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At least 199 records · Page 11

Understanding the Space Weathering of Mercury Through Laboratory Experiments

Introduction: Airless surfaces across the solar system are continually modified by energetic particles from solar wind and micrometeoroid bombardment [1,2]. This process is known as space weathering, and it alters the chemical, microstructural, and optical properties of surface regoliths on airless bodies, including Mercury. On the Moon and S-type asteroids, the reflectance spectral signatures of space weathering include reddening (increasing reflectance with increasing wavelength), darkening (lowering of reflectance), and the attenuation of characteristic absorption bands [2]. Such spectral changes are driven by the production of Fe-bearing nanoparticles(npFe) through both solar wind irradiation and micrometeoroid bombardment. While our understanding of space weathering for the Moon and near-Earth S-types asteroids is advanced, insight into how these processes operate on other planetary bodies is limited. In particular, Mercury experiences a uniquely intense space weathering environment than planetary counterparts at 1 AU, including a moreintense solar wind flux and higher velocity micrometeoroid impacts [4]. Additionally, Mercury has a surface composition unique in the inner solar system, including regions of the surface with very low albedo known as the low reflectance material (LRM), which is enriched in carbon, likely graphite, up to 4wt.% [5].In addition, the concentration of Fe across Mercury’s surface islow (<2 wt.%) compared to the Moonor S-type asteroids asteroids[6]. Our understanding of the effects of space weathering on C-rich and Fe-poor phases is limited. Since Fe plays a critical role inthe development of space weathering characteristicson other airless surfaces(e.g., npFe), its limited availability may significantly affect the development of space weathering features in Mercury surface materials. We can simulate space weathering processes in the laboratory to explore their effects on the microstructural, chemical, and spectral characteristics of Mercury surface materials[7]. Here we used pulsed laser irradiation to simulate the short duration, high-temperature events associated with micrometeoroid impacts. We performed coordinated analyses including reflectance spectroscopy and electron microscopy to investigate the spectral, chemical, and microstructural changes in these mercurian analog samples. Methods: For these experiments, we usedforsteritic olivine with varying FeOcontents, a mineral phase proposed to be abundant on the surface of Mercury. We mixed each sample with graphite to simulate LRM regions of the surface. Wesynthesized the olivinesamplesat 1-bar at NASA’s Johnson Space Centerand prepared pressed powder pellets for laser irradiation [8].We prepared three samples, each with a base layer of olivineto maintain structural integrity and topped witha surface layer containing the graphite-olivine mixture: 1) Sample SC-001 San Carlos olivine(Fo90.91),2)Sample F-S-002 with0.05 wt.% FeO olivine, and 3) F-T-004 with 0.53 wt.% FeO olivine. Each sample was mixed with 5 wt.% powdered graphite and had grain sizes ranging from 45to 125μm. We irradiated each sample usinga pulsed Nd-YAG laser, (l=1064 nm, ~6 ns pulse duration, energy of 48 mJ/pulse) while undervacuumat Northern Arizona University. The laser was rastered1x and then 5x over the surfaceof each sample to simulate progressive space weathering. We collected in situreflectance spectra from the samples after each laser pulse witha Nicolet IS50 Fourier-Transform Infrared spectrometer (lfrom 0.65-2.5 μm). We used an FEI Nova NanoSEM200scanning electron microscope (SEM) and a Hitachi TM4000 Plus benchtop SEM at Purdue University to image the surface morphology and topography of the samples. We extracted thin sections for analysis in the transmission electron microscope(TEM)using