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At least 271 records · Page 15

A Comparison of Three Neodymium Atomic Data Sets for Kilonova Modeling

We examine the impact of input neodymium (Nd) atomic data on the light curves and spectra of kilonovae (KNe), probing the sensitivity of kilonova observables to the atomic physics of this important lanthanide element. We use the SuperNu Monte Carlo radiative transfer code, simulating a simple semianalytic 1D kilonova (KN) with a pure Nd atmosphere, fixing the radiative transfer method while using input atomic data generated by three different codes: the LANL suite of atomic physics codes, HULLAC, and Autostructure. We see that the choice of atomic data significantly shapes the resulting light curves and spectra. Peak bolometric luminosities differ by a ratio of nearly 1.5 between HULLAC/Autostructure and LANL data sets. Moreover, we observe significant near- to mid-IR differences in the structure of the spectra. We specifically attribute these differences to the choice of atomic data for neutral Nd I. Many of the results here have been adapted from a presentation at “Radiative Transfer and Atomic Physics of Kilonovae” in Stockholm, 2023. We additionally present a LANL data set with energies calibrated to available values in the NIST Atomic Spectra Database, and demonstrate that this calibration also significantly affects IR spectral structure at late time. The substantial differences in KN observables that arise from tuning the atomic data of just one lanthanide element highlight the special attention that must be paid to atomic physics uncertainties when modeling KNe, from AT2017gfo to beyond.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Enhancing ICARUS and REDTOP Software and Hardware: Event Generator Interface Development and Calorimeter Tile Prototype

ICARUS (Imaging Cosmic And Rare Underground Signals) is a liquid argon time projection chamber (LArTPC) detector that pursues the sterile neutrino, which relies on accurate simulations of neutrino-argon interactions. REDTOP (Rare Eta Decays To Observe new Physics) is a proposed low-energy, high-intensity meson factory designed to explore rare $\eta$/$\eta'$ meson decays and probe physics beyond the Standard Model. As a next-generation experiment, this requires both accurate simulations and innovative detector technologies. This project contributes to both ICARUS, from a simulation perspective, and REDTOP, from both a simulation and detection perspective, through the event generation of lepton-nucleon interactions and the physical enhancement of the calorimeter technology within the REDTOP detector. We developed an interface between ACHILLES (A CHIcago Land Lepton Event Simulator), a theory-driven lepton-level event generator, and GENIE, a robust event generator framework used for neutrino physics. By incorporating the precise theoretical cross-section calculations of ACHILLES into the experimental realism of GENIE, the interface allows for improved accuracy of neutrino-nucleon simulations, which can be adapted for the proton beam specifications of the REDTOP meson factory as well as for the ICARUS experiment. In parallel, we developed an improved prototype for the ADRIANO2 (A Dual Readout Integrally Active Non-segmented Option) dual-readout calorimeter tiles for the REDTOP detector. To improve the efficiency of the lead-glass tiles trapping Cherenkov light for energy reconstruction and particle identification, we optimized the application of a highly reflective coating. Through viscosity and thickness control, masking, and a custom spray technique, we refined the coating process to reduce surface defects and improve light yield. Together, these efforts strengthen the ICARUS neutrino program and REDTOP's capability of detecting rare decay events.

Visser, Erin [Michigan State U.] (ORCID:0009000184↗

OpenFacadeControl: enabling integration of automated facades with other building systems

Automated facades are, for the most part, still considered as separate from other building systems throughout the design, installation, commissioning, operation, and maintenance cycle. This takes place despite the fact that their energy and comfort performance are deeply interlinked with the operation of lighting and HVAC systems. Over the last two decades, research has shown that there are significant advantages from operating facades as an integrated system with the rest of the building. Nevertheless, significant barriers prevent this type of integration becoming more common. One of them is the lack of a platform that is inexpensive to implement and that easily allows the practical implementation of integrated control algorithms across fenestration and other building systems, using a variety of communications protocols. This is particularly challenging when automated facades are installed in existing buildings, where interaction with legacy building systems that were installed over the past lifetime of the building can require a high degree of interoperability. OpenFacadeControl (OFC) is an open-source controls framework aimed at unified control of facades and other building systems, including the sharing of third-party sensor information. Through leveraging the Volttron controls platform, it allows the integration of systems and sensors that are manufactured by different companies and that use different communications protocols into an ensemble that functions as a single system. OFC is designed to enable integrated control algorithms of varying degrees of complexity, ranging from simple, heuristic controls to more sophisticated approaches like model-predictive control. Use of a research version to test advanced lighting and shading strategies in a full-scale experimental testbed has demonstrated the ease of deploying advanced control solutions using OpenFacadeControl. This paper presents the structure of OpenFacadeControl and a demonstration case showing the use of OFC in laboratory tests of advanced lighting and fenestration controls that coordinated motorized shades communicating via the BACnet building communications standard and lights communicating via internet-protocol-based application programming interface (API), based on the readings of a shared light level sensor communicating via a different API.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Experimental validation of color rendition specification criteria based on ANSI/IES TM-30-18

