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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

X-ray Scattering and Coarse-Grained Simulations for Clustering and Interactions of Monoclonal Antibodies at High Concentrations

Attractive protein-protein interactions (PPI) in concentrated monoclonal antibody (mAb) solutions may lead to reversible oligomers (clusters) that impact colloidal stability and viscosity. Herein, the PPI are tuned for two mAbs via the addition of arginine (Arg), NaCl, or ZnSO 4 as characterized by the structure factor ( S eff (q)) with small angle x-ray scattering (SAXS). The SAXS data are fit with molecular dynamics simulations by placing a physically relevant short-ranged attractive interaction on selected beads in coarse-grained 12 bead models of the mAb shape. The optimized 12-bead models are then used to differentiate key microstructural properties, including center of mass radial distribution functions ( g COM (r)), coordination numbers and cluster size distributions (CSD). The addition of co-solutes results in more attractive Seff(q) relative to the no co-solute control for all systems tested, with the most attractive systems showing an upturn at low q . Only the All1 model with an attractive site in each Fab and Fc region (possessing Fab-Fab, Fab-Fc and Fc-Fc interactions) can reproduce this upturn, and the corresponding CSDs show the presence of larger clusters compared to the control. In general, for models with similar net attractions, i.e., second osmotic virial coefficients, the size of the clusters increases as the attraction is concentrated on a smaller number of evenly distributed beads. Here, the cluster size distributions from simulations are used to improve the understanding and prediction of experimental viscosities. The ability to discriminate between models with bead interactions at particular Fab and Fc bead sites from SAXS simulations, and to provide real space properties (CSD and g COM (r)), will be of interest in engineering protein sequence and formulating protein solutions for weak PPI to minimize aggregation and viscosities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor

Inhibition of the SARS-CoV-2 main protease (M pro ) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of M pro . Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC 50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for M pro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Strategy for Increasing the Efficiency of the Oxygen Reduction Reaction in Mn-Doped Cobalt Ferrites

Alkaline fuel cells have drawn increasing attention as next-generation energy-conversion devices for electrical vehicles, since high pH enables the use of non-precious-metal catalysts. Herein, we report on a family of rationally designed Mn-doped cobalt ferrite (MCF) spinel nanocrystals, with an optimal composition Mn 0.8 (CoFe 2 ) 0.73 O 4 (MCF-0.8), that are effective electrocatalysts for the oxygen reduction reaction. MCF-0.8 exhibits a half-wave potential (E 1/2 ) of 0.89 V vs RHE in 1 M NaOH, only 0.02 V less than that of commercial Pt/C under identical testing conditions and, to the best of our knowledge, one of the highest recorded values in the literature. Moreover, MCF-0.8 exhibits remarkable durability (ΔE 1/2 = 0.014 V) after 10 000 electrochemical cycles. In situ X-ray absorption spectroscopy (XAS) reveals that the superior performance of the trimetallic MCF-0.8 originates from the synergistic catalytic effect of Mn and Co, while Fe helps preserve the spinel structure during cycling. We employed in situ XAS to track the evolution of the oxidation states and the metal–oxygen distances not only under constant applied potentials (steady state) but also during dynamic cyclic voltammetry (CV) (nonsteady state). The periodic conversion between Mn(III, IV)/Co(III) and Mn(II, III)/Co(II) as well as the essentially constant oxidation state of Fe during the CV suggests collaboration efforts among Mn, Co, and Fe. Mn and Co serve as the synergistic coactive sites to catalyze the oxygen reduction, apparently resulting in the observed high activity, while Fe works to maintain the integrity of the spinel structure, likely contributing to the remarkable durability of the catalyst. Furthermore, these findings provide a mechanistic understanding of the electrocatalytic processes of trimetallic oxides under real-time fuel cell operating conditions. This approach provides a new strategy to design high-performance non-precious-metal electrocatalysts for alkaline fuel cells.