the FEI Helios NanoLab 660 focused ion beam (FIB) SEM at the University of Arizona.We performed analysis of the microstructural and chemical characteristics of the samples using the 200 keV JEOL 2500 scanning TEM at Johnson Space Center. Reflectance Spectroscopy Results:Reflectance spectra for each sample are shown in Fig. 1.SC-001:The spectrum of the unirradiated sample exhibits a weak 1.0 μm absorption feature, associated with Fe2+in the olivine,and low overall reflectance (Fig. 1a). Thereflectance and the depth of the absorption band increases after 1x laser raster but are at their lowest after 5x laser rasters.F-T-004:The unirradiated sample has a blue-sloped spectrum with low reflectance without identifiable absorption features (Fig. 1b). With progressive laser irradiation, the sample reflectance increases and becomes strongly red-sloped.F-S-002:The unirradiated sample exhibits a dark, blue-slopedspectrum. The brightness of the sample increases significantly from <0.2average reflectance over >0.8 reflectance in the most irradiated sampleand thespectral slope also becomesslightly reddened (Fig. 1c). Microstructural and Chemical Analysis: Two primary alteration textures were observed in the samples exposed to simulated space weathering: 1) fluffy C-rich,and 2) vesiculated melt. The fluffy C-rich texture is composed oflow-densitydeposits distributed across the surface of the sample(Fig. 2A). Analysis of a FIB section extracted from a low-density C-rich region in sample SC-001 reveals multiple globule-type deposits, discrete from stacked graphite, likely produced via melting from the laser irradiation [9].The vesiculated melt textureis smooth and uniformly distributed across isolated regions of the sample surface. The vesicles measure up to 100s of nmin diameter. Analysis of a FIB section from this texture was extracted from sample F-T-004 reveals a layer of amorphous melt material, close to 100 nm thick and uniform across the FIB section (Fig. 2B). Isolated regions of this melt layer contain small nanoparticles, <5 nm in diameter. Chemical analysis through energy dispersive X-ray spectroscopy reveals the composition of this layer is enriched in Si and depleted in Mg and O compared to the underlying sample. Implications for Space Weathering on Mercury: Previous experiments simulating space weathering of Mercury have showndarkening and reddening of spectra[7,10].However, our use of low-Fe materials and graphite to create a sample set more analogous to the mercurian surface. Our results indicate that sample composition plays a significant and important role in the space weathering of Mercury. In particular, our spectral data demonstrates a strong correlation between spectral slope, Fe content, and simulated space weathering. While the variation in FeO content between samples F-S-002 and F-T-004 is <0.6 wt.%, the spectra deviate from flat to strongly red-sloped(F-T-004). This reddeningmay be linked to the presence of very small nanoparticles observed in the melt textures extracted from sample F-T-004. For the SC-001 sample, the fluffy C-rich textures may be developed by the amalgamation of small graphite particles into these unique morphologies. Such observations indicate that space weathering on Mercury may result in both familiar and new microstructural and chemical characteristics. References: [1]Hapke B. (2001) J. Geophys. Res.-Planet.,106,10039–10073. [2]Pieters C.M. and Noble S.K. (2016) J. Geophys. Res-Planet., 121, 1865–1884. [3] Lucey P.G., and Riner, M.A. (2011) Icarus,212, 451-462.[4]CintalaM.J.(1992)J. Geophys. Res.-Planet.,97,947–973.[5]Klima R.L.et al.(2018)Geophys.Res.Letters, 45, 2945–2953. [6]Nittler L.R., et al. (2011) Science 333, 1847-1850.[7]Sasaki S. and Kurahashi E. (2004) Space weathering on Mercury, Adv.Space Res., 33, 2152-2155.[8] Vander KaadenK.E., et al. (2018) LPSCXLIX, Abstract 1230. [9] McGlaun M.L. et al. (2019) LPSCL, Abstract 2019. [10] TrangD.et al. (2018)LPSCXLIX,Abstract2083