An experiment was conducted to examine color preference specification criteria. Twenty-five participants each evaluated 90 lighting scenes in a room filled with objects. The lighting scenes included nine chromaticity groups, each with 10 systematically-varied color rendition conditions designed to meet or not meet previously proposed color preference specification criteria using ANSI/IES TM-30-18 Rf, Rcs,h1, and Rg. The color rendition conditions did not meet the criterion for none, one, two, or all three of these measures. Participants, who chromatically adapted to each chromaticity group, rated the objects’ color appearance on eight-point scales for saturated-dull, normal-shifted, and like-dislike (preference), as well as a binary for acceptable or unacceptable. The findings corroborate past work, but also indicate that color preference criteria could be adjusted slightly to improve performance, with Tier A having Rf = 78, Rg = 95, and -1% = Rcs,h1 = 15%, Tier B having Rf = 74, Rg = 92, and -7% = Rcs,h1 = 19%, and Tier C having Rf = 70, Rg = 89, -12% = Rcs,h1 = 23%. A companion regression analysis shows models based on Rf, Rg, and Rcs,h1 were superior in predicting color preference compared to those using other measures of color rendition.

Royer, Michael P.↗

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗

A literature review of pyroprocessing safeguards

Pyroprocessing is a promising technology for reprocessing used nuclear fuel (UNF) from light water reactors (LWR) and sodium fast reactors (SFR). With the advancement of fast reactors with projects from companies such as TerraPower, the prospects of pyroprocessing are more promising than ever before, as fast reactors can consume all actinides, not just uranium and plutonium. Before pyroprocessing can be implemented commercially, pre-existing safeguards used for aqueous reprocessing must be adapted for pyroprocessing. Safeguards, as determined by both the Nuclear Regulatory Commission (NRC) and the International Atomic Energy Agency (IAEA), are discussed in this review as guidelines for approaches used in both domestic and international reprocessing plants. The implementation of safeguards in aqueous reprocessing was then reviewed based on the experience from existing aqueous facilities. The experimental pyroprocessing facilities were identified as pyroprocessing plants in the design stage with one operating exception of the Fuel Conditioning Facility (FCF). The safeguard methods implemented or designed for each and the accompanying challenges of utilizing existing safeguards in pyroprocessing are considered and a summary of applicable approaches is included.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗

Adaptive nonlinear wavefront shaping in layered TMDs

Two-dimensional transition metal dichalcogenides are promising candidates for nonlinear photonics applications, offering strong nonlinear susceptibility and compatibility with integrated platforms. Although considerable effort has been devoted to manipulating second harmonic generation in these materials, the dynamic control of nonlinear wavefronts remains largely unexplored. Metasurfaces have enabled significant progress in nonlinear beam engineering, yet their practical implementation is limited by low conversion efficiencies and restricted design flexibility. In this work, we employ feedback-based wavefront shaping using spatial light modulators to enhance SHG from pyramid-like WS 2 multilayer structures by over three orders of magnitude in selected spatial regions. This approach allows us to program nonlinear holograms and dynamically shape the SHG signal through phase-only modulation, opening new possibilities for nonlinear imaging, optical information processing, and data communication.

Mavian, Alex [Rensselaer Polytechnic Inst., Troy, ↗

Integrated fluorescence light microscopy-guided cryo-focused ion beam-milling for in situ montage cryo-ET

Cryogenic-electron tomography (cryo-ET) permits the in situ visualization of biological macromolecules at the molecular level. Owing to the variable thickness of cells, tissues and organisms, frozen specimens may need to be thinned by cryo-focused ion beam (FIB) milling to produce thin (<500 nm) cryo-lamellae suitable for cryo-ET. Locating regions of interest remains a challenge because untargeted milling can lead to inadvertent ablation and removal of regions of interest. Correlative light and electron microscopy, combined with cryo-FIB milling, can guide the identification of labeled targets in the cellular milieu. Multiple transfers between cryo-imaging instruments, cumbersome correlation algorithms, limited accuracy and low throughput have hindered the routine adoption of cryo-FIB milling within a multimodal correlative workflow for in situ structural biology. Here, in this study, we present a workflow for 3D correlative cryo-fluorescence light microscopy-FIB-ET that streamlines fluorescence light microscopy-guided FIB milling, improving throughput while preserving both structural and contextual information. The complete integration of hardware and software described here minimizes sample contamination from cross-platform exchanges and greatly enhances the efficiency of 3D targeting in cryo-milling. We then describe procedures for implementing montage parallel array cryo-ET (MPACT), which can be easily adapted to any modern life-science transmission electron microscope. MPACT supports high-throughput cryo-ET acquisitions (10 tilt series in 1.5 h) for structure determination and comprehensive contextual understanding of macromolecules within their native surroundings. A complete session from sample preparation to MPACT data processing takes 5−7 d for an individual experienced in both cryo-EM and cryo-FIB milling.

