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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Teaching a neural network to attach and detach electrons from molecules

Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

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

Mechanism of DNA origami folding elucidated by mesoscopic simulations

Abstract Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Polarization-driven band topology evolution in twisted MoTe 2 and WSe 2

Motivated by recent experimental observations of opposite Chern numbers in R-type twisted MoTe 2 and WSe 2 homobilayers, we perform large-scale density-functional-theory calculations with machine learning force fields to investigate moiré band topology across a range of twist angles in both materials. We find that the Chern numbers of the moiré frontier bands change sign as a function of twist angle, and this change is driven by the competition between moiré ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moiré-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau level physics in the absence of magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts

The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd 8 Au 92 nanoparticles supported on silica using surface-sensitive techniques, we report that whether Pd overgrowth occurs before or after Au nanoparticle deposition onto the support controls the surface Pd ensemble size and abundance. These differences in Pd ensembles influence catalytic reactivity in H 2 –D 2 isotope exchange and benzaldehyde hydrogenation, which, in correlation with theoretical calculations, is used to elucidate the active site(s) in each reaction. To clarify how the synthetic sequence controls the formation of Pd ensembles, we combine numerical wetting calculations and molecular dynamics simulations (with a machine-learned force field) to visualize Pd deposition and migration on the nanoparticle surface, respectively. Our results suggest that the nanoparticle–support interface restricts nanoparticle accessibility to Pd deposition, which consequently controls the Pd ensemble size, illustrating the critical role of nanoscale wetting phenomena during bimetallic catalyst preparation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions

Predicting the binding affinity of ligands to protein pockets is key in the drug design pipeline. The flexibility of ligand-pocket motifs arises from a range of attractive and repulsive electronic interactions during binding. Accurately accounting for all interactions requires robust quantum-mechanical (QM) benchmarks, which are scarce for ligand-pocket systems. Additionally, disagreement between “gold standard” Coupled Cluster (CC) and Quantum Monte Carlo (QMC) methods casts doubt on many benchmarks for larger non-covalent systems. We introduce the “QUantum Interacting Dimer” (QUID) benchmark framework containing 170 non-covalent (non-)equilibrium systems modeling chemically and structurally diverse ligand-pocket motifs. Symmetry-adapted perturbation theory shows that QUID broadly covers non-covalent binding motifs and energetic contributions. Robust binding energies are obtained using complementary CC and QMC methods, achieving agreement of 0.5 kcal/mol. The benchmark data analysis reveals that several dispersion-inclusive density functional approximations provide accurate energy predictions, though their atomic van der Waals forces differ in magnitude and orientation. Contrarily, semiempirical methods and empirical force fields require improvements in capturing non-covalent interactions (NCIs) for out-of-equilibrium geometries. The wide span of NCIs, highly accurate interaction energies, and analysis of molecular properties take QUID beyond the “gold standard” for QM benchmarks of ligand-protein systems.

Puleva, Mirela [University of Luxembourg, Luxembou↗
DOE OSTI2019

An electrostatic spectral neighbor analysis potential for lithium nitride

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li 3 N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li 3 N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm –1 K –1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.

36 MATERIALS SCIENCE↗
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 OSTI2024

Molecular transport enhancement in pure metallic carbon nanotube porins

Nanofluidic channels impose extreme confinement on water and ions, giving rise to unusual transport phenomena strongly dependent on the interactions at the channel–wall interface. Yet how the electronic properties of the nanofluidic channels influence transport efficiency remains largely unexplored. Here we measure transport through the inner pores of sub-1 nm metallic and semiconducting carbon nanotube porins. We find that water and proton transport are enhanced in metallic nanotubes over semiconducting nanotubes, whereas ion transport is largely insensitive to the nanotube bandgap value. Molecular simulations using polarizable force fields highlight the contributions of the anisotropic polarizability tensor of the carbon nanotubes to the ion–nanotube interactions and the water friction coefficient. We also describe the origin of the proton transport enhancement in metallic nanotubes using deep neural network molecular dynamics simulations. Finally, these results emphasize the complex role of the electronic properties of nanofluidic channels in modulating transport under extreme nanoscale confinement.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Molecular Simulation of MoS 2 Exfoliation

