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At least 325 records · Page 18

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DOE OSTI2025

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

Metal–organic frameworks (MOFs) offer promise as selective CO 2 sorbents, but successful MOF sorbent materials need high CO 2 binding affinity and selectivity for CO 2 over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO 2 adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO 2 affinity. A preliminary high-throughput screening of over 900 anion-pillared MOFs was performed using rigid UFF+DDEC6 force fields to predict zero-loading heats of adsorption for CO 2 and H 2 O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO 2 heat of adsorption and experimental relevance. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies and forces, with an iterative sampling scheme combining molecular dynamics, geometry optimization, random geometric insertion, and NVT Monte Carlo-based configuration generation to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included, contrasting with previous models that approximated the MOF as rigid. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations with the MLFF produced CO 2 adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO 2 sorption by models with rigid structures. Bond and angle histogram analysis showed that MOF flexibility increased the variance of fluorine–fluorine diagonal distances at adsorption sites, resulting in a lower predicted sorption for flexible, asymmetric SIFSIX-3-Cu pore geometries compared to the rigid, symmetric DFT-optimized SIFSIX-3-Cu pore geometry. A detailed description of flexibility afforded by the MLFF resulted in an accurately predicted CO 2 uptake (0.88 mmol/g) at low pressure (40 Pa) compared to the experimentally measured value (1.24 mmol/g). In conclusion, these results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

adsorption↗

DOE OSTI2023

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Driving Force Dependent Photoinduced Charge Transfer Dynamics in Polymer-Wrapped Semiconducting Single-Walled Carbon Nanotubes

Here, we investigate the thermodynamic driving-force dependences of photoinduced charge separation (CS) and subsequent charge transfer dynamics in single-walled carbon nanotube (SWNT)–perylenediimide (PDI) donor–acceptor (D–A) superstructures. Pump–probe spectroscopy reveals that [SWNT (•+)n ]-(PDI –• ) n CS states form on an ∼100 fs time scale following photoexcitation; these dynamics are invariant across an ∼400 mV driving force range, indicating that SWNT hole polaron formation time scales are determined by nanotube lattice and solvent relaxation. These CS states feature SWNT hole polarons adjacent to (geminate) and nearby (nongeminate) PDI radical anions. Analysis of the free energy dependence for charge recombination (CR) of [SWNT •+ ] geminate -(PDI –• ) CS states highlights an ∼2 meV value for D–A electronic coupling (HAB) and ∼0.93 eV for the total reorganization energy (λ T ). A corresponding driving force dependence of the CR dynamics for [SWNT •+ ] nongeminate -(PDI –• ) CS states indicates a diminished H AB value (∼0.6 meV) and a larger λ T (∼1.1 eV), consistent with larger transfer distances. SWNT excitons that persist following photoinduced CS drive photooxidation of PDI –• components of [SWNT (•+)n ]-(PDI –• ) n CS states ( 1 SWNT* + PDI •– → PDI + SWNT •– ); this reaction manifests a significantly reduced λ T value (∼0.67 eV) as the initially prepared SWNT reduced state bears the character of a conduction band injected electron ([SWNT •– ] CB ). This reaction thus gives rise to relaxed, nongeminate SWNT electron and hole polarons on the same nanotube; these polarons react on a 10 2 ps time scale independent of the electronic structure of these SWNT-PDI superstructures.

anions↗

DOE OSTI2022

Long‐Term Observations of Turbulence Vertical Velocity Spectra in a Convective Mixed Layer: Dependence on Land‐Surface Forcing in the U.S. Southern Great Plains

