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DOE OSTI2020

Verification and Validation of MFiX-PIC

This work is aimed at verification and validation (V&V) of particle-in-cell (PIC) approach in the National Energy Technology Laboratory’s (NETL) Multiphase Flow with Interphase eXchanges (MFiX-PIC) which was officially released in April 2019. Under verification, the results from MFiX-PIC are compared with exact or analytical solutions, while experimental results are used for benchmarking under validation. The test suite consists of the following cases: Terminal velocity. Particle advection in time-varying flow field. Particle settling in a dense medium. Evaporation. Rayleigh-Taylor instability. Minimum fluidization. Mini-circulating fluidized bed. The cases reported in a format consistent with the existing Verification and Validation Manual (Musser et al., 2018) explore a wide range of flow velocities and concentration regimes. Only one case considered in this work verifies the implementation of heat and mass transfer routines in MFiX-PIC, while a few other studies are currently underway to further expand the validation space in reacting flows. This study highlights the need for a rigorous analysis to objectively quantify the effect of model parameters. In addition, there is a scarcity of high-quality experimental data in the literature suitable for validating PIC methodology. Future efforts would include performing experiments which provide results for a valuable database. While designing such experiments, particle count should be significant to have sufficient particles per parcel while maintaining an optimal parcel to cell volume ratio. Also, datasets should have objectively assessed experimental uncertainty whereby confidence interval in those measurements become more explicit.

42 ENGINEERING↗

NASA NTRS2005

High Temperature Materials Needs in NASA's Advanced Space Propulsion Programs

In recent years, NASA has embarked on several new and exciting efforts in the exploration and use of space. The successful accomplishment of many planned missions and projects is dependent upon the development and deployment of previously unproven propulsion systems. Key to many of the propulsion systems is the use of emergent materials systems, particularly high temperature structural composites. A review of the general missions and benefits of utilizing high temperature materials will be presented. The design parameters and operating conditions will be presented for both specific missions/vehicles and classes of components. Key technical challenges and opportunities are identified along with suggested paths for addressing them.

Eckel, Andrew J.↗

NASA NTRS2005

A Reliability Model to Assess Reliability of Shuttle Derived Launch Vehicle and Next Generation Vehicles

One important thrust in NASA s space exploration vision is to improve the safety and reliability of future launch vehicles. A quantitative reliability model is an essential technical tool to evaluate launch vehicle reliability and to enhance launch vehicle configuration down selection and detailed designs. A quantitative reliability model that supports NASA's thrust has been developed and implemented in an MS Excel spreadsheet. The model addresses key reliability parameters, and provides for sensitivity analysis of various vehicle designs. This paper presents the specific elements of the model and examples of selected sensitivity analysis results. Reliability input parameters in the model include the following: 1. Main propulsion element failure probability; 2. Propulsion element catastrophic failure fraction (Cf). This is defined as the ratio of probability of uncontained failures over sum of probability of uncontained failures and contained failures); 3. Engine-out design vs. no-engine-out design; 4. Engine with redlines vs. no redlines; 5. Different levels of health management system implementation; 6. Launch Escape System (LES) reliability; and 7. Power level correlation with reliability. The sensitivity results will illustrate the impact of each of the above parameters on several reliability metrics: Loss of Mission (LOM), LOV (Loss of Vehicle) and Loss of Crew (LOC).

Huang, Zhao↗

NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases [1]. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission [2]. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions.

Zwack, Mathew R.↗

NASA NTRS2019

Characterization of Low-Temperature, Co-Fired Ceramic-Manufactured Electrostatic Thruster-Closeout Report