A wide variety of two-dimensional layered materials are synthesized by liquid-phase exfoliation. Here we examine exfoliation of MoS 2 into nanosheets in a mixture of water and isopropanol (IPA) containing cavitation bubbles. Using force fields optimized with experimental data on interfacial energies between MoS 2 and the solvent, multimillion-atom molecular dynamics simulations are performed in conjunction with experiments to examine shock-induced collapse of cavitation bubbles and the resulting exfoliation of MoS 2 . The collapse of cavitation bubbles generates high-speed nanojets and shock waves in the solvent. Large shear stresses due to the nanojet impact on MoS 2 surfaces initiate exfoliation, and shock waves reflected from MoS 2 surfaces enhance exfoliation. Structural correlations in the solvent indicate that shock induces an ice VII like motif in the first solvation shell of water.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Computing inelastic neutron scattering spectra from molecular dynamics trajectories

Abstract Inelastic neutron scattering (INS) provides a weighted density of phonon modes. Currently, INS spectra can only be interpreted for perfectly crystalline materials because of high computational cost for electronic simulations. INS has the potential to provide detailed morphological information if sufficiently large volumes and appropriate structural variety are simulated. Here, we propose a method that allows direct comparison between INS data with molecular dynamics simulations, a simulation method that is frequently used to simulate semicrystalline/amorphous materials. We illustrate the technique by analyzing spectra of a well-studied conjugated polymer, poly(3-hexylthiophene-2,5-diyl) (P3HT) and conclude that our technique provides improved volume and structural variety, but that the classical force field requires improvement before the morphology can be accurately interpreted.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamics of the water-plasma interface in various discharge modes of atmospheric-pressure plasmas

This paper presents an experimental study of the dynamics of the water-plasma interface during the interaction high-voltage (HV) atmospheric pressure discharge with the water surface. Under the influence of the voltage applied to the pin type electrode, discharges are formed at the air layer between the sharp-tip HV electrode and deionized water. Three discharge regimes were identified from synchronized electrical waveforms and imaging: (i) a weak linear regime at 3–10.6 kV, (ii) a branching streamer regime at the maximum applied voltage of 12.6 kV, and (iii) a continuous arc-like regime occurring during the transition to lower voltage and higher current (e.g., U ≈ 3.2 kV, I ≈ 4.07 mA). High-speed shadowgraph images showed a symmetric interfacial cavity whose depth increased nonlinearly with voltage from h 0 ≈ 0.1 mm at 3 kV to h 0 ≈ 2.7 mm at 10.6 kV, reaching a maximum depth of h 0 ≈ 5.9 mm at 12.6 kV, while the cavity disappeared in the continuous-channel regime and was replaced by outward-propagating wave-like motion. The deformation of the water surface during the discharge-water interaction is governed by the balance between electric field forces, surface tension, gravitational forces, and electrohydrodynamic forces, whose relative contributions vary with the applied voltage. This effect was explained by quantitatively estimating the magnitudes of the acting forces and establishing the force balance. Optical emission spectroscopy in the continuous-channel regime indicated air plasma signatures and yielded a gas temperature of approximately 400 K, while prolonged operation (≈ 20 min) increased the water temperature to ~ 70 °C, reduced the water-layer thickness from 6 mm to 3 mm, and decreased pH from 7 to 4. These regime-resolved, quantitative results clarify how the dominant interfacial forcing shifts with discharge mode and provide a mechanistic basis for controlling plasma-water interactions in atmospheric-pressure applications.