M S Thompson↗

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.

59 BASIC BIOLOGICAL SCIENCES↗

Cryo-EM sample preparation for high-resolution structure studies

High-resolution structures of biomolecules can be obtained using single-particle cryo-electron microscopy (SPA cryo-EM), and the rapidly growing number of structures solved by this method is encouraging more researchers to utilize this technique. As with other structural biology methods, sample preparation for an SPA cryo-EM data collection requires some expertise and an understanding of the strengths and limitations of the technique in order to make sensible decisions in the sample-preparation process. Here, in this article, common strategies and pitfalls are described and practical advice is given to increase the chances of success when starting an SPA cryo-EM project.

36 MATERIALS SCIENCE↗

Large Scale Study of Ligand–Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition for Reduced-Order Modeling of S n Radiation Transport

Deterministic solutions to the Sn radiation transport equation can be computationally expensive to calculate. Reduced-order modeling enables efficient approximation of the full-order model (FOM) solution. We propose a novel method for constructing reduced-order models (ROMs) of the S n radiation transport equation, offline maximizing minimally invasive (OMMI) proper orthogonal decomposition (POD). POD uses the method of snapshots to create a reduced-order basis for constructing an ROM. Minimally invasive POD leverages the sweep infrastructure existing in deterministic transport codes to create a POD-based ROM, even when infeasible by traditional methods. Offline maximizing minimally invasive proper orthogonal decomposition (OMMI-POD) extends minimally invasive POD by performing sweeps offline, therefore maximizing the potential speedup. OMMI-POD does so by creating a library of reduced systems from a training set. This library of reduced systems is then interpolated to provide a rapid approximate solution of the S n radiation transport equation. The model is evaluated on a set of test problems, achieving a low error with a 466 times speedup over the FOM. Also presented is a study of the effect of sampling method on the performance of OMMI-POD, specifically comparing naive uniform sampling to the more accurate and computationally expensive greedy sampling.

97 MATHEMATICS AND COMPUTING↗

Thermodynamic Basis for the Stabilization of Helical Peptoids by Chiral Sidechains

Peptoids are a class of highly customizable biomimetic foldamers that retain properties from both proteins and polymers. It has been shown that peptoids can adopt peptide-like secondary structures through the careful selection of sidechain chemistries, but the underlying conformational landscapes that drive these assemblies at the molecular level remain poorly understood. Given the high flexibility of the peptoid backbone, it is essential that methods applied to study peptoid secondary structure formation possess the requisite sensitivity to discriminate between structurally similar yet energetically distinct microstates. In this work, a generalizable simulation scheme is used to robustly sample the complex folding landscape of various 12mer polypeptoids, resulting in a predictive model that links sidechain chemistry with preferential assembly into one of 12 accessible backbone motifs. Using a variant of the metadynamics sampling method, four peptoid dodecamers are simulated in water: sarcosine, N-(1-phenylmethyl)glycine (Npm), (S)-N-(1-phenylethyl)glycine (Nspe), and (R)-N-(1-phenylethyl)glycine (Nrpe)–to determine the underlying entropic and energetic impacts of hydrophobic and chiral peptoid sidechains on secondary structure formation. Our results indicate that the driving forces to assemble Nrpe and Nspe sequences into polyproline type-I helices in water are found to be enthalpically driven, with small benefits from an entropic gain for isomerization and steric strain due to the presence of the chiral center. The minor entropic gains from bulky chiral sidechains in Nrpe- and Nspe-containing peptoids can be explained through increased configurational entropy in the cis state. However, overall assembly into a helix is found to be overall entropically unfavorable. Furthermore, these results highlight the importance of considering the many various competing interactions in the rational design of peptoid secondary structure building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Using intrusive approaches as a step towards accounting for stochasticity in wind turbine design

Current wind turbine design methods require tens of thousands of time-domain simulations and use different random seeds to account for the stochasticity of the environmental conditions. The account of stochasticity is nonintrusive because the sampling method calls a deterministic model multiple times without changing its underlying equations. In this work, we investigate and demonstrate using simple proof of concepts how intrusive approaches can be used to directly account for stochasticity in the equations representing a mechanical system. Our long term goal is to apply such methodology to the design of wind turbines without requiring an excessive number of simulations. Intrusive methods manipulate stochastic variables directly to provide the probability density functions (PDFs) of the states and outputs at any time as functions of the PDFs of the inputs. We illustrate how different methods can be used with a reduced-order model of a wind turbine with one degree of freedom and for linear and nonlinear models. We discuss how the methods can be extended and what it will take to apply them to a level of fidelity similar to current state-of-the-art wind turbine design tools.

17 WIND ENERGY↗

The Role of Remote Sensing in Assessing Forest Biomass in Appalachian South Carolina

Information is presented on the use of color infrared aerial photographs and ground sampling methods to quantify standing forest biomass in Appalachian South Carolina. Local tree biomass equations are given and subsequent evaluation of stand density and size classes using remote sensing methods is presented. Methods of terrain analysis, environmental hazard rating, and subsequent determination of accessibility of forest biomass are discussed. Computer-based statistical analyses are used to expand individual cover-type specific ground sample data to area-wide cover type inventory figures based on aerial photographic interpretation and area measurement. Forest biomass data are presented for the study area in terms of discriminant size classes, merchantability limits, accessibility (as related to terrain and yield/harvest constraints), and potential environmental impact of harvest.