Yang, Jie E. [Univ. of Wisconsin, Madison, WI (Uni↗

Flexibility in the Energy Balancing Network of Photosynthesis Enables Safe Operation under Changing Environmental Conditions

Given their ability to harness chemical energy from the sun and generate the organic compounds necessary for life, photosynthetic organisms have the unique capacity to act simultaneously as their own power and manufacturing plant. This dual capacity presents many unique challenges, chiefly that energy supply must be perfectly balanced with energy demand to prevent photodamage and allow for optimal growth. From this perspective, we discuss the energy balancing network using recent studies and a quantitative framework for calculating metabolic ATP and NAD(P)H demand using measured leaf gas exchange and assumptions of metabolic demand. We focus on exploring how the energy balancing network itself is structured to allow safe and flexible energy supply. We discuss when the energy balancing network appears to operate optimally and when it favors high capacity instead. We also present the hypothesis that the energy balancing network itself can adapt over longer time scales to a given metabolic demand and how metabolism itself may participate in this energy balancing.

59 BASIC BIOLOGICAL SCIENCES↗

Topographic hydro-conditioning to resolve surface depression storage and ponding in a fully distributed hydrologic model

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here, in this study, we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.

54 ENVIRONMENTAL SCIENCES↗

High-speed Focal Plane Wave Front Sensing with an Optical Chopper

Focal plane wave front sensing and control is a critical approach to reducing noncommon path errors between a conventional astronomical adaptive optics (AO) wave front sensor (WFS) detector and a science camera. However, in addition to mitigating noncommon path errors, recent focal plane wave front sensing techniques have been developed to operate at speeds fast enough to enable "multi-WFS" AO, where residual atmospheric errors are further corrected by a focal plane WFS. Although a number of such techniques have been recently developed for coronagraphic imaging, here we present one designed for noncoronagraphic imaging. Utilizing conventional AO system components, this concept additionally requires (1) a detector imaging the focal plane of the WFS light source and (2) a pupil plane optical chopper device that is the noncommon path to the first WFS and is synchronized to the focal plane imager readout. These minimal hardware requirements enable the temporal amplitude modulation to resolve the sine ambiguity of even wave-front modes for low, mid, and high wave front spatial frequencies. Similar capabilities have been demonstrated with classical phase diversity by defocusing the detector, but such techniques are incompatible with simultaneous science observations. This optical chopping technique, however, enables science imaging at up to 50% duty cycle. In conclusion, we present both simulations and laboratory validation of this concept on SEAL, the Santa Cruz Extreme AO Laboratory testbed.

79 ASTRONOMY AND ASTROPHYSICS↗

Variable rate neural compression for sparse detector data

Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity—a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ion-specific clustering of metal-amphiphile complexes in rare earth separations

The nanoscale structure of a complex fluid can play a major role in the selective adsorption of ions at the nanometric interfaces, which is crucial in industrial and technological applications. Here we study the effect of anions and lanthanide ions on the nanoscale structure of a complex fluid formed by metal-amphiphile complexes, using small angle X-ray scattering. The nano- and mesoscale structures we observed can be directly connected to the preferential transfer of light (La and Nd) or heavy (Er and Lu) lanthanides into the complex fluid from an aqueous solution. While toluene-based complex fluids containing trioctylmethylammonium-nitrate (TOMA-nitrate) always show the same mesoscale hierarchical structure regardless of lanthanide loading and prefer light lanthanides, those containing TOMA-thiocyanate show an evolution of the mesoscale structure as a function of the lanthanide loading and prefer heavy lanthanides. The hierarchical structure indicates the presence of attractive interactions between ion-amphiphile aggregates, causing them to form clusters. A clustering model that accounts for the hard sphere repulsions and short-range attractions between the aggregates has been adapted to model the X-ray scattering results. The new model successfully describes the nanoscale structure and helps in understanding the mechanisms responsible for amphiphile assisted ion transport between immiscible liquids. Accordingly, our results imply different mechanisms of lanthanide transport depending on the anion present in the complex fluid and correspond with anion-dependent trends in rare earth separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

