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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations

Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.

BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
DOE OSTI2026

Shadow molecular dynamics for flexible multipole models

Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. Shadow molecular dynamics simulations are driven by approximate “shadow” Born–Oppenheimer potentials for which the exact charges and forces are directly accessible without relying on costly (and approximate) iterative solvers. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy functions, potentials, and force terms, explicitly incorporating monopole–monopole, dipole–monopole, and dipole–dipole interactions. In our formulation, both atomic monopoles and atomic dipoles are treated as extended dynamical variables alongside the propagation of the nuclear degrees of freedom. We demonstrate that introducing the additional dipole degrees of freedom preserves the stability and accuracy previously seen in monopole-only shadow molecular dynamics simulations. In addition, we present a shadow molecular dynamics scheme where the monopole charges are held fixed while the dipoles remain flexible. Our extended shadow dynamics provide a framework for stable, computationally efficient, and versatile molecular dynamics simulations involving long-range interactions between flexible multipoles. This is of particular current interest in combination with machine-learned interatomic potentials, including long-range electrostatic interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

From bulk to surface: Structure and dynamics of amorphous alumina from deep potential molecular dynamics

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio -quality models of amorphous Al 2 O 3 bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 Å, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al–O bonds, and hosts a broad population of under-coordinated motifs (notably AlO 3 and OAl 2 ) whose abundances are governed by glass stability. These under-coordinated surface motifs exhibit distinct vibrational signatures and occur as locally paired Lewis acid and Brønsted base sites consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, surface relaxation and the glass-transition temperature remain comparable to their bulk counterparts, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure–property relationships in disordered oxide interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mapping the structural–mechanical landscape of amorphous carbon with ReaxFF molecular dynamics

We use ReaxFF molecular dynamics (MD) to investigate the relationship between structural and mechanical properties in bulk and nanostructured amorphous carbon (a-C). The liquid-quench MD method is used to generate isotropic bulk samples with mass densities ranging from 0.96 to 3.29 g/cm3. Structural analysis identifies two types of structures with distinct short- and medium-range order: lower-density sp2-dominated a-C, which is characterized by a bimodal ring-size distribution, and higher-density sp3-dominated tetrahedral amorphous carbon (ta-C), exhibiting a unimodal ring-size distribution. Stress–strain MD simulations and analysis reveal how an atomistic structure impacts elastic properties and post-yield atomic rearrangements. All stretched structures demonstrate elastic isotropy and plasticity driven by a ring-size expansion mechanism reflected in changes in ring statistics. The plastic region is substantially larger in ta-C than in a-C due to the post-yield shift from sp3 to sp2 C dominant bonding. In both a-C and ta-C, ultimate failure occurs when a reactive crack, traversed by long sp chains, forms and propagates predominantly perpendicular to the direction of the applied strain. Oxygen infiltration into the fractured region significantly reduces stress resistance, primarily through the early rupture of long sp chains. MD simulations and analysis are extended to a-C slabs, a-C nanotubes, and partially a-C nanotubes. The latter nanostructure highlights the differences between the elastically isotropic a-C walls, which develop circumferential cracking, and the crystalline walls, which tear along crystallographic directions. These results provide a strong foundation for further computational characterization of a-C materials.

Dernov, A. (ORCID:0009000004220973)↗
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unraveling the Nature of Vibrational Dynamics in CsPbI 3 by Inelastic Neutron Scattering and Molecular Dynamics Simulations

Cesium lead iodide, CsPbI 3 , is an optoelectronic material of large interest for various technological applications; however, fundamental questions surrounding the vibrational dynamics of this material, especially regarding its role in structural phase transitions, remain to be elucidated. Here, in a combined variable temperature inelastic neutron scattering (INS) and machine-learning based molecular dynamics (MD) simulation study, we show that the stable phase at room temperature, i.e., the nonperovskite δ-phase, exhibits phonon modes with weak anharmonicity with only a weak temperature dependence from 10 K all the way up to the transition to the cubic perovskite α-phase at approximately 600 K. In contrast, the α-phase features anharmonic and damped vibrational dynamics, mainly associated with overdamped tilting motions of the PbI 6 octahedra. Crucially, these overdamped tilting modes, which relate to the tetragonal and orthorhombic distorted perovskite phases (β- and γ-phase, respectively) formed at lower temperatures, stay overdamped by more than 100 K above the respective phase transition. This suggests a flat energy landscape of octahedral tilting motions in α-CsPbI3 and with structural fluctuations on the picosecond time scale with tilting patterns that locally resemble the structure of the β- and γ-phases. The vibrational dynamics of α-CsPbI 3 are also characterized by pronounced anharmonic motions with large thermal displacements of the Cs + ions, but these modes remain underdamped at 600 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