The goal of the project was to evaluate prototypes of an experimental thruster developed by the University of Arkansas (UA), Fayetteville, AR. The design under evaluation is a radio frequency (RF) electrostatic thruster that was fabricated using the low-temperature, co-fired ceramic (LTCC) materials and fabrication process. This materials system is analogous to printed circuit board (PCB) technology with the most significant difference being that the laminate is replaced by a ceramic material and the copper layer is replaced by printed sinterable silver paste. LTCC designs are baked after fabrication and assembled to realize an entirely monolithic structure with internal conductors, vias, and cavities. In this process, the LTCC electrostatic thruster (LTCC-ET) that is the subject of the present work becomes a monolithic ceramic thruster capable of withstanding temperatures in excess of 500 ºC. The UA and NASA Marshall Space Flight Center (MSFC) jointly performed prototype testing on the LTCC-ET under a NASA Cooperative Agreement Notice (CAN) award. The LTCC-ET was tested at MSFC in May 2018 over a 1-week period. There were two goals for the test program: (1) Testing to determine the operating parameters required to create plasma ignition in the test articles. This was explored by setting a propellant flowrate and increasing RF power until plasma ignition was observed. Testing was conducted with both argon and krypton. (2) Investigate the thrust and specific impulse (Isp) performance of the thruster as a function of propellant flowrate and grid voltage. This goal was not met during the project as technical challenges in maintaining stable plasma ignition arose due to stress and heating of the RF power feed. In summary, a prototype thruster design (consisting of three packaged units) was fabricated by UA and tested for the first time under vacuum conditions at MSFC to experimentally determine basic performance metrics and functionality. It was found that the design was not sufficiently optimized or robust enough in its initial iteration to support a significant test campaign or characterization program. It was concluded that the propellant outlet channels must be reduced in size with the flowpaths adjusted to increase propellant residence time in the thruster, and that the RF connector must be replaced with a version capable of handling higher power throughput and heating. However, even in its unoptimized form, a plasma could be produced in the LTCCET, demonstrating the efficacy of the design approach. The design is especially compelling due to its low cost to manufacture and, more importantly, its scalability of size and power throughput. Low cost and scalability are also important in that additional functionalities, such as thrust vectoring and plume charge neutralization, can be integrated into future designs with minimal additional cost. This project has matured the LTCC-ET development Technology Readiness Level (TRL) from 1 to 2. The low-cost RF plasma source portion of the LTCC device was matured from TRL 2 to 4 through the demonstration of RF plasma ignition under vacuum conditions.

Huang, P.-H. A.↗

DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗

DOE OSTI2021

Hole formation effect on surface morphological response of plasma-facing tungsten

We report simulation results on the effect of helium (He) bubble bursting-mediated surface hole formation on the surface morphological response of tungsten plasma-facing components (PFCs) in nuclear fusion devices. Our analysis is based on an atomistically informed, continuum-scale model, which is capable of accessing the spatiotemporal scales relevant to the fuzz nanostructure formation process on the surface of PFC tungsten. Our simulations account, in an empirical fashion, for two types of subsurface bubble dynamical phenomena in the nanobubble region of PFC tungsten during He plasma irradiation, involving bubble bursting and surface crater formation. We demonstrate that the hole formation effect on the PFC tungsten surface accelerates the growth rate of nanotendrils and the onset of fuzz formation. As a result, the predicted incubation time for surface nanotendril growth is shortened, in agreement with experimental data of incubation fluence at comparable plasma exposure conditions. We also explore systematically the dependence of the PFC surface morphological response on the areal density of holes introduced at regular time intervals onto the He-implanted tungsten surface, a parameter in our analysis that serves as a proxy for the rate of He bubble bursting. More importantly, our simulations capture fine surface features in the PFC tungsten surface morphology and predict that the average spacing between nanotendrils is on the order of 100 nm, consistent with the experimental findings.

Chen, Chao-Shou (ORCID:0000000307910042)↗

NASA NTRS1985

Geometric-optical Modeling of a Conifer Forest Canopy

The objective of this research is to explore how the geometry of trees in forest stands influences the reflectance of the forest as imaged from space. Most plant canopy modeling has viewed the canopy as an assemblage of plane-parallel layers on top of a soil surface. For these models, leaf angle distribution, leaf area index, and the angular transmittance and reflectance of leaves are the primary optical and geometric parameters. Such models are now sufficiently well developed to explain most of the variance in angular reflectance measurements observed from homogeneous plant canopies. However, forest canopies as imaged by airborne and spaceborne scanners exhibit considerable variance at quite a different scale. Brightness values vary strongly from one pixel to the next primarily as a function of the number of trees they contain. At this scale, the forest canopy is nonuniform and discontinuous. This research focuses on a discrete-element, geometric-optical view of the forest canopy.