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

Accurate energies for ππ* excited states via exchange scaling: the XS-CASSCF method

The state-averaged complete-active space self-consistent field method (SA-CASSCF) is a widely employed electronic structure method used for studying photochemistry and dynamics owing to its ability to provide a reliable description even of complicated cases while still retaining computational efficiency. However, SA-CASSCF suffers from one Achilles heel, related to the description of ionic ππ* excited states, whose energy is often overestimated by 1–2 eV. In light of this challenge, we present the XS-CASSCF method, a new approach based on the idea of exchange scaling (XS) that screens the involved energy terms to improve the excitation energies of singlet ionic ππ* states. First, we illustrate the power of the XS-CASSCF method using hexatriene and para-quinodimethane as examples, showing that it corrects the targeted ionic states while leaving the other states largely unaffected, giving root-mean-square errors (RMSE) below 0.2 eV for the four lowest states in both cases. Subsequently, XS-CASSCF vertical excitation energies are tested against theoretical best estimates for a set of 11 molecules and 56 excited states. XS-CASSCF performs exceptionally well for the ππ* states of hydrocarbons, reducing the RMSE over 21 excitation energies from 0.96 to 0.27 eV. In the challenging subset of molecules with heteroatoms and a larger number of ππ* and nπ* states, we find that improvements can also be obtained, albeit not as pronounced. We conclude with an outlook into more realistic molecular materials focusing on their singlet–triplet (S 1 /T 1 ) gaps, finding that significant improvements can be obtained along the whole range of S 1 /T 1 gaps studied, going from 0.1 eV to more than 1.5 eV. Owing to notable improvements across significant classes of molecules combined with its conceptual simplicity, we believe that XS-CASSCF is a promising addition to the electronic structure toolbox, serving both as a standalone electronic structure method and as a starting point for further correlated treatment.

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

Numerical Prediction of Oscillation Behaviors of a Multiphase Core–Shell Droplet During Interfacial Tension Measurement

Interfacial tension between molten high-temperature materials is to be measured using the electrostatic levitation furnace (ELF) and the electromagnetic levitator (ISS-EML) aboard the International Space Station. A levitated compound droplet of a concentric core–shell structure is excited either by an impulsive electromagnetic field or by a superimposed electrostatic field. The oscillation behavior of the compound droplet is analyzed to measure interfacial tension at the interface of the two phases. In support of the space experiments, a computational fluid dynamic model was developed to characterize the oscillation behavior of multiphase core–shell droplet. The developed model predicted the interfacial tension between molten copper-rich and cobalt-rich phases with a difference of 3.3 pct compared to the values reported in literature. The developed model is being utilized to investigate the influence of various test parameters on measured surface tension and also being extended for molten steel–molten slag systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Macroscale Control of Reactivity using 3D Printed Materials with Intrinsic Catalytic Properties

The morphology of heterogeneous catalysts can impact their performance. However, standard manufacturing methods like extrusion or pelleting offer little options for tailoring catalyst shape. Herein, stereolithographic 3D printing is used to produce catalysts with controlled topologies to enhance their performance. A series of magnetic stir-bar compartments (SBC) were 3D printed and tested as catalysts for sucrose hydrolysis. The SBC were printed using acrylic acid (AA) and 1,6-hexanediol diacrylate (HDDA) as acid sites and hydrophobic crosslinking domains, respectively. Variations in the number and tilt direction of the SBC blades produced significant changes in their apparent catalytic activities. These changes resulted from differences in the fraction of active surface effectively interacting with the reactants in solution, as revealed by computational fluid dynamics simulations. Moreover, varying HDDA:AA ratios in SBC regulated reactant-surface interactions to control catalytic activity. Overall, 3D printing catalysts enables quick performance optimization by simultaneously controlling macroscopic structure and molecular composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Self-assembly of aramid amphiphiles into ultra-stable nanoribbons and aligned nanoribbon threads

