The next revolution in computational simulations: Harnessing AI and quantum computing in molecular dynamics
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Lappala, Anna (ORCID:0000000199262972)↗
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Automated, reliable, and efficient continental-scale replication of 7.3 petabytes of computational simulation data: A case study
We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.
Globus↗
Reticular Materials and AI-Driven Computer Simulations for Seawater Mining of Valuable Metals (Final Technical Report)
This Final Technical Report describes our exploratory efforts that combine reticular materials synthesis (hydrolytically robust metal–organic frameworks, MOFs) with AI‑enabled molecular simulations to develop mechanistic, quantitative design rules for recovering lithium and other alkali-metal ions from highly dilute, competitive aqueous resources (e.g., seawater). The central outcome is a joint experimental–computational study of ion uptake in MOF‑808 (Chemical Science, 2025) that quantifies both thermodynamics and kinetics of Li + , Na + , and K + uptake and identifies how pore size, pore hydration state, dehydration penalties, and pore-window transport barriers govern selectivity. Guided by these insights, we synthesized and tested functionalized MOF‑808 and multivariate MOFs incorporating ion-recognition motifs (including carboxylates and crown-ether linkers) and evaluated uptake in synthetic seawater, highlighting framework topology and pore chemistry as levers for improved Li + /Na + discrimination. We also developed transferable simulation models, enhanced-sampling protocols, and automated workflows that enable systematic screening of porous sorbents.
42 ENGINEERING↗
Computer simulation of reaction and transport of core-scale serpentinization of Fe-bearing olivine and evolution of geological hydrogen
We present reactive flow and transport simulations of core-scale serpentinization of forsteritic, Fe-bearing olivine under hydrothermal conditions. The model captures fluid flow, evolving porosity–permeability, and redox-controlled H 2 production in a cylindrical core over 55 days at 245 °C and 37 bar. Key processes include olivine dissolution, precipitation of serpentine and magnetite, and oxidation of Fe2+ to Fe 3+ . Here, results show strong coupling among flow velocity, alteration front propagation, and spatially heterogeneous H 2 generation. Elevated H2 concentrations align with inlet-localized magnetite precipitation, consistent with experiments. Secondary mineral formation reduces porosity and permeability, altering transport pathways. Reactive flow at the fluid–solid interface governs H 2 generation rates and distribution. Dissolved SiO2 promotes serpentine and talc formation by suppressing brucite, while bicarbonate extends reaction duration and moderates surface complexation effects.
Geologic hydrogen↗
Computational simulation of asymmetric phase transformation in cracked Li₇La₃Zr₂O₁₂: Variant selection and chemo-mechanical implications
Coupling between microcracks and phase transformation in ion-conducting ceramics can jointly affect mechanical responses and ion transport. In this work we investigate the cubic-to-tetragonal phase transformation in Li₇La₃Zr₂O₁₂ in the presence of a microcrack under hydrostatic loading and quantify its implications for crack-tip stress concentration and Li-ion transport using phase-field and molecular dynamics simulations. The phase transformation exhibits a strong asymmetry between hydrostatic tension and compression. Under tension, the crack edge nucleates one tetragonal variant that amplifies the crack-tip stress intensity and promotes crack opening. Under compression, the crack tip nucleates a different tetragonal variant that enhances the stress-induced crack-closure tendency. Effective Li diffusivity analysis shows faster transport degradation under compression due to accelerated transformation kinetics, exposing a trade-off between mechanical stability and ionic conductivity. These results highlight the intertwined nature of cracking, phase transformation, and ionic transport in ion-conducting oxides and provide mechanistic insights into chemo-mechanical degradation of solid electrolytes.
36 MATERIALS SCIENCE↗
Computer Simulations Show That Liquid–Liquid Phase Separation Enhances Self-Assembly
Not provided.
Chemistry↗
Phonon second harmonic generation in NaBr studied by inelastic neutron scattering and computer simulation
The phenomenon of second harmonic generation (SHG) was found for phonons in anharmonic NaBr by inelastic neutron scattering. The temperature dependence of this phonon SHG was measured from 300 K to 650 K. At 300 K the second harmonic (SH) is seen as a high-energy branch around 33 meV, nearly independent of $\overrightarrow{Q}$. The temperature effective potential (TDEP) method and classical molecular dynamics (MD) simulation with machine learning interatomic potential were able to reproduce the SH, and showed that SHG occurs with the flat transverse optical (TO) phonon branch. A classical model of a nonlinear medium explains the intensity and lifetime of the SH, compared to those of the TO modes. Also successful was a quantum model based on the Heisenberg-Langevin equation for interacting phonons coupled to a thermal bath, which also predicts a spectral distribution of the SH. In conclusion, the measured temperature dependence of the intensity of the second harmonic showed that it follows the Planck distribution of a one-phonon quasiparticle, and not two TO phonons.