DelloStritto, Mark (ORCID:0000000206785860)↗
DOE OSTI2025

Overcoming time and complexity limitations in molecular dynamics investigations of equilibrium melting

Abstract A hybrid Monte-Carlo molecular-dynamics method for determining solidus and liquidus compositions in multicomponent systems is presented that overcomes both the time limitations in conventional molecular dynamics that prevent the evolution of distinct solid and liquid compositions via diffusion and the complexity challenge that prevents use of thermodynamic assessment in systems of many components. This hybrid method is validated in the Cu–Ni system against an independent assessment of solidus and liquidus compositions based on the regular solution model. Strategies for efficient mapping of different phase diagrams, based on the thermodynamic parameter T 0 , the temperature at which two phases of the composition X 0 have equal free energies, are presented and then demonstrated for the copper-nickel fully miscible system and the gold–silicon eutectic system. A calculation of the solidus and liquidus sampled during the equilibrium melting of equiatomic CrMnFeCoNi is performed, indicating that this method has potential to be extended to the study of many component alloys.

Au-Si↗
DOE OSTI2025

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.

Atomistic models↗
DOE OSTI2025

Structure, dynamics, and electrochemistry of choline chloride/ethylene glycol eutectic solvents at an electrode surface explored by molecular dynamics simulations

Choline chloride and ethylene glycol mixtures with 1:2, 1:4, and 1:6 molar ratios on the surfaces of graphite and gold electrodes were studied using classical molecular dynamics simulations. Both neutral and charged electrodes were considered. The liquid composition, solvation structure, molecular orientation, and dynamics at the electrode surface are significantly different from those of the bulk liquid. These properties strongly depend on the electrode material and charge density, whereas they are less sensitive to the overall solvent composition. The effect of the electrode on the composition, structure, and orientation of the liquid fades beyond ∼10 Å from the surface of the electrode. This distance corresponds to about two layers of the structured solvent, despite the fact that the layered structure extends to at least five layers or about 25 Å. However, the electrode influences solvent dynamics over a longer distance. The electrochemical properties of the eutectic solvent at both electrode surfaces were also studied. The simulations captured the experimental differential capacitance shapes for both electrode systems, although the magnitudes and exact shapes differ. The simulations further revealed that the solvent in the first solvation layer can both overscreen and underscreen the electrode charges depending on the electrode material and electrode potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Energy Transfer Mechanisms in Large Low-Bandgap Polymers from Time-Resolved Experiments and Nonadiabatic Molecular Dynamics Calculations

Conjugated polymers offer unprecedented chemical tunability for modulating energy transfer in a multitude of infrared light applications. In this work, we use a combination of time-resolved spectroscopic experiments and nonadiabatic molecular dynamics calculations to probe the photochemistry and nonradiative transitions in a recently synthesized narrow bandgap donor–acceptor conjugated polymer based on alternating cyclopentadithiophene and electronegative benzothiadiazole heterocycles. Using large-scale semi-empirical nonadiabatic molecular dynamics, which can treat a large 260-atom hexamer, we calculate an S 5 → S 1 lifetime of 34.75 fs, which is consistent with our time-resolved spectroscopic data. Our simulations suggest that vibronic motions of the central carbons in the cyclopentadithiophene functional groups are predominantly involved in the nonradiative transitions, and the excitation becomes more localized on a monomer fragment over time. The combined use of time-resolved experiments and nonadiabatic molecular dynamics calculations in this work provides mechanistic insight into chemical functionalities that can be tuned to enhance energy transfer in other prospective low-bandgap polymer materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Molecular dynamics simulation of hydrodynamic transport coefficients in plasmas