Shain, W.↗

Tropospheric HO determination by FAGE

In the measurement of tropospheric HO we have employed three low-pressure laser-excited fluorescence (LEF) experimental systems. These instruments operate by expanding the ambient air flow via a nozzle, followed by transit down a flowtube through a detection region traversed by the excitation laser beam. This sampling method we named FAGE (fluorescence assay with gas expansion). The instruments employed a hydrocarbon reagent, added below the nozzle, to remove HO for background measurement. In the second and third instruments, air sampling via parallel nozzles and tubes, with reagent addition alternating between two channels, permitted continuous signal measurement with simultaneous measurement of background. The first two instruments (FAGE1 and FAGE2) used 282 nm HO excitation by frequency-doubled tunable dye lasers, pumped by pulsed Nd:YAG lasers at 10-30 Hz repetition rate. The third instrument (FAGE3) uses 308 nm excitation in which the dye laser is pumped by a copper vapor laser, pulsed at 5600 Hz.

Hard, Thomas M.↗

Structural system reliability calculation using a probabilistic fault tree analysis method

The development of a new probabilistic fault tree analysis (PFTA) method for calculating structural system reliability is summarized. The proposed PFTA procedure includes: developing a fault tree to represent the complex structural system, constructing an approximation function for each bottom event, determining a dominant sampling sequence for all bottom events, and calculating the system reliability using an adaptive importance sampling method. PFTA is suitable for complicated structural problems that require computer-intensive computer calculations. A computer program has been developed to implement the PFTA.

Torng, T. Y.↗

Sampling Versus Filtering in Large-Eddy Simulations

A LES formalism in which the filter operator is replaced by a sampling operator is proposed. The unknown quantities that appear in the LES equations originate only from inadequate resolution (Discretization errors). The resulting viewpoint seems to make a link between finite difference approaches and finite element methods. Sampling operators are shown to commute with nonlinearities and to be purely projective. Moreover, their use allows an unambiguous definition of the LES numerical grid. The price to pay is that sampling never commutes with spatial derivatives and the commutation errors must be modeled. It is shown that models for the discretization errors may be treated using the dynamic procedure. Preliminary results, using the Smagorinsky model, are very encouraging.

Debliquy, O.↗

Phosphate Textural Diversity in CI Chondrites and C-Type Asteroids

Introduction: Apatite, Ca 5 (PO 4 ) 3 (CL/F/OH-), is a ubiquitous phosphate found throughout the solar system, including the most primitive solids, CI-chondrites [1,2] and related samples of carbonaceous asteroids Ryugu [3] and Bennu [4], returned by JAXA’s Hayabusa2 and NASA’s OSIRIS-REx missions, respectively. Apatite in these primitive solids is found as individual grains or mineral clusters and has been inferred to form from the hydrothermal sequence during cooling of their respective parent body or bodies. To better understand the formation history and reworking of these early phosphates we have undertaken a highly coordinated study of phosphate microstructures, geochemistry and U/Pb geochronology from a suite of CI meteorites and carbonaceous asteroid samples. These data have identified novel phosphate microstructures and complex relationships across a suite of apatite grains from the early solar system, indicating multiple episodes of growth and modification on the CI parent body(ies). Methods: Samples of CI chondrites, Alais, Ivuna, Orgueil, Oued Chebeika 002, Yamato (Y) 82162, Y980115, Y980134, and two chips of Hayabusa2 Ryugu particles (A0262 and C0263) have been acquired for analysis. The bulk texture of the samples were first scanned by X-ray Computed Tomography (XCT) using the Nikon XT H 320 within the XFACT facility at NASA JSC or the Xradia 620 Versa at the UTCT facility. Based on the identification of petrofabrics or features of interest from the XCT data, samples were chipped, oriented, potted in epoxy and thick sections were prepared. After anhydrously polishing the samples with silicon carbide and dry diamond powder down to 1 µm, the samples were ion polished using a Hitachi ArBlade. Energy dispersive Xray spectrometry (EDS) maps were collected to ID phosphates of interest using a JEOL 7900F SEM. The internal microstructures of identified phosphates were then mapped by electron backscatter diffraction (EBSD) using the JEOL 7900F. Based on the EDS and EBSD data, domains of interest were targeted for quantitative chemical analyses using a JEOL 8530 EPMA. Subsequently, in situ U-Pb and 207 Pb/ 206 Pb ages will be collected using a Cameca ims1290 secondary ion mass spectrometer at UCLA across a range of microstructures. Results: Apatite grains are ubiquitous throughout the CI chondrites and carbonaceous asteroid materials, found as individual grains, disseminated clusters or grain aggregates. Apatite grains are associated with serpentine, magnet-ite, and/or carbonate. Of particular interest, we have identified individual and aggregate polycrystalline grains ex-hibiting an internal ‘honeycomb’ texture (Fig. 1). Some of these grains appear overprinted by subsequent alteration while others remain unaltered. Apatite halogen sites are dominated by the missing component, assumed to be OH, and F, comparable to published values from Bennu, Ryugu and CM-chondrites [3-5]. However, the Yamato CI-like meteorites show a broader range of Cl values, and one grain from Alais is dominated by CL. Summary: Apatite growth features indicate a protracted and complex growth history, consistent with precipitation from an evolving fluid system. The ‘honeycomb’ texture identified in some CI meteorites is, to the best of our knowledge, the first report of such a microstructure in meteoritic phosphate. The microtextures and zoning will guide subsequent age analyses, to better constrain the formation and reworking of phosphate in CI(-like) materials. Acknowledgments: We thank the National Institute of Polar Research, Japan for samples of Y82162, 980115 and 980134 meteorites, ASU’s Buseck Center for Meteorite Studies for samples of Ivuna and Alais meteorites, and JAXA curation for chips of Ryugu material. This work was funded by NASA ROSES LARS grant 24-LARS24-0014. References: [1] Morlok et al., 2016, GCA 70:5371-5394. [2] Alfin g et al., 2019, Geochemistry 79:125532. [3] Nakamura et al. (2022) Science 379:1-15. [4] Seifert et al. (2026) MAPS 61:504-521. [5] Piralla et al. (2021) MAPS 56:809-828.

