Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “molecular geometry”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Gas Permeation and Separation Characteristics of Microporous TpHz COF Membranes Synthesized by Substrate-Assisted Interfacial Polymerization

Microporous two-dimensional covalent organic framework (2D COF) membranes offer promise for gas separation applications, but their gas transport mechanism remains unclear. In this study, a TpHz 2D COF membrane supported on a macroporous nylon substrate is prepared by substrate-assisted interfacial polymerization under mild conditions. The formation of a continuous and dense thin (~300 nm thick) TpHz layer is confirmed by scanning electron microscopy and Fourier transform infrared spectroscopy. Characterization by X-ray diffraction, grazing incidence wide-angle X-ray scattering, and N 2 porosimetry qualitatively reveals the microstructures of the supported TpHz membranes, i.e., they comprise partially oriented 2D COF lamellar crystallites with moderate crystallinity in an eclipsed (AA) stacking geometry, centering the effective membrane pore size distribution at ~1.1 nm. Further, single gas permeation data show that the transport of common molecular gases, including H 2 , He, CH 4 , N 2 , and CO 2 , through the synthesized TpHz membranes follows the Knudsen transport mechanism, where single gas permeance decreases with an increasing molecular weight and permeation temperature. Binary gas separation results show that in the equimolar CO 2 /N 2 mixture, the presence of the CO 2 surface flow slightly hinders the N 2 flow at room temperature due to the reduced membrane channel size by the adsorbed CO 2 gas layer on TpHz’s pore wall. In contrast, permeation of the equimolar CH 4 /N 2 binary mixture does not exhibit a discernible surface flow of both gases due to their much lower gas uptake on TpHz, and their transport mechanism follows Knudsen-like behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

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

Cu site differentiation in tetracopper(I) sulfide clusters enables biomimetic N 2 O reduction

Copper clusters feature prominently in both metalloenzymes and synthetic nanoclusters that mediate catalytic redox transformations of gaseous small molecules. Such reactions are critical to biological energy conversion and are expected to be crucial parts of renewable energy economies. However, the precise roles of individual metal atoms within clusters are difficult to elucidate, particularly for cluster systems that are dynamic under operating conditions. Here, we present a metal site-specific analysis of synthetic Cu 4 (μ 4 -S) clusters that mimic the Cu Z active site of the nitrous oxide reductase enzyme. Leveraging the ability to obtain structural snapshots of both inactive and active forms of the synthetic model system, we analyzed both states using resonant X-ray diffraction anomalous fine structure (DAFS), a technique that enables X-ray absorption profiles of individual metal sites within a cluster to be extracted independently. Using DAFS, we found that a change in cluster geometry between the inactive and active states is correlated to Cu site differentiation that is presumably required for efficient activation of N 2 O gas. More precisely, we hypothesize that the Cu δ+ ∙∙∙Cu δ- pairs produced upon site differentiation are poised for N 2 O activation, as supported by computational modeling. These results provide an unprecedented level of detail on the roles of individual metal sites within the synthetic cluster system and how those roles interplay with cluster geometry to impact the reactivity function. We expect this fundamental knowledge to inform understanding of metal clusters in settings ranging from (bio)molecular to nanocluster to extended solid systems involved in energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Materials Data on MgH20C4Br2(N4O3)2 by Materials Project