Doppler lidar vertical velocity retrievals were analyzed for the scale and structure of mixed-layer turbulence over a 7-year period, on fair-weather warm season days (442 cases) in the U.S. Southern Great Plains. Based on the hypothesized influence of land-surface forcing and updraft size on convective boundary layer clouds, a spectral analysis was performed to quantify effects of surface forcing (surface buoyancy flux, friction velocity, and evaporative fraction) on turbulence scale. Significant (order-of-magnitude) variations in spectral density were found in the energy-production subrange and mesoscale regimes. Integral scale decreased with increasing buoyancy flux (and Monin-Obukhov stability parameter), while spectral density in the energy-production subrange increased, implying a transition to buoyancy-driven cellular structures with narrower updrafts. The influence of stability parameter was limited to the neutral to convective transition, and could not explain the wide variation in spectral density in the mesoscale regime. However, high friction velocity (u* > 0.5 m s –1 ) was associated with larger integral scale (updraft width), and a greater portion of spectral density in the mesoscale regime. Lidar profiles and radar reflectivity for individual cases revealed evidence of roll and wave-like structures contributing to mesoscale variability on high friction velocity days, suggesting shear instabilities on days with wetter land surface conditions and smaller buoyancy flux. The role of friction velocity emphasizes the multivariate nature of surface influences on turbulence, and implies that variance-based turbulence closures may not be adequate for capturing effects of surface forcing on updraft size.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Leakage From Coexisting Geologic Forcing and Injection‐Induced Pressurization: A Semi‐Analytical Solution for Multilayered Aquifers With Multiple Wells

Abstract Abnormal fluid pressures (above or below hydrostatic pressure) can develop and persist in sedimentary basins. The common occurrence of abnormal pressures may cause challenges for project permitting of geological carbon sequestration (GCS), particularly in reservoirs with pre‐injection overpressure. The leaky wells that may exist in some sedimentary basins can provide flow paths between deep brine aquifers and shallower freshwater aquifers. Pre‐injection relative overpressures can cause brine leakage through leaky wells even before any injection occurs. The tendency for flow through leaky wells is coupled with the process of pressure dissipation that occurs through aquitards. Specifically, with non‐zero permeability, aquitards can dissipate pressure over large areal extents and thereby reduce leakage rates through leaky wells. This study presents development of a semi‐analytical solution for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. The geologic forcing that causes abnormal pressures in the multilayered system can coexist with any number of injection, extraction, and leaky wells that also affect fluid pressure. The semi‐analytical model is applied to explore how leakage through leaky wells varies as functions of pressurization rate, along with aquitard and leaky well properties in an overpressured multilayered system. The results show that although injection‐induced pressures can dissipate rapidly through suitably permeable aquitards, coexisting geologic forcing may create sustained rates of brine leakage into freshwater aquifers through leaky wells. In GCS, a very low‐permeability aquitard with high capillary entry pressure to free‐phase CO 2 is desired to serve as the caprock to prevent leakage of CO 2 from the storage reservoir. Nevertheless, the results from this study show that the brine leakage impact to shallow freshwater aquifers through leaky wells might substantially decrease with increasing aquitard permeability values, as long as small aquitard permeability and high capillary entry pressure serve to prevent CO 2 leakage.

Cihan, A.↗

DOE OSTI2025

Improving the QBO Forcing by Resolved Waves With Vertical Grid Refinement in E3SMv2

Abstract The quasi‐biennial oscillation (QBO) is the dominate mode of variability in the tropical stratosphere and plays an important role in stratospheric dynamics and chemistry. The QBO is notably deficient in many climate models, including the Energy Exascale Earth System Model (E3SM) developed by the US Department of Energy. In this work, we refine the lower stratospheric vertical grid spacing from roughly 1 km to 500 m to facilitate more realistic equatorial wave activity in the lower stratosphere in E3SM version 2. The refinement results in a simulated QBO with a reasonable amplitude and easterly‐westerly transition in both directions, but still has a longer period than observed, slower easterly downward propagation speed, and shallower vertical depth. Similar refinement in the multi‐scale modeling framework configuration of E3SM yields similar improvements. By analyzing the forcing contributions from different wave types, we find that most of the QBO forcing still comes from parameterized gravity wave drag from convection. The improved QBO forcing contributions from resolved waves, especially equatorial Kelvin waves and resolved small scale waves, can be attributed to the grid refinement.

E3SM↗

DOE OSTI2020

Vegetation forcing modulates global land monsoon and water resources in a CO 2 -enriched climate

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO 2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO 2 -enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions.

