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DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗

DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗

DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗

DOE OSTI2026

Elucidating microstructural evolution and hardness variation across friction self-piercing riveted Al-7055 using synchrotron X-ray scattering and advanced microscopy techniques

Friction self-piercing riveting (FSPR) is a unique hybrid joining technique that combines the advantages of mechanical interlocking, frictional heat, and solid-state joining (if metallurgically compatible) to produce crack free joints in high strength and/or low-ductility alloys at room temperature. Here, in the current study, Al-7055 sheets were joined using FSPR for lightweight automotive applications and significant microhardness variations were observed across the joint cross-section. A detailed microstructural characterization at multiple length scales was carried out using advanced electron microscopy and X-ray scattering techniques to provide a fundamental understanding of the process-structure-property relationships. The relative contributions of microstructural characteristics at various length scales (i.e., grain size, dislocation density, solute concentration, precipitate nature) to strengthening were estimated using existent formulations (i.e., Hall-Petch, Taylor, precipitate bypass/shear equations) and correlated to the observed microhardness values across different regions. Small-angle X-ray scattering and scanning transmission electron microscopy revealed significant changes in the size and volume fraction of precipitate species, i.e., GP-I Zones, η′, and Mg/Zn solute co-clusters, depending on the process region. It was observed that the dissolution of the small η′/GP-I zones (T ∼ 150–200 °C) in the heat-affected zone were the key reason for the hardness drop. Further, it was shown that solid-solution, dislocation, grain size and solute co-cluster strengthening played a key role in the thermo-mechanically affected zone and grain-refined zone (GRZ). Finally, these observations were leveraged along with the Zener-Holloman relationship and grain size in the GRZ to estimate the peak joining temperature of the GRZ (∼ 350 °C) near the steel rivet.

aluminum 7xxx alloy↗

DOE OSTI2025

Structure–property relations of binary ferrite melts

Molten ferrite systems are used in the smelting and refining processes in steelmaking, to reduce the loss of metals in slags and to accelerate reaction rates. Here, high-energy x-ray diffraction experiments have been performed on aerodynamically levitated molten spheres of 43BaO–57FeO X and 43SrO–57FeO X at 1873 K using laser beam heating. The composition was varied within the range of x = 1–1.5 by changing the oxygen partial pressure of the levitation gas. The corresponding x-ray pair distribution functions have been interpreted using empirical potential structure refinement (EPSR) modeling. In oxygen-rich melts (x = 1.5), our EPSR models indicate very similar structures for the different alkaline-earth liquids, with both the Ba–O and Sr–O coordination numbers to be ∼8.4 and the total Fe–O coordination numbers ∼5.7. However, our models show that in reducing environments, the Fe 3+ and Fe 2+ ions exhibit very different behaviors in the Ba- and Sr-ferrite liquids. In the Ba-ferrite melt, the Fe 3+ –O coordination number decreases from 5.7 (at x = 1.5) to 5.2 (at x = 1.07), whereas Fe 2+ –O remains constant at ∼5.0 across the same compositional range. In the Sr melts, both the Fe 2+ –O and Fe 3+ –O coordination numbers rise from ∼5.7 (at x = 1.5) to 6.3 (at x = 1.07). All models show the structures to be heterogeneous with intertwined nanometer sized clusters or channels of Ba/Sr–O and Fe–O polyhedra that grow as oxygen content is reduced. Changes in the viscosity and electrical properties are interpreted in terms of the number of bridging and non-bridging oxygens associated with FeO 4 tetrahedra and concentration of charge carriers, respectively.

Benmore, Chris J. [Argonne National Laboratory (AN↗

DOE OSTI2024

Ramen

Many computationally unintensive models for material process-structure-property connections exist in the literature (e.g. journal articles and textbooks). However, to our knowledge there is no existing compilation of these models in one package. Many industrially and research relevant workflows involve combinations of many of these models and if the models have inconsistent interfaces, building these workflows can be challenging. Ramen provides a collection of computationally unintensive models for calculating material process-structure-property relationships from the literature, as well as related tools (e.g. for plotting results). These models use a consistent interface structure and all leverage material data files and data structures from the Mist library (also developed at ORNL). Ramen provides a structured package for combining many standard models in one place, with a standardized interface. This approach makes combining models in long/complex chains easy and straightforward.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗

DOE OSTI2024

Reactive flash sintering and characterization of bulk high entropy nitrides

Over the past decade, numerous high-entropy ceramics have been synthesized, often displaying attractive properties. However, the study on facile preparation of bulk high entropy nitrides (HEN) are limited, despite its broad potential applications. This research demonstrates for the first time rapid fabrication (within ∼6 min) of bulk high-entropy nitrides, especially (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N, from binary nitride powder mixtures using a highly efficient reactive flash sintering (RFS) technique. X-ray diffraction (XRD) shows the HENs from RFS are near single-phase solid solutions with a rock salt crystal structure, while in situ synchrotron study carried out during RFS captured in real time the formation of HEN, which was preserved upon cooling, suggesting thermodynamic stability of the HEN phase, even up to extreme pressure (∼35.6 GPa). Microscopic analyses using SEM, STEM, and EDS reveal decent uniformity for HEN with no obvious segregation of elements, even to submicron scale. Some properties of the obtained bulk HENs are consistent with expectations. For example, their hardness and bulk modulus are close to estimates based on rule-of-mixture (ROM) values from the constituent binary nitrides. Meanwhile, some other measured properties seem to show surprises. For example, the fracture toughness for the HENs (e.g., 7.81 ± 1.40 MPa•m 1/2 or higher) turns out to be more than double of the expected ROM estimates. The significantly improved fracture toughness is attributed to the observed nano-layered structure of the HENs, despite the HEN’s cubic crystal structure and high hardness. In addition, the oxidation resistance shows improvement up till ∼800°C, possibly due to Ta doping that suppress oxygen vacancy formation in the oxide shell, while the 5-metal HEN of (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N displays superconductivity (T c of ∼5–7 K from magnetism and resistivity measurements, slightly lower than ROM estimate), despite insulating property of starting AlN. Furthermore, future study combining experimental investigation using larger samples to confirm the observed increase in fracture toughness and oxidation resistance, theoretical modeling at different length scale, and more detailed structural/chemical characterization, especially at the atomic scale, are all needed to fully understand the inter-relationships between composition, processing, structure, and novel properties for these HENs and the development of related new materials for different applications.

Flash sintering↗

DOE OSTI2025

Exploring baryon resonances with transition generalized parton distributions: status and perspectives

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum, and forces in the system. Transition GPDs extend these concepts to N → N* transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions e + N → e' + M + N*, such as deeply-virtual Compton scattering (M = γ) or meson production (M = π, K, etc.), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperipheral collisions), and the theoretical and experimental developments needed to realize this program.

Experimental Nuclear Physics↗

DOE OSTI2024

Enzymatic depolymerization of polyester: Foaming as a pretreatment to increase specific surface area

Abstract Poly(ethylene terephthalate) (PET) is widely used for its high strength‐to‐weight ratio, gas barrier properties, and chemical resistance. The growing PET use highlights the demand for a better recycling system. Enzymatic recycling, alongside mechanical and chemical methods, is eco‐friendly and yields properties similar to virgin PET. Substrate properties ( T g , crystallinity, and specific surface area [SSA]) and enzyme stability significantly impact conversion efficiency. Higher SSA and lower crystallinity tend to yield improved depolymerization when employing leaf compost‐cutinase (LCC‐ICCG) enzymes. This study explored melt extrusion and foaming as pretreatment techniques to modify PET structural properties, using a low‐cost chemical foaming agent (CFA). The monomer conversion rate and efficiency during depolymerization were measured and related to the processing, extrudate micro‐ and meso‐structure, and polyester type. Pretreated PET substrates showed reduced T g , crystallinity, density, and enhanced SSA, resulting in a 90% mass loss for foamed RPET and VPET substrates within 2 days. In contrast, PET with ~30% of cyclohexanedimethanol comonomer exhibited a nearly 50% lower depolymerization rate, with zero BHET production. It indicates that the combination of low crystallinity, low T g , and high SSA leads to improved monomer conversion. These findings emphasize the significance of amorphization and foaming in enhancing PET enzymatic depolymerization.

42 ENGINEERING↗

DOE OSTI2025

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Density of molten oxides measured in an aero-acoustic levitator

Knowing the thermophysical properties of high-temperature melts can aid the design of melt processes and validate atomic structural models, such as those used in studying glass formation. Property measurements on such melts are challenging, however, due to container-related contamination and heterogeneous nucleation. Containerless processing techniques that employ levitation can be used to avoid these obstacles. In that context, we demonstrate here the application of silhouette imaging to measure the density of molten oxides in an aero-acoustic levitation instrument (AAL). The AAL combines gas jet levitation with actively controlled acoustic positioning to enable full optical access to samples ca. 2–4 mm in diameter, which are laser beam heated and melted. The cross sections of molten drops are imaged using a monochromatic light source and narrowband-filtered camera. Melt volume is calculated from the cross sections and used to find density at several temperatures ranging 1530–1920 K, including up to 350 K of supercooling. We report densities for CaAl 2 O 4 , Ca 12 Al 14 O 33 , CaSiO 3 , their Fe 2 O 3 -bearing analogs, and 83TiO 2 -17RE 2 O 3 (RE = La or Nd). These provide important benchmarks of the capabilities, measurement uncertainties, and future outlook for this technique.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Structure and mechanical properties of grain boundaries in molybdenum disulfide (MoS 2 )