MgC4H4(N2O3)2(NH2)4(H2)2(H2Br)2 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of eight ammonia molecules, four hydrogen molecules, four molecular hydrogen;hydrobromide molecules, and two MgC4H4(N2O3)2 clusters. In each MgC4H4(N2O3)2 cluster, Mg2+ is bonded in an octahedral geometry to six O2- atoms. There are a spread of Mg–O bond distances ranging from 1.97–2.23 Å. There are two inequivalent C4+ sites. In the first C4+ site, C4+ is bonded in a 1-coordinate geometry to two N3- and one O2- atom. Both C–N bond lengths are 1.33 Å. The C–O bond length is 1.26 Å. In the second C4+ site, C4+ is bonded in a single-bond geometry to one O2- atom. The C–O bond length is 1.17 Å. There are two inequivalent N3- sites. In the first N3- site, N3- is bonded in a distorted single-bond geometry to one C4+ atom. In the second N3- site, N3- is bonded in a distorted single-bond geometry to one C4+ atom. There are two inequivalent H1+ sites. In the first H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the second H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. There are three inequivalent O2- sites. In the first O2- site, O2- is bonded in a bent 120 degrees geometry to one Mg2+ and one C4+ atom. In the second O2- site, O2- is bonded in a bent 150 degrees geometry to one Mg2+ and one C4+ atom. In the third O2- site, O2- is bonded in a distorted trigonal planar geometry to one Mg2+ and two H1+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on ScH16I3O8 by Materials Project

ScH10O7IH2OHI1IH3 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of four hydriodic acid molecules, four molecular hydrogen;hydroiodide molecules, four water molecules, and four ScH10O7I clusters. In each ScH10O7I cluster, Sc3+ is bonded in a 7-coordinate geometry to seven O2- atoms. There are a spread of Sc–O bond distances ranging from 1.97–2.35 Å. There are ten inequivalent H1+ sites. In the first H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the second H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the third H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the fourth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the fifth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the sixth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the seventh H1+ site, H1+ is bonded in a single-bond geometry to one O2- and one I1- atom. The H–O bond length is 1.00 Å. The H–I bond length is 2.42 Å. In the eighth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the ninth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.97 Å. In the tenth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. There are seven inequivalent O2- sites. In the first O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the second O2- site, O2- is bonded in a single-bond geometry to one Sc3+, one H1+, and one O2- atom. The O–O bond length is 1.48 Å. In the third O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the fourth O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the fifth O2- site, O2- is bonded in a 1-coordinate geometry to one Sc3+ and one O2- atom. In the sixth O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and one H1+ atom. In the seventh O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. I1- is bonded in a 1-coordinate geometry to one H1+ atom.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on ZnH12(NO3)2 by Materials Project

ZnNHO3(H2)5HNO3 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of sixteen hydrogen molecules, four molecular hydrogen molecules, four nitric acid molecules, and four ZnNHO3 clusters. In each ZnNHO3 cluster, Zn2+ is bonded in a distorted linear geometry to one H1+ and one O2- atom. The Zn–H bond length is 1.51 Å. The Zn–O bond length is 1.89 Å. N1- is bonded in a trigonal planar geometry to three O2- atoms. There are a spread of N–O bond distances ranging from 1.23–1.37 Å. H1+ is bonded in a single-bond geometry to one Zn2+ atom. There are three inequivalent O2- sites. In the first O2- site, O2- is bonded in a bent 120 degrees geometry to one Zn2+ and one N1- atom. In the second O2- site, O2- is bonded in a single-bond geometry to one N1- atom. In the third O2- site, O2- is bonded in a single-bond geometry to one N1- atom.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A molecular dynamics survey of grain boundary energy in uranium dioxide and cerium dioxide

Uranium dioxide (UO 2 ) is the primary fuel material that is used in current nuclear reactors. As one of the most fundamental material parameters, grain boundary (GB) energy strongly influences many fuel properties, and the influences depend on the characters and properties of individual GBs. Using molecular dynamics simulations, a high throughput survey of GB energy in UO 2 was carried out for the purpose of elucidating the roles of GB geometry such as misorientation and inclination, as well as the bonding nature of UO 2 , in affecting GB energy. GB energies in CeO 2 were calculated as well for comparison with UO2toinvestigate the generality of GB energy anisotropy in fluorite phase oxides. The results show significant GB energy anisotropy in both UO 2 and CeO 2 that is associated with the cubic symmetry of the fluorite structure. More interestingly, the GB anisotropy is found to be dependent not only on the crystal structure but also the ionic bonding. As such, the GB energy anisotropy in fluorite oxides has significant differences compared with that in fcc metals. We report that the data obtained and the increased knowledge on GB anisotropy will facilitate GB engineering for nuclear fuels with improved properties