Small molecule self-assembly is an established route for producing high surface area nanostructures with readily customizable chemistries and precise molecular organization. However, these structures are fragile, exhibiting molecular exchange, migration, and rearrangement - among other dynamic instabilities - and ultimately disassociate upon drying. Here we show a small molecule platform, the aramid amphiphile (AA), that overcomes these dynamic instabilities by incorporating a Kevlar-inspired domain into the molecular structure. Strong, anisotropic interactions between AAs suppress molecular exchange and elicit spontaneous self-assembly in water to form nanoribbons with lengths of up to 20 microns. The nanoribbons have a Young’s modulus of 1.7 GPa and tensile strength of 1.9 GPa. Here, we exploit this stability to extend small molecule self-assembly to hierarchically ordered macroscopic materials outside of solvated environments. Through an aqueous shear alignment process, we organize AA nanoribbons into arbitrarily long flexible threads that support 200 times their weight when dried. Tensile tests of the threads provide a benchmark for Young’s moduli (between ~400 and 600 MPa) and extensibilities (between ~0.6 and 1.1%) that depend on the counterion chemistry in solution. This bottom-up approach to macroscopic materials could benefit solid-state applications, historically inaccessible by self-assembled nanomaterials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Tutorial on the Synthesis and Validation of a Closed-Loop Wind Farm Controller Using a Steady-State Surrogate Model

In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbines. However, this demands an accurate and computationally tractable model of the wind farm dynamics. This problem calls for a closed-loop solution. This tutorial paper fills the scientific gap by demonstrating the full closed-loop controller synthesis cycle using a steady-state surrogate model. Furthermore, a novel, computationally efficient and modular communication interface is presented that enables researchers to straight-forwardly test their control algorithms in large-eddy simulations. High-fidelity simulations of a 9-turbine farm show a power production increase of up to 11% using the proposed closed-loop controller compared to traditional, greedy wind farm operation.

17 WIND ENERGY↗
DOE OSTI2017

Molecular Dynamics Simulation of Resin Adsorption at Kaolinite Edge Sites: Effect of Surface Deprotonation on Interfacial Structure

Low-salinity water flooding, a method of enhanced oil recovery, consists of injecting low ionic strength fluids into an oil reservoir in order to detach oil from mineral surfaces in the underlying formation. Although highly successful in practice, the approach is not completely understood at the molecular scale. Molecular dynamics simulations have been used to investigate the effect of surface protonation on the adsorption of an anionic crude oil component on clay mineral edge surfaces. A set of interatomic potentials appropriate for edge simulations has been applied to the kaolinite (010) surface in contact with an aqueous nanopore. Decahydro-2-napthoic acid in its deprotonated form (DHNA – ) was used as a representative resin component of crude oil, with monovalent and divalent counterions, to test the observed trends in low-salinity water flooding experiments. Surface models include fully protonated (neutral) and deprotonated (negative) edge sites, which require implementation of a new deprotonation scheme. The surface adsorptive properties of the kaolinite edge under neutral and deprotonated conditions have been investigated for low and high DHNA – concentrations with Na + and Ca 2+ as counterions. The tendency of DHNA – ions to coordinate with divalent (Ca 2+ ) rather than monovalent (Na + ) ions greatly influences adsorption tendencies of the anion. Additionally, the formation of net positively charged surface sites due to Ca 2+ at deprotonated sites results in increased DHNA – adsorption. Divalent cations such as Ca 2+ are able to efficiently bridge surface sites and organic anions. Replacing those cations with monovalent cations such as Na + diminishes the bridging mechanism, resulting in reduced adsorption of the organic species. As a result, a clear trend of decreased DHNA – adsorption is observed in the simulations as Ca 2+ is replaced by Na + for deprotonated surfaces, as would be expected for oil detachment from reservoir formations following a low-salinity flooding event.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Nanoscale Miscibility in In Situ Polymerized Hybrid Electrolytes Speeds Up Ion Dynamics and Enables Stable Cycling of Li Metal Batteries