58 GEOSCIENCES↗

DOE OSTI2021

Ocean fronts and eddies force atmospheric rivers and heavy precipitation in western North America

Atmospheric rivers (ARs) are responsible for over 90% of poleward water vapor transport in the mid-latitudes and can produce extreme precipitation when making landfall. However, weather and climate models still have difficulty simulating and predicting landfalling ARs and associated extreme precipitation, highlighting the need to better understand AR dynamics. Here, using high-resolution climate models and observations, we demonstrate that mesoscale sea-surface temperature (SST) anomalies along the Kuroshio Extension can exert a remote influence on landfalling ARs and related heavy precipitation along the west coast of North America. Inclusion of mesoscale SST forcing in the simulations results in approximately a 40% increase in landfalling ARs and up to a 30% increase in heavy precipitation in mountainous regions and this remote impact occurs on two-week time scales. The asymmetrical response of the atmosphere to warm vs. cold mesoscale SSTs over the eddy-rich Kuroshio Extension region is proposed as a forcing mechanism that results in a net increase of moisture flux above the planetary boundary layer, prompting AR genesis via enhancing moisture transport into extratropical cyclones in the presence of mesoscale SST forcing.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets

Abstract We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Ultrafast lattice disordering can be accelerated by electronic collisional forces

In the prevalent picture of ultrafast structural phase transitions, the atomic motion occurs in a slowly varying potential energy surface determined adiabatically by the fast electrons. However, this ignores non-conservative forces caused by electron-lattice collisions, which can significantly influence atomic motion. Most ultrafast techniques only probe the average structure and are less sensitive to random displacements, and therefore do not detect the role played by non-conservative forces in phase transitions. Here we show that the lattice dynamics of the prototypical insulator-to-metal transition of VO 2 cannot be described by a potential energy alone. We use the sample temperature to control the preexisting lattice disorder before ultrafast photoexcitation across the phase transition and our ultrafast diffuse scattering experiments show that the fluctuations characteristic of the rutile metal develop equally fast (120 fs) at initial temperatures of 100 K and 300 K. This indicates that additional non-conservative forces are responsible for the increased lattice disorder. In conclusion, these results highlight the need for more sophisticated descriptions of ultrafast phenomena beyond the Born-Oppenheimer approximation as well as ultrafast probes of spatial fluctuations beyond the average unit cell measured by diffraction.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The homogenous alternative to biomineralization: Zn- and Mn-rich materials enable sharp organismal “tools” that reduce force requirements

We measured hardness, modulus of elasticity, and, for the first time, loss tangent, energy of fracture, abrasion resistance, and impact resistance of zinc- and manganese-enriched materials from fangs, stings and other “tools” of an ant, spider, scorpion and nereid worm. The mechanical properties of the Zn- and Mn-materials tended to cluster together between plain and biomineralized “tool” materials, with the hardness reaching, and most abrasion resistance values exceeding, those of calcified salmon teeth and crab claws. Atom probe tomography indicated that Zn was distributed homogeneously on a nanometer scale and likely bound as individual atoms to more than ¼ of the protein residues in ant mandibular teeth. This homogeneity appears to enable sharper, more precisely sculpted “tools” than materials with biomineral inclusions do, and also eliminates interfaces with the inclusions that could be susceptible to fracture. Based on contact mechanics and simplified models, we hypothesize that, relative to plain materials, the higher elastic modulus, hardness and abrasion resistance minimize temporary or permanent tool blunting, resulting in a roughly 2/3 reduction in the force, energy, and muscle mass required to initiate puncture of stiff materials, and even greater force reductions when the cumulative effects of abrasion are considered. We suggest that the sharpness-related force reductions lead to significant energy savings, and can also enable organisms, especially smaller ones, to puncture, cut, and grasp objects that would not be accessible with plain or biomineralized “tools”.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Constraints on fifth forces and ultralight dark matter from OSIRIS-REx target asteroid Bennu

It is important to test the possible existence of fifth forces, as ultralight bosons that would mediate these are predicted to exist in several well-motivated extensions of the Standard Model. Recent work indicated asteroids as promising probes, but applications to real data are lacking so far. Here we use the OSIRIS-REx mission and ground-based tracking data for the asteroid Bennu to derive constraints on fifth forces. Our limits are strongest for mediator masses m ~ (10 -18 -10 -17 ) eV, where we currently achieve the tightest bounds. These can be translated to a wide class of models leading to Yukawa-type fifth forces, and we demonstrate how they apply to U(1) B dark photons and baryon-coupled scalars. Our results demonstrate the potential of asteroid tracking in probing well-motivated extensions of the Standard Model and ultralight bosons near the fuzzy dark matter range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Kelvin probe force microscopy under ambient conditions