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

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

Methane Adsorption on Heteroatom-Modified Maquettes of Porous Carbon Surfaces

Experimental and theoretical studies disagree on the energetics of methane adsorption on carbon materials. However, this information is critical for the rational design and optimization of the structure and composition of adsorbents for natural gas storage. The delicate nature of dispersion interactions, polarization of both the adsorbent and the adsorbate, interplay between H-bonding and tetrel bonding, and induced dipole/Coulomb interactions inherent to methane physisorption require computational treatment at the highest possible level of theory. In this study, we employed the smallest reasonable computational model, a maquette of porous carbon surfaces with a central site for substitution and methane binding. The most accurate predictions of methane adsorption energetics were achieved by electron-correlated molecular orbital theory CCSD(T) and hybrid density functional theory MN15 calculations employing a saturated, all-electron basis set. Here, the characteristic geometry of methane adsorption on a carbon surface (“lander approach”) arises due to bonding interactions of the adsorbent π-system with the proximal H–C bonds of methane, in addition to tetrel bonding between the antibonding orbital of the distal C–H bond and the central atom of the maquette (C, B, or N). The polarization of the electron density, structural deformations, and the comprehensive energetic analysis clearly indicate a ∼3 kJ mol –1 preference for methane binding on the N-substituted maquette. The B-substituted maquette showed a comparable or lower binding energy than the unsubstituted, pure C model, depending on the level of theory employed. The calculated thermodynamic results indicate a strategy for incorporating electron-enriched substitutions (e.g., N) into carbon materials as a way to increase methane storage capacity over electron-deficient (e.g., B) modifications. The thermochemical analysis was revised for establishing a conceptual agreement between the experimental isosteric heat of adsorption and the binding enthalpies from statistical thermodynamics principles.

03 NATURAL GAS↗
DOE OSTI2024

Comparative Pore Structure and Dynamics for Bacterial Microcompartment Shell Protein Assemblies in Sheets or Shells

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria that encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins, analogous to many membrane-bound organelles. The shell proteins assemble into multimeric tiles; hexamers, trimers, and pentamers, and these tiles self-assemble into larger assemblies with icosahedral symmetry. While icosahedral shells are the predominant form in vivo , the tiles can also form nanoscale cylinders or sheets. The individual multimeric tiles feature central pores that are key to regulating transport across the protein shell. Our primary interest is to quantify pore shape changes in response to alternative component morphologies at the nanoscale. We used molecular modeling tools to develop atomically detailed models for both planar sheets of tiles and curved structures representative of the complete shells found in vivo . Subsequently, these models were animated using classical molecular dynamics simulations. From the resulting trajectories, we analyzed the overall structural stability, water accessibility to individual residues, water residence time, and pore geometry for the hexameric and trimeric protein tiles from the Haliangium ochraceu m model BMC shell. These exhaustive analyses suggest no substantial variation in pore structure or solvent accessibility between the flat and curved shell geometries. We additionally compare our analysis to hydroxyl radical footprinting data to serve as a check against our simulation results, highlighting specific residues where water molecules are bound for a long time. Although with little variation in morphology or water interaction, we propose that the planar and capsular morphology can be used interchangeably when studying permeability through BMC pores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Self-assembly of a triply periodic continuous mesophase with Fddd symmetry in simple one-component liquids

Triply periodic continuous morphologies (networks) arising as a result of the microphase separation in block copolymer melts have so far never been observed self-assembled in systems of particles with spherically symmetric interaction. Here, we report a molecular dynamics simulation where two simple one-component liquids form upon cooling an equilibrium network with the Fddd space group symmetry. This complexity reduction in the liquid network formation in terms of the particle geometry and the number of components evidences the generic nature of this class of phase transition suggesting opportunities for producing these structures in a variety of new systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Capturing Ultrafast Electron Driven Chemical Reactions in Molecules