Strahler, A. H.↗

NASA NTRS2008

Deep Space Mission Emergency Mode Downlink

This paper investigates telecommunications between a deep space mission satellite and the ground station during an emergency mode. Once emergency is detected, spacecraft is put into a safe mode, i.e., antenna to be used for emergency mode communications is pointed towards the sun and use total available power to transmit. There are many parameters affecting communications in this mode and these should be properly balanced to produce desired results. This paper explores the effectiveness of spacecraft antenna gain pattern in the emergency mode with respect to positions of the spacecraft, earth, Sun Earth Probe (SEP) angle at the receiving antenna, and the range of the spacecraft with respect to the ground station. The paper also provides parabolic reflector antenna diameter that should be used for emergency mode as a function of the satellite to sun range in the solar system.

Kantak, Anil V.↗

NASA NTRS2018

Probabilistic Sensitivity Analyses Incorporating Surrogate Models for the Structural Response of a Representative Inflatable Space Structure

This study concentrates on results from probabilistic analysis and numerical simulation tools to identify parameter sensitivities and assess surrogate model suitability for a novel inflatable airlock concept, specifically the Non-Axisymmetric Inflatable Pressure Structure (NAIPS) under NASA's Minimalistic Advanced Softgoods Hatch (MASH) Program. The current studies extended the demonstration of probabilistic analysis tools to identify parameter sensitivities by: incorporating nonlinear material load-strain property curves and orthotropic representation of the fabric; and exploring the use of surrogate models to facilitate rapid evaluations as new information becomes available, as well as enable optimizations. The airlock model has been simplified to focus on the end dome section. Brief overviews of the finite element model and associated surrogate models are provided along with the probabilistic sensitivity analysis approaches. Responses of interest include the sensitivities of various loads in the structural elements of the NAIPS to material properties, cord lengths, inflation pressure and friction between softgoods components. The use of surrogate models was explored to facilitate rapid sensitivity evaluations as parameter ranges change. The completion of the sensitivity studies improved understanding of the dependence of load responses to several uncertain parameters and confidence in the ability to use surrogate models to represent the finite element simulations.

Lyle, Karen H.↗

DOE OSTI2024

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS↗

NASA NTRS1979

Autonomy in space navigation

The potential development of autonomous onboard navigation systems for future space missions is discussed. Motivations for autonomy in the near-earth satellite program and in deep-space navigation are examined, emphasizing lower mission-operation costs for the first, and reduction of ground-system complexity as well as encounter accuracy for the latter. Uses of navigation parameters by the Information Adaptive System, including sensor-pointing computation and image correction, are noted, as are the accuracy parameters of the NAVSTAR Global Positioning System. The role of autonomous onboard navigation in contact exploration of Neptune's and Uranus's satellites as well as in such extreme-high-velocity encounters as those with retrograde comets is investigated. The Automated Optical Navigation program is described

Jordan, J. F.↗

NASA NTRS2005

Arctic Warming Signals from Satellite Observations

Global warming signals are expected to be amplified in the Arctic primarily because of ice-albedo feedback associated with the high reflectivity of ice and snow that blankets much of the region. The Arctic had been a poorly explored territory basically because of its general inaccessibility on account of extremely harsh weather conditions and the dominant presence of thick perennial ice in the region. The advent of satellite remote sensing systems since the 1960s, however, enabled the acquisition of synoptic data that depict in good spatial detail the temporal changes of many Arctic surface parameters. Among the surface parameters that have been studied using space based systems are surface temperature, sea ice concentration, snow cover, surface albedo and phytoplankton concentration. Associated atmospheric parameters, such as cloud cover, temperature profile, ozone concentration, and aerosol have also been derived. Recent observational and phenomenological studies have indeed revealed progressively changing conditions in the Arctic during the last few decades (e g , Walsh et al. 1996; Serreze et al 2000; Comiso and Parkinson 2004). The changes included declines in the extent and area of surfaces covered by sea ice and snow, increases in melt area over the Greenland ice sheets, thawing of the permafrost, warming in the troposphere, and retreat of the glaciers. These observations are consistent with the observed global warming that has been associated with the increasing concentration of greenhouse gases in the atmosphere (Karl and Trenberth 2003) and confirmed by modeling studies (Holland and Bitz, 2003). The Arctic system, however, is still not well understood complicated by a largely fluctuating wind circulation and atmospheric conditions (Proshutinsky and Johnson 1997) and controlled by what is now known as the Arctic Oscillation (AO) which provides a measure of the strength of atmospheric activities in the region (Thompson and Wallace 1998). Meanwhile, the observed Arctic conditions since the 1970s have been shown to exhibit a linear behavior that directly contradicts what has been expected from the A0 (Overland, 2005). The decade of the 1990s has been regarded as the warmest decade in the last century and current data indicates that the 2000s may be even a warmer decade than the 1990s further supporting the linear variability. In this paper, we use satellite data to gain insights into the warming Arctic and how the abnormally warm conditions during the last few years are reflected in the region.