Molecular dynamics simulations are used to calculate transport coefficients in a two-component plasma interacting through a repulsive Coulomb potential. The thermal conductivity, electrical conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity are computed using the Green–Kubo formalism over a broad range of Coulomb coupling strength, 0.01 ≤ Γ ≤ 140. Emphasis is placed on testing standard results of the Chapman–Enskog solution in the weakly coupled regime (Γ ≪ 1) using these first-principles simulations. As expected, the results show good agreement for Γ ≲ 0.1. However, this agreement is only possible if careful attention is paid to the definitions of linear constitutive relations in each of the theoretical models, a point that is often overlooked. For example, the standard Green–Kubo expression for thermal conductivity is a linear combination of thermal conductivity, electrothermal, and thermoelectric coefficients computed in the Chapman–Enskog formalism. Meaningful results for electrical conductivity are obtained over the full range of coupling strengths explored, but it is shown that potential and virial components of the other transport coefficients diverge in the strongly coupled regime (Γ ≫ 1). In this regime, only the kinetic components of the transport coefficients are meaningful for a classical plasma.

Electrical conductivity↗
DOE OSTI2025

Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions

Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar + ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl 2 and Ar + impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl 2 -Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. Further, the demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

MixPI: Mixed-time slicing path integral software for quantized molecular dynamics simulations

We introduce the MixPI software to implement path integral molecular dynamics (PIMD) simulations for the study of condensed phase systems where nuclear quantum effects (NQEs) are important. In contrast to existing PIMD simulation software, MixPI enables the implementation of mixed quantum–classical path integral simulations where only a subset of system degrees of freedom (dofs) are treated quantum mechanically in an extended phase space while the remaining dofs are described classically. We expect this software to be particularly useful for simulations of electron and proton transfer in condensed phase systems, as well as for the study of biological and material systems where only a handful of dofs contribute significantly to the observed NQEs. We demonstrate the use of MixPI in two different systems. The first is a simple water model where we implement a set of mixed quantum–classical simulations to compute average energy and radial distribution functions. We use these simulations to benchmark the effectiveness of MixPI and to demonstrate how it enables systematic investigation into the origin of observed NQEs. We then compute radial distribution functions for a system where MixPI is essential: a solvated metal (M 2+ ) cation described using an explicit quantized electron localized on an M 3+ ion in water.

chemical physics↗
DOE OSTI2025

Mechanisms and stability of Li dynamics in amorphous Li-Ti-P-S-based mixed ionic–electronic conductors: A machine learning molecular dynamics study

Mixed ionic–electronic conductors (MIECs) exhibit both high ionic and electronic conductivity to improve the battery performance. In this work, we investigate the mechanism and stability of transport channels in our recently developed MIEC material, amorphous Ti-doped lithium phosphorus sulfide (LPS), using molecular dynamics (MD) simulations with a 99% accurate machine-learning force field (MLFF) trained on ab initio MD data. The achieved MLFF helps efficient large-scale MD simulations on LPS with three Ti concentrations (10%, 20%, and 30%) and six temperatures (25°C to 225°C) to calculate ionic conductivity, activation energy, Li-ion transport mechanism, and configurational entropy. Results show that ionic conductivities and activation energies are consistent with our recent experimental values. Moreover, Li-ion transport occurs via free-volume diffusion facilitated by the formation of disordered Li-S polyhedra. The enhanced stability of transport channels at 10% and 20% Ti doping, compared to 0% and 30%, is observed by analyzing the vibrational and configurational entropy of these disordered Li-S polyhedra. Overall, this study highlights the utility of MLFF-based large-scale MD simulations in explaining the transport mechanism and the stability of Li-ion in Ti-doped LPS electrolyte with significant computational efficiency.

And configuration entropy↗
DOE OSTI2024

Impact of classical statistics on thermal conductivity predictions of BAs and diamond using machine learning molecular dynamics

Machine learning interatomic potentials (MLIPs) have greatly enhanced molecular dynamics (MD) simulations, achieving near-first-principles accuracy in thermal conductivity studies. In this work, we reveal that this accuracy, observed in BAs and diamond at sub-Debye temperatures, stems from an accidental error cancelation: classical statistics overestimates specific heat while underestimating phonon lifetimes, balancing out in thermal conductivity predictions. However, this balance is disrupted when isotopes are introduced, leading MLIP-based MD to significantly underpredict thermal conductivity compared to experiments and quantum statistics-based Boltzmann transport equation. This discrepancy arises not from classical statistics affecting phonon–isotope scattering rates but from its impact on the interplay between phonon–isotope and phonon–phonon scattering in the normal scattering-dominated BAs and diamond. In conclusion, this work underscores the limitations of MLIP-based MD for thermal conductivity studies at sub-Debye temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware

The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