The goals of this project are to develop experimental tools to investigate the dynamics of dissociative electron attachment (DEA) reactions in real time. In a DEA reaction, the attachment of a low energy electron to a molecule results in bond breaking and the fragmentation of the molecule. A new instrument has been designed and is under construction to perform time-resolved photoelectron and photo-fragment spectroscopy following electron transfer from an iodide anion to a molecule within a mass-selected molecular dimer. Iodide binds only weakly through the dipole interaction with many polar molecules, and such molecules forming a dimer with iodide undergo only small changes from the equilibrium geometry in isolation. When the electron is transferred from the iodide, the iodine atom is no longer bound, so the electron transfer step can be considered analogous to electron attachment and brings the key advantage of ultrafast timing precision. The experiments proposed here will establish the experimental tools to interrogate the dynamics of dissociative electron attachment on the fundamental timescales of the nuclear motion, by simultaneously probing the valence electronic structure and the fragmentation pathways.

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

Atomic Diffusivities of Yttrium, Titanium and Oxygen Calculated by Ab Initio Molecular Dynamics in Molten 316L Oxide-Dispersion-Strengthened Steel Fabricated via Additive Manufacturing

Oxide-dispersion-strengthened (ODS) steels have long been viewed as a prime solution for harsh environments. However, conventional manufacturing of ODS steels limits the final product geometry, is difficult to scale up to large components, and is expensive due to multiple highly involved, solid-state processing steps required. Additive manufacturing (AM) can directly incorporate dispersion elements (e.g., Y, Ti and O) during component fabrication, thus bypassing the need for an ODS steel supply chain, the scale-up challenges of powder processing routes, the buoyancy challenges associated with casting ODS steels, and the joining issues for net-shape component fabrication. In the AM process, the diffusion of the dispersion elements in the molten steel plays a key role in the precipitation of the oxide particles, thereby influencing the microstructure, thermal stability and high-temperature mechanical properties of the resulting ODS steels. In this work, the atomic diffusivities of Y, Ti, and O in molten 316L stainless steel (SS) as functions of temperature are determined by ab initio molecular dynamics simulations. The latest Vienna Ab initio Simulation Package (VASP) package that incorporates an on-the-fly machine learning force field for accelerated computation is used. At a constant temperature, the time-dependent coordinates of the target atoms in the molten 316L SS were analyzed in the form of mean square displacement in order to obtain diffusivity. The values of the diffusivity at multiple temperatures are then fitted to the Arrhenius form to determine the activation energy and the pre-exponential factor. Given the challenges in experimental measurement of atomic diffusivity at such high temperatures and correspondingly the lack of experimental data, this study provides important physical parameters for future modeling of the oxide precipitation kinetics during AM process.

Chemistry↗
DOE OSTI2026

Rational design of heterogeneous single-site catalysts via surface organometallic chemistry

Single-site heterogeneous catalysts offer an attractive route to unite the molecular precision of homogeneous catalysis with the durability and practical advantages of solids. Surface organometallic chemistry (SOMC) provides a particularly powerful strategy for this purpose by grafting molecular precursors onto tailored surfaces and converting support functionalities into ligand environments for isolated metal centers. As a result, SOMC brings the language and logic of coordination chemistry to heterogeneous catalysis, where the support becomes an integral part of the active site coordination sphere. This Review surveys recent progress in the rational design of SOMC-derived single-site catalysts, with emphasis on synthetic routes, post synthetic transformations, and the deliberate tuning of catalytic behavior through metal-support interactions. Discussions are made on how support identity, hydroxyl topology, acidity, and redox activity shape the geometry, electronic structure, and oxidation state of supported metal sites, as well as how these factors determine activity, selectivity, and stability. We also examine a central limitation of these systems: despite their molecularly informed design, supported single sites often exist as structurally distributed ensembles rather than uniform species, particularly on amorphous supports. This site heterogeneity, along with catalyst dynamics under operating conditions, remains a major barrier to definitive structure-activity relationships. Therefore, emerging approaches that combine advanced characterization, first-principles modeling, ensemble kinetics, and machine learning to resolve active-site structure and guide catalyst development are highlighted. Together, these advances position SOMC as a versatile coordination chemistry framework for the predictive design of heterogeneous catalysts with well-defined molecularly tailored active sites.