While the potential use of copolymerized electrolytes in Li metal batteries is subject to intense investigation, the fundamental understanding of the nanoscale domain formation and its effect on Li + transport is still lacking. In this study, we investigated the correlation between the Li + transport mechanism and the miscibility of monomers in polymer blend electrolytes derived from the in situ copolymerization of methyl methacrylate (MMA) and vinylene carbonate (VC) in the presence of polyethylene glycol dimethyl ether (PEGDME) plasticizer and bis(trifluoromethanesulfonyl)imide (LiTFSI) salt. The addition of a polar short chain plasticizer reduced the dynamic and structural heterogeneities of the electrolyte. Small-angle X-ray scattering (SAXS) measurements and coarse-grained molecular dynamics (MD) simulations were used to investigate the nanoscale structure of the electrolytes. The distribution of relaxation times corresponding to the three distinct diffusion mechanisms of the free and interfacial Li + ions at the copolymer/plasticizer and electrolyte/SEI boundaries was analyzed in a broad temperature range to elucidate the Li + transport mechanism. Furthermore, the chemical composition of the SEI and the contribution of a ceramic lithium lanthanum zirconium oxide (LLZO, Li 7 La 3 Zr 2 O 12 ) phase on the interfacial resistance, salt degradation, and SEI stability were studied by X-ray photoelectron spectroscopy (XPS) depth profile analysis and electrochemical testing.

Li metal↗
DOE OSTI2021

Novel 2D velocity estimation method for large transient events in plasmas

Dynamics of fast transient events are challenging to be analyzed with high time resolution. Such events can occur in fusion plasmas such as the filaments during edge-localized modes (ELMs). Here, we present a robust method—the spatial displacement estimation—for estimating the displacements of structures with fast dynamics from high spatial and time resolution imaging diagnostics [e.g., gas-puff imaging (GPI)] with sampling time temporal resolution. First, a background suppression method is shown, which suppresses the slowly time-evolving and spatially non-uniform background in the signal. In the second step, a two-dimensional polynomial trend subtraction method is presented to tackle the remaining polynomial order trend in the signal. After performing these pre-processing steps, the spatial displacement of the propagating structure is estimated from the two-dimensional spatial cross-correlation coefficient function calculated between consecutive frames. The method is tested for its robustness and accuracy by simulated Gaussian events and spatially displaced random noise. An example application of the method is presented on propagating ELM filaments measured by the GPI system on the National Spherical Torus Experiment spherical tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Design and Analysis of an Integrated Additively Manufactured Test Article for Plasma-Facing Components

Current plasma-facing components (PFCs) used in helium-cooled divertor modules are complex structures with tungsten tile, steel sleeve components, and cartridges, all assembled in a helium-cooled multiple jet (HEMJ) structure. The goal of this project is to simplify the complex PFC design using additive manufacturing techniques to create a single integrated tungsten test article. Apart from the flexibility this opens up in exploring a wide array of geometries for the article, having a single integrated article significantly reduces the number of joints and parts in the article, thus reducing chances of leaks. A process called electron beam melting has shown to produce very high-density samples and unique geometries, enabling HEMJ or similar designs. To validate and optimize this novel design, the model underwent a series of computational fluid dynamics and finite element analysis simulations to replicate steady-state heat flux in the divertors. The simulations presented in this study consider a steady-state base heat flux of 5 MW/m 2 , with water serving as the coolant. Future research will explore the use of helium as a coolant, simulate edge-localized-mode conditions, and include experimental validation. Since 3D-printed tungsten is anisotropic, the build direction versus build plane of the article are taken into consideration for the test article strength. Because of the high operating temperatures and low ductility of tungsten, thermal creep and brittle fracture are important failure mechanisms to consider. In conclusion, the cap is evaluated with various flow velocities and nozzle diameters, and an optimal design choice is made for which this cap will survive the divertor conditions with a conservative safety margin.