Kelvin probe force microscopy (KPFM) is a technique derived from atomic force microscopy that provides maps of surface potential or work function differences across material systems, with nanometre-scale resolution. KPFM is a useful tool for investigating electrical phenomena such as dipole orientation, interfacial charge transfer, charge accumulation, band bending and doping levels. This Primer aims to provide an overview of typical ambient-condition KPFM measurements, covering their underlying principles, experimental implementations and wide-ranging applications. Key KPFM variants, including amplitude and frequency modulation, heterodyne detection schemes and innovative open loop and pulsed force techniques, are discussed, with practical guidance on optimizing signal acquisition and reducing errors. Specialized approaches, such as time-resolved KPFM and multimodal KPFM, are discussed for their ability to capture dynamic charge processes and chemical information, respectively. Here, we highlight recent advances in KPFM applications, spanning metal alloys, soft matter, ferroelectrics, photovoltaics and 2D materials, showcasing its versatility across research domains. By addressing current limitations and identifying future opportunities, this Primer underscores the transformative potential of KPFM in advancing the understanding of nanoscale electrical phenomena.

Zahmatkeshsaredorahi, Amirhossein [Lehigh Univ., B↗

DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗

DOE OSTI2022

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. Finally, we expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Ductile adhesive elastomers with force-triggered ultra-high adhesion strength

Elastomers play a vital role in many forthcoming advanced technologies in which their adhesive properties determine materials’ interface performance. Despite great success in improving the adhesive properties of elastomers, permanent adhesives tend to stick to the surfaces prematurely or result in poor contact depending on the installation method. Thus, elastomers with on-demand adhesion that is not limited to being triggered by UV light or heat, which may not be practical for scenarios that do not allow an additional external source, provide a solution to various challenges in conventional adhesive elastomers. Herein, we report a novel, ready-to-use, ultra high-strength, ductile adhesive elastomer with an on-demand adhesion feature that can be easily triggered by a compression force. The precursor is mainly composed of a capsule-separated, two-component curing system. After a force-trigger and curing process, the ductile adhesive elastomer exhibits a peel strength and a lap shear strength of 1.2 × 10 4 N m -1 and 7.8 × 10 3 kPa, respectively, which exceed the reported values for advanced ductile adhesive elastomers. The ultra-high adhesion force is attributed to the excellent surface contact of the liquid-like precursor and to the high elastic modulus of the cured elastomer that is reinforced by a two-phase design. In conclusion, incorporation of such on-demand adhesion into an elastomer enables a controlled delay between installation and curing so that these can take place under their individual ideal conditions, effectively reducing the energy cost, preventing failures, and improving installation processes.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Investigating PVC polymer–plasticizer interactions with atomistic MD simulations and potential of mean force calculations

For this work, atomistic molecular dynamics (MD) simulations coupled with potential of mean force (PMF) calculations were employed to investigate the interactions between PVC polymer chains containing 6–20 repeating units and various plasticizers, with the goal of identifying potential replacements for the toxic plasticizer di(2-ethylhexyl)phthalate (DEHP) used in blood bags. The selected plasticizers belong to various chemical families, such as orthophthalates, citrates, adipates, and the terephthalate DEHT. Both the polymer and the plasticizers lack ionizable groups, and their interactions are primarily governed by van der Waals and electrostatic forces. A model correlating PMF profiles with interaction forces was developed and validated across polyvinyl chloride (PVC) polymers of different lengths and all investigated plasticizers. This model provides insight into how structural variations in plasticizers influence their respective PMF values. The study ranks the investigated plasticizers based on binding affinity, identifying TOTM (tris(2-ethylhexyl) trimellitate, TEHTM), BTHC (butyryl trihexyl citrate, Citroflex B-6), ATHC (acetyl trihexyl citrate, Citroflex A-6, CA-6), and DEHT (bis(2-ethylhexyl)terephthalate, DOTP/DEHTP) as promising alternatives for further investigation. This comprehensive study encompasses PVC polymers of four different lengths and 14 plasticizers, with all data averaged over 30 independent simulations. The approach provides a deeper understanding of molecular interactions, enabling the tailoring of polymer–plasticizer systems for diverse applications, including extractables and leachables, with relevance spanning materials science to biomedical engineering, and also serves as a basis for developing coarse-grained simulation protocols. Overall, this study provides valuable insights for designing safer and more efficient plasticizer substitutes and is well supported by other studies.

Shet, Sai Athmeeya G. [Sri Sathya Sai Institute of↗