Comiso, Josefino C.↗

NASA NTRS2013

Probabilistic Modeling of the Renal Stone Formation Module

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

kidney stones↗

NASA NTRSDate not supplied

Integrated Modeling for Payload Test of the Roman Space Telescope

The Nancy Grace Roman Telescope (RST) is a NASA observatory designed to unravel the secrets of dark energy and dark matter, search for and image exoplanets, and explore many topics in infrared optics. Scheduled to launch in no earlier than October 2026, this 2.4 meter aperture telescope has a field of view 100 times greater than the Hubble Space Telescope. The mission is currently in its construction phase, where integrated modeling between thermal, structural, and optical models of the observatory is necessary to demonstrate science quality images over the range of operational parameters. This presentation discusses the most recent integrated modeling analysis cycle for Roman, including model correlation with our instrument level testing. We include a discussion on improved processes of the handling of the various flows of data between the modeling disciplines and discipline specific monte-carlo analysis predictions. We will finish with the predicted uncertainties and expected performance for our upcoming observatory alignment verification test using machine learning algorithms.

telescope↗

DOE OSTI2025

SULI Intern Final Report: Computationally Investigating Hydrogen Thermo-Diffusion in Yttrium Hydride Using Multiscale Methods

The renaissance of nuclear energy has arrived, heralding an age of abundant inexpensive clean energy, and renewed space exploration. In nuclear-powered spacecraft and microreactors, safety and size are of utmost importance. Yttrium Hydride (YHx) is being researched for its utility as a neutron moderator in nuclear reactors; the hydrogen in YHx slows down neutrons, enabling a continuous nuclear reaction in the reactor. This has the benefit of allowing reactors to be more safe, compact, and efficient. The goal of this effort is to computationally predict the coefficient of temperature-dependent hydrogen diffusion within YHx, the Soret coefficient. This parameter is essential for determining the safe operating modes of YHx moderators. Zirconium Hydride (ZrHx) is used in the Training, Research, Isotopes, General Atomics (TRIGA) reactor, is the reference material for these calculations. In this work, nanoscale atomic modeling in the Vienna Ab initio Simulation Package (VASP) is combined with the mesoscale finite element phase-field module in the Multiphysics Object-Oriented Simulation Environment (MOOSE); this culminates in a new multiscale computational method to simulate Soret diffusion of hydrogen in YHx. This data is useful for predicting experimental outcomes. This workflow involves convergence testing followed by static, Nudged Elastic Band (NEB), Quasi-Harmonic Approximation (QHA), and Molecular Dynamics (MD) calculations - linked with phase field simulation. NEB simulates hydrogen migration, while QHA and MD predict temperature-dependent properties. The static calculations align with literature, and preliminary NEB and QHA calculations yield accurate results. Once the atomic calculations are complete, we will incorporate Electron Backscatter Diffraction (EBSD) images and VASP-generated parameters into the phase field module to simulate intra- and intergranular transport of hydrogen in ZrHx and YHx. Future research will extend our approach to fuel-moderator materials systems such as Uranium-Yttrium Hydride (U-YHx). This work contributes to the development of advanced nuclear energy solutions for space travel.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗

DOE OSTI2024

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