atmospheric pressure plasma↗
DOE OSTI2026

Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics

In this work, we evaluate the biomolecular dynamics behaviors when conventionally iterating between all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations over multiple cycles. We implemented the workflow to iterate between AA and CG in OpenMM, namely the iterative multiscale MD (iMMD) simulation workflow. In particular, we aim to identify practical applications for iterating between AA and CG simulations in a conventional manner without any constraints or model modifications. We evaluate the iMMD workflow on four representative systems, spanning folding of two soluble proteins and protein-protein as well as protein-lipid interactions of two membrane proteins. We observe that iteration between AA and CG representations could help the soluble proteins exit undesirable metastable states to fold, resulting from random protein structural distortions due to cycling. Consequently, the most reliable use of iterative AA and CG simulations appears to be to accelerating complex lipid mixing for membrane-bound protein systems rather than sampling protein conformational space. Our work explores the practical usages and limitations for iterative AA and CG simulations using readily available AA and CG force fields. The evaluated iMMD workflow in OpenMM is made available at https://github.com/lanl/iMMD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Full structural ensembles of intrinsically disordered proteins from unbiased molecular dynamics simulations

Abstract Molecular dynamics (MD) simulation is widely used to complement ensemble-averaged experiments of intrinsically disordered proteins (IDPs). However, MD often suffers from limitations of inaccuracy. Here, we show that enhancing the sampling using Hamiltonian replica-exchange MD (HREMD) led to unbiased and accurate ensembles, reproducing small-angle scattering and NMR chemical shift experiments, for three IDPs of varying sequence properties using two recently optimized force fields, indicating the general applicability of HREMD for IDPs. We further demonstrate that, unlike HREMD, standard MD can reproduce experimental NMR chemical shifts, but not small-angle scattering data, suggesting chemical shifts are insufficient for testing the validity of IDP ensembles. Surprisingly, we reveal that despite differences in their sequence, the inter-chain statistics of all three IDPs are similar for short contour lengths (< 10 residues). The results suggest that the major hurdle of generating an accurate unbiased ensemble for IDPs has now been largely overcome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

First principles Monte Carlo simulations of unary and binary adsorption: CO 2 , N 2 , and H 2 O in Mg-MOF-74

Dative bonding of adsorbate molecules onto coordinatively-unsaturated metal sites in metal–organic frameworks can lead to unique adsorption selectivities. However, the distortion of the electron density during dative bonding poses a challenge for force-field-based simulations. Here in this paper, we report first principles Monte Carlo simulations with the PBE-D3 functional for the adsorption of CO 2 , N 2 , and H 2 O in Mg-MOF-74, and obtain accurate predictions of the unary isotherms without any of the adjustments or fitting often required for systems with strong adsorption sites. Lastly, simulations of binary CO 2 /N 2 and H 2 O/CO 2 mixtures yield selectivities of 200 and 160, respectively, and indicate that predictions from ideal adsorbed solution theory need to be viewed with caution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Predicting the preferred morphology of hexagonal boron nitride domain structure on nickel from ReaxFF-based molecular dynamics simulations

An understanding of the nucleation and growth of hexagonal boron nitride (hBN) on nickel substrates is essential to its development as a functional material. In particular, fundamental insights into the formation of the hexagonal lattices with alternating boron (B) and nitrogen (N) atoms could be exploited to control hBN lattice morphologies for targeted applications. Here, the preferred shapes and edge configurations of atomically smooth hBN on Ni(111) were investigated using molecular dynamics (MD) simulations, along with reactive force field (ReaxFF) developed to represent the Ni/B/N system and the lattice-building B–N bond formation. The obtained hBN lattices, from different B : N feed ratios, are able to confirm that hBN domain geometries can indeed be tuned by varying thermodynamic parameters (i.e., chemical potentials of N and B) – a finding that has only been predicted using quantum mechanical theories. Here, we also showed that the nitrogen fed to the system plays a more crucial role in dictating the size of hBN lattices. With an increase of the relative N content, the simulated hBN domain shapes also transition from equilateral triangles to hexagons, again, consistent with the anticipation based on Density Functional Theory (DFT) calculations. Hence, a plausible approach to acquire a desired hBN nanostructure depends on careful control over the synthesis conditions, which now can benefit from reliable molecular simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