36 MATERIALS SCIENCE↗
An Ab Initio Molecular Dynamics Study of Key Thermodynamic Input Parameters for Computer Simulation of U-6Nb Solidification
The key to metallic fuel development is the fabrication of uranium metal and alloys into fuel forms. U-Nb alloys are one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. Melt processing has been a key component of the metallic fuel cycle, and process models require thermophysical parameters at elevated temperatures, particularly above the melting temperatures, regarding which experimental data are scarce, for accurate simulations and process development. By means of ab initio density-functional theory (DFT) quantum molecular dynamics (QMD), we have calculated the main thermophysical parameters—the density, thermal expansion coefficient, specific heat, thermal conductivity, melting temperature, latent heat of fusion, and viscosity—used in the modeling of the U-6 wt.% Nb alloy casting. The melting temperature of the U-6 wt.% Nb alloy at ambient pressure is obtained by means of QMD simulations using the Z-method. The ambient volume change and latent heat of melting of U-6 wt.% Nb are also derived from QMD simulations in conjunction with analytical fitting for the energy and pressure. The thermal conductivity for the solid U-Nb alloy is calculated from the semi-classical Boltzmann transport equation combined with an estimate of the electron relaxation time obtained from DFT simulations.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Binding Free Energy Analysis of Colicin D, E3 and E8 to Their Respective Cognate Immunity Proteins Using Computational Simulations
Colicins are antimicrobial proteins produced by bacteria for the purpose of destroying neighboring bacteria. Colicin activity is neutralized by a specific cognate immunity protein in order to protect the host. This study investigates the structural and binding mechanisms underlying the interaction of colicin-D, -E3 and -E8 to their respective immunity proteins (ImD, Im3 and Im8) using structure prediction, molecular dynamics (MD) simulations and MM-PBSA approach of free energy calculations. High-confidence colicin-immunity (Col-Im) complex structures predicted using AlphaFold2 were subjected to MD simulations of 150 ns with GROMACS and were analyzed for the binding free energy calculation using gmx_MMPBSA. Results showed that the complex of Col_E3-Im3 exhibited the most favorable binding free energy, driven by strong van der Waals and electrostatic interactions. Col_D-ImD and Col_E8-Im8 also showed the favorable binding. Electrostatics and hydrogen bonding emerged as a key factor driving binding and stability, while polar solvation acted as a destabilizing factor across all systems. These outcomes provide an understanding of the molecular mechanisms of Col-Im systems, with potential applications for developing natural antimicrobials for food safety.
Biochemistry & Molecular Biology↗
Long-Range Dispersion Governs the Behavior of Near-Critical Fluids: Universal Scaling and Implications for Accurate Molecular Simulation
Computer simulations of near-critical and supercritical fluids often deviate from experimental results, a discrepancy commonly attributed to force-field resolutions and inaccuracies. We demonstrate that these errors primarily stem from using finite cutoff schemes for dispersion interactions, which become effectively long-range as the correlation length grows near the critical point. By employing the smooth particle-mesh Ewald method to account for full long-range dispersion, we show that coarse-grained models can also achieve high quantitative accuracy for n-undecane and propylbenzene. This approach enables the precise determination of density fluctuations and correlation lengths, which exhibit universal critical scaling and depend on the molecular size and shape. Our findings resolve a longstanding debate in molecular simulations of near-critical fluids, establishing that capturing long-wavelength fluctuations is essential for bridging the gap between the microscopic behavior and macroscopic critical phenomena.
Chen, Guang↗
Beyond-classical computation in quantum simulation
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here, we show that superconducting quantum annealing processors can rapidly generate samples in close agreement with solutions of the Schrödinger equation. We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three-, and infinite-dimensional spin glasses, supporting the observed stretched-exponential scaling of effort for matrix-product-state approaches. We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer within a reasonable time frame. Thus, quantum annealers can answer questions of practical importance that may remain out of reach for classical computation.
King, Andrew D. [D-Wave Quantum Inc., Burnaby, BC ↗
Investigation of Electrocatalytic CO 2 Reduction on MXene Materials via First-Principles Simulations
Computational studies of CO 2 reduction to yield various products were carried out on the basal plane and edges of different MXene materials. The impact of vacancies upon Mo 2 TiC 2 T x , W 2 TiC 2 T x , and Ti 3 C 2 T x (T x = O and OH) was also examined for both edge and basal sites. Initial calibrations were carried out to generate surfaces with optimal oxide/hydroxide ratios and proper termination sites upon which various vacancy sites were explored to ensure an accurate model of the surfaces’ resting states. From this work, Mo 2 TiC 2 O was determined to exhibit the lowest theoretical overpotential for methane as determined by volcano plot analyses. At a large enough vacancy concentration, the CO 2 reduction reaction (CO 2 RR) is predicted to outcompete the hydrogen evolution reaction (HER) as the predominant reaction on the surface. In conclusion, when examining the edge of Mo 2 TiC 2 O, stronger CO 2 binding was exhibited to such an extent that the reaction was predicted to terminate after the generation of formate on the edge.
