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DOE OSTI2024

Low-frequency Raman active modes of twisted bilayer MoS 2

Abstract We study the low-frequency Raman active modes of twisted bilayer MoS 2 for several twist angles using a force-field approach and a parametrized bond polarizability model. We show that twist angles near high-symmetry stacking configurations exhibit stacking frustration that leads to significant buckling of the moiré superlattice. We find that atomic relaxation due to the twist is of prime importance. The periodic displacement of the Mo atoms shows the realization of a soliton network, and in turn, leads to the emergence of a number of frequency modes not seen in the high-symmetry stacking systems. Some of the modes are only seen in the XZ Raman polarization setup while others are seen in the XY setup. The symmetry of the normal modes, and how this affects the Raman tensors is examined in detail.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Liquid-to-gas transfer of sodium in a liquid cathode glow discharge

Abstract Plasma-liquid interactions have been extensively studied with a focus on the transport of reactive species from the plasma to the liquid phase and their induced liquid phase chemistry and resulting applications. While solute transfer from the liquid to the gas phase in plasmas has been widely used in analytical chemistry, the underlying processes remain relatively unexplored. We report spatially and temporally resolved absolute density measurements of sodium in a plasma with a NaCl solution cathode using two-photon absorption laser induced fluorescence (TaLIF). The observed non-linear increase in sodium density with solution conductivity is shown to correlate with droplet generation as visualized by Mie scattering. The findings are explained by droplet generation by electrospray induced by Taylor cone formation as underpinning mechanism for the introduction of sodium in the plasma. An analytical sheath model combined with a scaling law shows an increase in electric field force with solution conductivity that is consistent with the observed non-linear increase in sodium density in the plasma with solution conductivity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Understanding dynamic properties of materials using neutron spectroscopy and atomistic simulation

Recent developments in first-principles lattice dynamics and classical force field based molecular dynamics are revolutionising the field of neutron spectroscopy. Herein we present a short review of these methods, their critical role in the supporting of cutting-edge experiments, and how they are improved by matching experimental data. We begin with a brief overview of how lattice dynamics calculations can be compared to inelastic neutron scattering (INS) and molecular dynamics simulations to both INS and quasi-elastic neutron scattering (QENS). We then provide a series of exemplar applications where lattice dynamics and molecular dynamics have been used in conjunction with neutron spectroscopy to bring significant understanding to topical areas of materials science namely: (i) lattice dynamics and INS for the study of hybrid organic-inorganic perovskites (ii) lattice dynamics and INS for the study of flexible porous solids and (iii) molecular dynamics and QENS for probing molecular behaviour in zeolite catalysis. In all three cases, the understanding gained through the synergy of experiment and computation would have been significantly reduced using either in isolation. Finally, we consider the current state of the art, describing outstanding challenges and suggesting future directions in this exciting and fertile area of physical science.

36 MATERIALS SCIENCE↗

DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗

DOE OSTI2022

OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances

Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python package for modeling protein structures from pairwise distances between any atoms, and optionally, dihedral angles. Finally, we provide a user-friendly input format for harnessing modern biomolecular force fields in an easy-to-install package that can efficiently make use of multiple compute cores.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The ABACUS cosmological N -body code

We present abacus, a fast and accurate cosmological N-body code based on a new method for calculating the gravitational potential from a static multipole mesh. The method analytically separates the near- and far-field forces, reducing the former to direct 1/r 2 summation and the latter to a discrete convolution over multipoles. The method achieves 70 million particle updates per second per node of the Summit supercomputer, while maintaining a median fractional force error of 10 -5 . We express the simulation time-step as an event-driven ‘pipeline’, incorporating asynchronous events such as completion of co-processor work, input/output, and network communication. abacus has been used to produce the largest suite of N-body simulations to date, the abacussummit suite of 60 trillion particles, incorporating on-the-fly halo finding. abacus enables the production of mock catalogues of the volume and resolution required by the coming generation of cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Thickness and strain dependence of piezoelectric coefficient in BaTiO 3 thin films

We explore the thickness dependence of the converse piezoelectric coefficient (d 33 ) in epitaxial thin films of BaTiO 3 (BTO) grown on (001) SrTiO 3 substrates. Piezoresponse force microscope was performed using an atomic force microscope equipped with an interferometric displacement sensor allowing direct quantification of electromechanical coupling coefficients in BTO free from unwanted background contributions. We find that 80-nm-thick films exhibit a d 33 of ~20.5pm/V, but as the thickness is reduced, the d 33 reduces to less than 2 pm/V for a 10 nm film. To explain the atomistic origin of the effect, we performed molecular dynamics simulations with a recently developed ab initio-derived reactive force field, constructed using the ReaxFF framework. Simulations predict that under applied electric fields thin films of BaTiO 3 show an increasing thickness, with compressive strain, of the region screening the depolarization-field. This study confirms quantitatively the drop in piezoelectric performance in BTO ultrathin films and again highlights the importance of the screening mechanisms when films approach the ultrathin limits in dictating the functional behaviors.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗

DOE OSTI2024

The seventh blind test of crystal structure prediction: structure ranking methods

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol −1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Chemistry↗

DOE OSTI2021

Quantitative order-parameter measurement in lattice-mismatched AlInP using precession electron diffraction