Divertor↗
DOE OSTI2024

Pretest Modeling A Spent Nuclear Fuel Seismic Shake Test

The U.S. Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is planning to conduct a series of full-scale shake table tests to simulate hypothetical earthquake conditions and record the response of surrogate spent nuclear fuel (SNF) assemblies in a dry canister storage system mockup. The shake table motions will represent a range of hypothetical earthquake conditions at hypothetical locations in the continental U.S. to generally define the range of mechanical loads that SNF can be expected to experience during extended dry storage periods. This paper describes the pretest predictions made with LS-DYNA models of the mockup storage systems. The test will use two dry storage system configurations, a mockup vertical concrete cask (VCC) and a mockup horizontal storage module (HSM). The test will use a production-quality canister and basket. Within the canister will be four instrumented fuel assemblies with fuel rods containing surrogate mass and 28 instrumented dummy assemblies that are intended to match the mass and outer dimension of a fuel assembly. The finite element models include models of the VCC and HSM on the shake table to calculate the system level dynamic responses and separate single fuel assembly models to calculate stress and strain in fuel assembly components. Both types of models include nonlinear behavior like rod-to-rod contact and the ability for VCC’s to rock and slide. This paper presents the expected response of the VCC, HSM, and fuel assemblies to the shake table testing that is planned to start in April of 2024. The earthquake conditions represent seismic hazards in the 2,000-to-20,000-year return period range. The test data is expected to confirm the expectation that fuel rod cladding will remain intact, fuel assembly structural components like guide tubes will remain intact, and no significant VCC sliding or tipping will occur in the range of conditions to be tested with the shake table.

Klymyshyn, Nicholas A.↗
DOE OSTI2022

Implementation and testing of physics-based pulverization model in BISON

This report summarizes lower length scale computational research conducted to improve the pulverization criterion for high-burnup UO 2 fuel in the BISON fuel performance code. This research was sponsored by the NEAMS program during FY22. Efforts to improve the model primarily focus on calculating the current pressure of bubbles in the high-burnup structure (HBS) region, as well as the critical bubble pressure at which pulverization occurs in the HBS. The phase-field model for predicting initial bubble pressure in the HBS was improved by implementing a more realistic model for defect production, and by coupling the phase-field model for inter-granular bubble evolution with the spatially-resolved cluster dynamics code Xolotl to simulate intra-granular fission gas evolution. An evolution equation for current bubble pressure was added to BISON, an improvement over the previous model that assumed that bubble pressure was static following HBS formation. To improve calculations of critical bubble pressure for pulverization, 3-D phase-field fracture simulations were performed, and a function for critical bubble pressure was fit—based on the simulation results—to replace the previous function, which had been determined using 2-D simulations. The impacts of these modifications to the existing pulverization assessment cases will be reported in the forthcoming engineering scale milestone on high-burnup UO 2 pulverization. A poromechanics-based approach was used to include the effect of bubble over pressurization on the stress state of the pellet at the engineering scale, and the initial strategy for integration of the pulverization criterion with pellet-scale mechanical degradation using a smeared cracking model was developed. This report also describes the initial research that was conducted to inform a forthcoming transient fission gas release (tFGR) model in BISON. Focusing on the fission gas release caused by pulverization of the outer pellet rim, a model for the amount of fission gas release was developed as a function of fuel porosity, bubble size, and fragment size. Implementation of this model in BISON and its impact of engineering-scale predictions will also be described in the aforementioned engineering-scale milestone on high-burnup UO 2 pulverization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Pion and kaon structure functions at an electron-ion collider

Pions and kaons are, along with protons and neutrons, the main building blocks of nuclear matter. They are connected to the Goldstone modes of dynamical chiral symmetry breaking, the mechanism thought to generate all hadron mass in the visible universe. The distribution of the fundamental constituents, the quarks and gluons, is expected to be different in pions, kaons, and nucleons. However, experimental data are sparse. As a result, there has been persistent doubt about the behaviour of the pion's valence quark structure function at large Bjorken-$x$ and virtually nothing is known about the contribution of gluons. The Electron-Ion Collider (EIC) with an acceptance optimized for forward physics could provide access to structure functions over a larger kinematic region. This would allow for measurements testing if the origin of mass is encoded in the differences of gluons in pions, kaons, and nucleons, and measurements that could serve as a test of assumptions used in the extraction of structure functions and the pion and kaon form factors. Furthermore, measurements at an EIC would also allow to explore the effect of gluons at high x.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