4) fillers enhance the tensile response modulus, with the effect being stronger for larger particles. We also simulate cluster breakup, yielding, and elongational flow under an applied time-linear tensile strain and study the flow viscosity as a function of filler-size and chain-length.

Materials science↗

DOE OSTI2026

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗

DOE OSTI2022

Methods to Calculate Electronic Excited-State Dynamics for Molecules on Large Metal Clusters with Many States: Ensuring Fast Overlap Calculations and a Robust Choice of Phase

Here, we present an efficient set of methods for propagating excited-state dynamics involving a large number of configuration interaction singles (CIS) or Tamm-Dancoff approximation (TDA) single-reference excited states. Specifically, (i) following Head-Gordon et al., we implement an exact evaluation of the overlap of singly-excited CIS/TDA electronic states at different nuclear geometries using a biorthogonal basis and (ii) we employ a unified protocol for choosing the correct phase for each adiabat at each geometry. For many-electron systems, the combination of these techniques significantly reduces the computational cost of integrating the electronic Schrodinger equation and imposes minimal overhead on top of the underlying electronic structure calculation. As a demonstration, we calculate the electronic excited-state dynamics for a hydrogen molecule scattering off a silver metal cluster, focusing on high-lying excited states, where many electrons can be excited collectively and crossings are plentiful. Interestingly, we find that the high-lying, plasmon-like collective excitation spectrum changes with nuclear dynamics, highlighting the need to simulate non-adiabatic nuclear dynamics and plasmonic excitations simultaneously. In the future, the combination of methods presented here should help theorists build a mechanistic understanding of plasmon-assisted charge transfer and excitation energy relaxation processes near a nanoparticle or metal surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Water-enhanced bifunctional metal-acid catalyst for C=C bond hydrogenation

Water-assisted proton shuttling can promote hydrogenation of polar functional groups, and it is generally believed that such an effect can be hardly applied to hydrogenation of C═C bonds due to the latter's weak interaction with water. Here, we report density functional theory calculations and metadynamics simulations, through which we show a dynamic bifunctional metal–acid site that can be transformed, when interacting with water, into an active configuration for unexpected water-enhanced proton shuttling to C═C bonds. In particular, we investigated B(OH)3 anchored to a Ni catalyst for hydrogenation of cyclohexene in an organic solvent, which showed in experiments an increased rate by 100 times when adding a small amount of water. Metadynamics simulations suggest that a B(OH)3–H2O cluster can form on Ni(111), which promotes the proton transfer in the first hydrogenation step, while the second hydrogenation is still driven by metal-mediated direct H-transfer. The recovery process of B(OH)3–H2O also involves a proton shuttling step. We find that the boric species on the surface serves as an electron reservoir and carries the negative charge to balance the positive charge in the proton transfer steps. This work thus provides fundamental insights into this dynamic transformation process of the metal–acid interface, which can in principle be applied to many other bifunctional systems for hydrogenating non-polar unsaturated groups by engineering the interfacial charge separation.

Sun, Shoutian↗

DOE OSTI2023

Structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquids close to Earth's liquid core conditions

Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid close to the Earth's liquid core conditions. Here, the simulation results reveal that the short-range structural order (SRO) in the $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid is very strong. About 80% of the atoms are arranged in crystallinelike SRO motifs. In particular, ~ $70$% of Fe-centered clusters can be classified as either hexagonal-close-packed-like or icosahedrallike SRO motifs. The SRO clusters centered on Fe, Si, or O atoms are strongly intermixed and homogenously distributed throughout the liquid. The atomic structure of the liquid and the fractions of dominant SRO clusters are not sensitive to pressure/temperature used in the simulations except that the SRO of the O-centered clusters is enhanced close to inner core pressures. The O-diffusion coefficient is about two to three times larger than the Fe and Si ions and increases more rapidly in the deeper core regions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Quantifying the phase diagram and Hamiltonian of S = 1/2 kagome antiferromagnets: bridging theory and experiment

Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J 2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J 2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.

Jiang, Shengtao [SLAC National Accelerator Laborat↗

DOE OSTI2023

Response of Sulfonated Polystyrene Melts to Nonlinear Elongation Flows

Ionizable polymers form dynamic networks with domains controlled by two distinct energy scales, ionic interactions and van der Waals forces; both evolve under elongational flows during their processing into viable materials. A molecular level insight of their nonlinear response, paramount to controlling their structure, is attained by fully atomistic molecular dynamics simulations of a model ionizable polymer, polystyrene sulfonate. As a function of increasing elongational flow rate, the systems display an initial elastic response, followed by an ionic fraction-dependent strain hardening, stress overshoot, and eventually strain-thinning. As the sulfonation fraction increases, the chain elongation becomes more heterogeneous. Finally, flow-driven ionic assembly dynamics that continuously break and reform control the response of the system.

36 MATERIALS SCIENCE↗