Lund, Colton [Argonne National Laboratory (ANL), A↗
Lie-algebraic classical simulations for quantum computing
The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity and in the development of quantum technologies. Efficient techniques such as those based on the Gottesman-Knill theorem for Clifford circuits, tensor networks for low entanglement-generating circuits, or Wick's theorem for fermionic Gaussian states have become central tools in quantum computing. In this work, we contribute to this body of knowledge by presenting a framework for classical simulations, dubbed “𝔤-sim”, which is based on the underlying Lie algebraic structure of the dynamical process. When the dimension of the algebra grows at most polynomially in the system size, there exist observables for which the simulation is efficient. Indeed, we show that 𝔤-sim enables new regimes for classical simulations, is able to deal with certain forms of noise in the evolution, as well as can be used to tackle several paradigmatic variational and nonvariational quantum computing tasks. For the former, we perform Lie-algebraic simulations to train and optimize parametrized quantum circuits (thus effectively showing that some variational models can be dequantized), design enhanced parameter initialization strategies, solve tasks of quantum circuit synthesis, and train a quantum-phase classifier. For the latter, we report large-scale noiseless and noisy simulations on benchmark problems. By comparing the limitations of 𝔤-sim and certain Wick's theorem-based simulations, we find that the two methods become inefficient for different types of states or observables, hinting at the existence of distinct, nonequivalent resources for classical simulation.
97 MATHEMATICS AND COMPUTING↗
pH-Dependent Vibrational Dynamics Drives Excited-State Quenching in the Phycobiliprotein Complex PC645
Phycocyanin 645 (PC645) is a closed-form lightharvesting complex found in the lumen of the photosynthetic membrane of cryptophyte algae. These peripheral antenna complexes contain bilin chromophores that absorb sunlight and transfer excitation energy to the core antenna complexes embedded in the thylakoid membrane. The location of cryptophyte antenna complex on the luminal side of the membrane is unusual. During photosynthetic activity, the pH of the lumen drops, by up to two pH units. There is little known about how this pH-change affects the light-harvesting complexes. In this study, we report multiscale simulations using a computationally efficient density functional tight-binding framework to investigate the spectroscopy and excitation energy transfer in the PC645 complex. Complementary experiments were conducted using both steady-state and time-resolved spectroscopic measurements at low, neutral, and high pH values. Our study shows that (de)protonation of specific bilin pigments, namely, the mesobiliverdins (MBVs), modulates the excitation energies, excitonic couplings, and spectral densities. These changes cause excitation transfer rates to increase by up to a factor of two to three, leading to pH-dependent energy transfer pathways in the complex. Using this model, we calculated the pH-dependent fluorescence quantum yield of the system, obtaining quantitative agreement with the experimental results. These computational simulations, supported by experiments, identify MBVs as a more prominent excitation sink than previously realized, and that this role is tuned by pH.
Maity, Sayan [Constructor Univ., Bremen (Germany);↗
Digital quantum simulation of cavity quantum electrodynamics: insights from superconducting and trapped ion quantum testbeds
We explore the potential for hybrid development of quantum hardware where currently available quantum computers simulate open cavity quantum electrodynamical (CQED) systems for applications in optical quantum communication, simulation and computing. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis–Cummings model containing N atoms coupled to a lossy cavity. We report the results of executing this algorithm on two noisy intermediate-scale quantum computers: a superconducting processor and a trapped ion processor, to simulate the population dynamics of an open CQED system featuring N = 3 atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform, obtaining results which agree closely with the exact solution of the system. These results can be used as a recipe for efficient and platform-specific quantum simulation of cavity–emitter systems on contemporary and future quantum computers.
cavity QED↗
Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments
Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.
Saranathan, Gayathri [Hewlett-Packard]↗
Quantum Computing and Simulations for Energy-Related Applications
Quantum Information Science (QIS) is an emerging field that has the potential to cause revolutionary advances in many areas of science and engineering, and nations around the world are vying for dominance in the field. To support DOE urgent task to make sure the U.S. wins the quantum race, in Spring of 2019 NETL started to establish and maintain QIS competency by focusing on energy-related applications. After more than five years’ hard-working, NETL QUEST (quantum for energy systems & technologies) team has made great progress on quantum sensing and quantum computing for energy applications. Significant outcomes have been achieved. To report our research progress and to propose new research directions, in this presentation at the American Physical Society (APS) annual meeting, I'm highlighting the progress of QUEST team on quantum computing for energy-related applications.
quantum computing↗
SCALE Non-LWR Models for NRC Volume 3
This dataset contains input and result files of computational simulations with the SCALE code system. The simulations cover radionuclide inventory and reactivity analyses of various advanced reactors. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