CHEQUP v0.1

CHEQUP (Castro-based Hofi Expansion with QUasineutral Plasma) is a simulation code for modeling the formation of hydrodynamic optical-field-ionized (HOFI) plasma channels, which are used as waveguides in laser-plasma acceleration experiments. This includes experiments performed at LBNL's BELLA facility as well as other laser facilities across the world. CHEQUP extends the open-source Castro hydrodynamics framework with physics modules tailored for modeling HOFI plasma channels -- including multi-species ionization and three-body recombination for mixtures of hydrogen, nitrogen, helium, and argon ; a two-temperature model tracking electron and heavy-species temperatures separately ; and coupling with other codes of the BLAST ecosystem (https://blast.lbl.gov/) such as WarpX, via the openPMD standard. CHEQUP inherits from Castro the ability to run on modern GPU architectures (NVIDIA CUDA, AMD HIP) and supports adaptive mesh refinement (AMR) for efficient multi-scale resolution. Compared to existing tools, CHEQUP would be, to our knowledge, the first open-source code implementing the full HOFI channel formation physics, and the first implementation capable of running on GPUs. This enables significantly faster, large-scale parameter scans critical for the design of next-generation LPA-based accelerators and light sources.

Lehe, Remi [Lawrence Berkeley National Laboratory ↗

Intermediate States Enable Keratin-like α-To-β Transformations in Strain-Responsive Synthetic Polypeptides

Nature’s fibrous proteins, such as α-keratin, achieve remarkable mechanical properties by undergoing strain-induced α-to-β conformational transitions. Inspired by these materials, we report a strategy for designing synthetic polypeptides that undergo similar transformations at elevated temperatures far exceeding keratin’s operational range. By employing helix-confined ring-opening polymerization (ROP) of N -carboxyanhydrides (NCAs) initiated by a short poly(γ-benzyl- L -glutamate) (PBLG) precursor, we synthesized poly( O -benzyl- L -serine) (PBLS) chains that adopt an α-helical structure yet transition into β-sheets upon heating. Compression molding at carefully chosen temperatures drives PBLS segments into an α-β intermediate state, characterized by relaxed intrachain hydrogen bonds and a hexagonal packing arrangement. Under mechanical strain, these intermediate states convert in situ into β-sheets, producing significant strain-hardening well below the spontaneous α-to-β temperature threshold. Further, this approach extends to polypeptides bearing different side chains, such as poly(S-benzyl- L -cysteine), demonstrating robust mechanical reinforcement across a wide temperature window up to ∼ 200 °C. In situ synchrotron X-ray analysis confirms that chain alignment, β-sheet formation, and domain growth occur stepwise during deformation. By harnessing the intermediate states and the supramolecular cooperativity conferred by compression-molded films, our method provides a versatile platform for developing next-generation polypeptide materials with tunable mechanical resilience and responsiveness─surpassing the temperature limitations of natural fibrous proteins and enabling potential applications demanding broad-temperature mechanical adaptability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A quantitative model of charge injection by ruthenium chromophores connecting femtosecond to continuous irradiance conditions

A kinetic framework for the ultrafast photophysics of tris(2,2-bipyridine)ruthenium(II) phosphonated and methyl-phosphonated derivatives is used as a basis for modeling charge injection by ruthenium dyes into a semiconductor substrate. By including the effects of light scattering, dye diffusion, and adsorption kinetics during sample preparation and the optical response of oxidized dyes, quantitative agreement with multiple transient absorption datasets is achieved on timescales spanning femtoseconds to nanoseconds. In particular, quantitative agreement with important spectroscopic handles—the decay of an excited state absorption signal component associated with charge injection in the UV region of the spectrum and the dynamical redshift of a ~500 nm isosbestic point—validates our kinetic model. Pseudo-first-order rate coefficients for charge injection are estimated in this work, with an order of magnitude ranging from 10 11 to 10 12 s –1 . The model makes the minimalist assumption that all excited states of a particular dye have the same charge injection coefficient, an assumption that would benefit from additional theoretical and experimental exploration. Here, we have adapted this kinetic model to predict charge injection under continuous solar irradiation and find that as many as 68 electron transfer events per dye per second take place, significantly more than prior estimates in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are ‘unseen’ during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope ‘hard splitting’ (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for ‘strict splitting’ in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction.

60 APPLIED LIFE SCIENCES↗