Molybdenum disulfide (MoS 2 ) is a two-dimensional material widely used as a lubricant in many applications involving mechanical loading under a wide range of operating temperatures. Since many synthesis and processing techniques yield MoS 2 in its polycrystalline form, establishing grain boundary (GB) structure–property relations is key to designing MoS 2 microstructures with tailored properties. Here, we employ classical, reactive atomistic simulations to investigate the structure and mechanical behavior of a wide range of GBs in MoS 2 as a function of temperature. Using a bicrystal MoS 2 geometry, we characterize the atomic structure and calculate the energy of several low-angle GBs. Then, we simulate the tensile deformation behavior of MoS 2 bicrystals at several temperatures. Our results reveal that at temperatures above 100 K, the deformation of MoS 2 bicrystals is characterized by the nucleation of shear bands from GBs that grow, with subsequent loading, into the MoS 2 crystals. At low temperatures, the tensile deformation is characterized by the nucleation and propagation of deformation fronts, resulting in altered bond angles and bond lengths. Quantitative analysis reveals a decrease in the ultimate tensile stress and ultimate failure strain of MoS 2 bicrystals with the increase in temperature. Furthermore, our simulations of the mechanical behavior of metastable GBs reveal that the strength and ductility decrease with the increase in energy of these boundary structures. In broad terms, our work provides future avenues to employ GB engineering as a strategy to tailor the properties of MoS 2 microstructures.

Moore, Robert D. [Lehigh Univ., Bethlehem, PA (Uni↗

DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Polymorphs of the n–Type Polymer P(NDI2OD–T2): A Comprehensive Description of the Impact of Processing on Crystalline Morphology and Charge Transport

A systematic study of the polymorphs emerging in P(NDI2OD-T2) (also commercially known as N2200), a prototypical organic semiconducting n-type polymer, is presented. Using a tightly integrated experimental and computational approach, detailed atomistic-level descriptions are provided investigating the three known P(NDI2OD-T2) polymorphs observed at room temperature as a function of thin-film processing. Importantly, over the course of the work, a missing link is uncovered, a fourth polymorph referred to here as Form I-β; this new form is a morphological intermediary observed upon thermal annealing, which evolves from Form I but tends to disappear upon full polymer chain melting. The computationally derived polymorph structures show excellent agreement with experimental X-ray scattering characterization. The relative stabilities of each polymorph are calculated in terms of both the bulk material and the polymorph-air interface. An energy landscape is then constructed to qualitatively compare the thermodynamic versus kinetic origins of each polymorph, and the factors driving (supra)assembly and associated transformations among polymorphs using an approach generalizable to other organic semiconducting polymers. Lastly, the relationships among preferential polymorphic crystallinity, relative chain orientations, and directional charge transport properties in P(NDI2OD-T2) are explored. Altogether, this work provides unprecedented insights into complex structure-processing-transport relationships in a representative semiconducting organic polymer.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Tuning the Properties of Lignin‐Derived Deep Eutectic Solvents for Biomass Processing

Lignin-derived deep eutectic solvents (DESs) have been investigated as sustainable green media for biomass processing. However, the properties and processability of DESs have not been fully understood with the chemical structures of their constituents for biomass fractionation. Here, in this article, the properties of the phenolic DESs are discussed with different numbers of functional groups, such as –OCH 3 and –CHO in their hydrogen bond donor (HBD) structures. The formation of DES is significantly related to the hydrogen bond between its constituents, identified by nuclear magnetic resonance (NMR) analysis and density functional theory calculation (DFT). Lower viscosity and net basicity of DES are achieved with fewer –OCH 3 on HBD structures, resulting in enhanced processability and fractionation efficiency. The thermal stability of the DES is also influenced by the –OCH 3 and –CHO of HBD, as indicated by its onset temperature. The recyclability of the phenolic DES is confirmed by the fractionation performance of the recycled DES. Understanding the structural impacts of DES constituents on the properties and performance is crucial for designing solvents in biorefinery applications.

Ryu, Jiae [State University of New York (SUNY), Sy↗

DOE OSTI2025

Characterizing leaf-scale fluorescence with spectral invariants

Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (p) and the scattering asymmetry parameter (q), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (R 2 ) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm -2 µm -1 sr -1 , respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. Further, the leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