CI chondrites↗

Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation

ABSTRACT Strong gravitational lensing has emerged as a promising approach for probing dark matter (DM) models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhaloes through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhaloes. We demonstrate that our method is capable of harnessing the statistical power of multiple subhaloes (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of DM perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.

Astronomy & Astrophysics↗

Tensor networks for High Energy Physics: contribution to Snowmass 2021

Tensor network methods are becoming increasingly important for high-energy physics, condensed matter physics and quantum information science (QIS). We discuss the impact of tensor network methods on lattice field theory, quantum gravity and QIS in the context of High Energy Physics (HEP). These tools will target calculations for strongly interacting systems that are made difficult by sign problems when conventional Monte Carlo and other importance sampling methods are used. Further development of methods and software will be needed to make a significant impact in HEP. We discuss the roadmap to perform quantum chromodynamics (QCD) related calculations in the coming years. The research is labor intensive and requires state of the art computational science and computer science input for its development and validation. We briefly discuss the overlap with other science domains and industry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Molten Salt Sampling Techniques and Analytical Approaches

Recent global interest in pyroprocessing and molten salt reactors has brought salt sampling methods and techniques back to the forefront of nuclear safeguards concerns. Issues with uranium supplies have also encouraged various countries to pursue advanced nuclear fuel cycles. Tracking nuclear material in molten salt has proven to be a challenge and updating molten salt sampling will greatly help in this endeavor. Molten salt is problematic to sample due to salt stratification, lack of homogeneity, solids, and difficulty with hot cell adaptations. Various salt sampling techniques have been used since before the 1960s including surface, spoon/spatula, and bar solidification. Since then, new types of sampling techniques have been developed to improve sampling results. These include rod/dip, pipet, suction, filtered sampling along with devices such as the Valve Core Sampler and the Multi-Level Sampler. These different approaches are being analyzed and improved upon along with developing requirements for an improved salt sampling device. Work continues to develop salt samplers that are more robust, easier to segment, collect at a specific depth, can work with filters, and can collect fines. Sampling parameters are also being narrowed in terms of stirring, settling time, filtration, depth, etc. In the future, we hope to address deficiencies for process control and nuclear material accountancy control by determining the best way to collect samples that minimizes contaminants and is representative. A compilation of salt sampling approaches, analyses techniques, and an evaluation of findings will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗

Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential

Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive ab-initio molecular dynamics simulations. Here, we present a deep neural network potential trained using accurate ab-initio data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the pK a , as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of ab-initio methods is required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