Precession electron diffraction (PED) was used to measure the long-range order parameter in lattice-mismatched AlInP epitaxial films under investigation for solid-state-lighting applications. Both double- and single-variant films grown at 620, 650, and 680°C were analyzed in TEM cross-section. PED patterns were acquired in selected-area-diffraction mode through external microscope control using serial acquisition, which allows inline image processing. The integrated peak intensities from experimental patterns were fit using dynamical simulations of diffraction from the ordered domain structures. Included in the structure-factor calculations were mean atomic displacements of the anions (P) due to ordering, which were found by valence-force-field calculations to have a nearly linear dependence on order parameter. A maximum order parameter of S = 0.36 was measured for a double-variant specimen grown at 650°C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Large-Aperture High-Field Nb 3 Sn Quadrupole Magnets for HiLumi

The HL-LHC Inner Triplet quadrupole magnets use Nb 3 Sn superconductor for the first time in a particle accelerator. Coil peak field, forces, and stored energy density are significantly higher than in previous accelerator magnets. The original design addressed all these challenges. Lessons learned in the early phases made the final design and specifications for series magnets more robust.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Modeling protein structures from predicted contacts with modern molecular dynamics potentials: accuracy, sensitivity, and refinement

Protein structure prediction has become increasingly popular and successful in recent years. An essential step for fragment-free, template-free methods is the generation of a final three-dimensional protein model from a set of predicted amino acid contacts that are often described by interresidue pairwise atomic distances. Here we explore the use of modern, open-source molecular dynamics (MD) engines, which have been continually developed over the last three decades with all-atom Hamiltonians to model biomolecular structure and dynamics, to generate accurate protein structures starting from a set of inferred pairwise distances. Additionally, the ability of MD empirical physical potentials to correct inaccuracies in the predicted geometries is tested. We rigorously characterize the effect of modeling parameters on results, the effect of different amounts of error in the predicted distances on the final structures, and test the ability of post-processing analysis to sort the best models out of a set of statistical replicas. We find that with exact distances and with noisy distances, the method can produce excellent structural models, and that the molecular dynamics force field seems to help correct errors in distance predictions, resisting the effects of applied noise.

Davidson, Russ↗

DOE OSTI2024

Exploring Li-Ion Transport Properties of Li 3 TiCl 6 : A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li 3 TiCl 6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li 3 TiCl 6 . Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li 3 TiCl 6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

Selvaraj, Selva Chandrasekaran (ORCID:000000029023↗

DOE OSTI2023

QUESTS: Quick Uncertainty and Entropy from STructural Similarity

QUESTS provides a strategy to quantify the uncertainty and the information content (entropy) of datasets of atomistic simulations. The code implements the calculation of a descriptor, compares information contents of datasets, verifies whether given structures are contained in specific datasets, and more. This code base enables better design of training sets for machine learning force fields as well as understanding some thermodynamic properties of materials.

Schwalbe Koda, Daniel↗

DOE OSTI2025

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗

DOE OSTI2020

Optical matter machines: angular momentum conversion by collective modes in optically bound nanoparticle arrays

The creation of optically powered self-assembling nano-to-meso-scale machines that do work is a long-standing goal in photonics. We demonstrate an optical matter (OM) machine that converts the spin angular momentum (SAM) of light into orbital angular momentum (OAM) to do mechanical work. The specific OM machine we study is based on a sixfold symmetric hexagonally ordered nanoparticle array that operates as an OM “gear” that is assembled and made to rotate in a circularly polarized Gaussian beam. The rotational symmetry of the OM gear leads to a selection rule for the allowed scattering modes based on their angular momentum. Electrodynamics calculations show that the collective scattering modes with the largest angular momentum scatter strongly in the transverse direction. Simulations and experiments show that the angular momentum that accompanies the scattered light causes a “negative torque” response on the OM gear and drives a “probe” particle placed outside the OM gear around the gear in an asymmetric force field analogously to Brownian ratchets. The gear–probe OM machine concept can be expanded to applications in nanofluidics and particle sorting.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Identifying key determinants and dynamics of SARS-CoV-2/ACE2 tight interaction

SARS-CoV-2 virus, the causative agent of Covid-19, has fired up a global pandemic. The virus interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) for an invasion via receptor binding domain (RBD) on its spike protein. To provide a deeper understanding of this interaction, we performed microsecond simulations of the RBD-ACE2 complex for SARS-CoV-2 and compared it with the closely related SARS-CoV discovered in 2003. We show residues in the RBD of SARS-CoV-2 that were mutated from SARS-CoV, collectively help make the RBD anchor much stronger to the N-terminal part of ACE2 than the corresponding residues on RBD of SARS-CoV. This would result in a reduced dissociation rate of SARS-CoV-2 from human receptor protein compared to SARS-CoV. The phenomenon was consistently observed in simulations beyond 500 ns and was reproducible across different force fields. Altogether, our study adds more insight into the critical dynamics of the key residues at the virus spike and human receptor binding interface and potentially aids the development of diagnostics and therapeutics to combat the pandemic efficiently.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Building an ab initio solvated DNA model using Euclidean neural networks

Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level.

59 BASIC BIOLOGICAL SCIENCES↗