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

First operation and validation of simulations for the divertor cryo-vacuum pump in Wendelstein 7-X

Ten cryo-vacuum pumps (CVPs) were installed in the subdivertor region of each island divertor in the stellarator Wendelstein 7-X (W7-X) and operated for the first time during the recently completed plasma campaign OP2.1. A pumping speed of 70 ± 1 $\frac{m^3}{s}$ was measured during dedicated tests with known hydrogen gas injection. Based on a conductance model, the estimated pumping speed ranges from 86-93 $\frac{m^3}{s}$ for different sticking coefficients between 0.6 and 0.8. After completion of the initial tests the CVPs were operated successfully throughout the campaign, with regeneration performed once a week. Neutral gas pressures in the subdivertor in the range of 10 −4 mbar are well within the molecular flow regime and limit the particle exhaust capabilities of the CVPs. Simulations of the neutral gas pressure in the three-dimensional complex geometry of the subdivertor were performed using the DIVGAS code based on the direct simulation Monte Carlo method and a model implemented in the steady-state thermal package in ANSYS, which are in agreement with the measured values during plasma operation.

Cryo-vacuum pumping↗
DOE OSTI2022

A Microporous Metal–Organic Framework Incorporating Both Primary and Secondary Building Units for Splitting Alkane Isomers

We demonstrate the assembly of a mononuclear metal center, a hexanuclear cluster, and a V-shaped, trapezoidal tetracarboxylate linker into a microporous MOF featuring an unprecedented 3-nodal (4,4,8)-c lyu topology. Here, the compound, HIAM-302, represents the first example that incorporates both primary building unit (PBU) and hexanuclear secondary building unit (SBU) in one structure, which should be attributed to the desymmetrized geometry of the organic linker. HIAM-302 possesses optimal pore dimensions and can separate mono-branched and di-branched alkanes through selective molecular sieving, which is of significant value in petrochemical industry.

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

Structural studies of the IFNλ4 receptor complex using cryoEM enabled by protein engineering

Abstract IFNλ4 has posed a conundrum in human immunology since its discovery in 2013, with its expression linked to complications with viral clearance. While genetic and cellular studies revealed the detrimental effects of IFNλ4 expression, extensive structural and functional characterization has been limited by the inability to express and purify the protein, complicating explanations of its paradoxical behavior. In this work, we report a method for robust production of IFNλ4. We then use yeast surface display to affinity-mature IL10Rβ and solve the 72 kilodalton structures of IFNλ4 (3.26 Å) and IFNλ3 (3.00 Å) in complex with their receptors IFNλR1 and IL10Rβ using cryogenic electron microscopy. Comparison of the structures highlights differences in receptor engagement and reveals a distinct 12-degree rotation in overall receptor geometry, providing a potential mechanistic explanation for differences in cell signaling, downstream gene induction, and antiviral activities. Further, we perform a structural analysis using molecular modeling and simulation to identify a unique region of IFNλ4 that, when replaced, enables secretion of the protein from cells. These findings provide a structural and functional understanding of the IFNλ4 protein and enable future comprehensive studies towards correcting IFNλ4 dysfunction in large populations of affected patients.

Science & Technology - Other Topics↗
DOE OSTI2024

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