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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Pulse Electrodeposition for Carbonate-Rich Deposits from Seawater

Seawater electrodeposition is gaining renewed interest in the context of sustainable development, both to build climate-resilient coastal infrastructure and for ocean-based decarbonization applications. Most of the applications benefit from CaCO 3 -rich deposits, but constant-voltage electrodeposition results in a mixture of CaCO 3 and Mg(OH) 2 , especially at higher voltages where precipitation rates are more desirable. The use of pulse voltages can help control interfacial pH that dictates the precipitation reactions. Here, we explore the use of pulse electrodeposition as a function of pulse frequency and duty cycle to control deposit composition. The most CaCO 3 -rich deposits were obtained under 10 Hz frequency and 10% duty cycle conditions for the voltage window investigated (-0.8 V to -1.2 V vs. SCE). While pulsing the voltage increases the amount of CaCO 3 deposited, the energy required per gram of CaCO 3 is significantly higher (14.5×) when compared to the base case of applying a constant voltage of -0.8 V vs. SCE. Further optimization of pulse conditions, electrode materials, and system configuration could lead to finding parameters that result in exclusively carbonate deposits without compromising precipitation rates, which may prove to be more useful for corrosion protection, coastal infrastructure, and other applications in sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying the Impact of LSST u -band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies

The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), promising to discover billions of galaxies out to redshift 7, using six photometric bands (ugrizy) spanning the near-ultraviolet to the near-infrared. The exact number of and quality of information about these galaxies will depend on survey depth in these six bands, which in turn depends on the LSST survey strategy, i.e., how often and how long to expose in each band. u-band depth is especially important for photometric redshift (photo-z) estimation and for detection of high-redshift Lyman-break galaxies (LBGs). In this paper, we use a simulated galaxy catalog and an analytic model for the LBG population to study how recent updates and proposed changes to Rubin’s u-band throughput and LSST survey strategy impact photo-z accuracy and LBG detection. We find that proposed variations in u-band strategy have a small impact on photo-z accuracy for z < 1.5 galaxies, but the outlier fraction, scatter, and bias for higher-redshift galaxies vary by up to 50%, depending on the survey strategy considered. The number of u-band dropout LBGs at z ∼ 3 is also highly sensitive to the u-band depth, varying by up to 500%, while the number of griz-band dropouts is only modestly affected. Under the new u-band strategy recommended by the Rubin Survey Cadence Optimization Committee, we predict u-band dropout number densities of 110 deg −2 (3200 deg −2 ) in year 1 (10) of LSST. We discuss the implications of these results for LSST cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Score-based deterministic density sampling

We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density π given only its score ∇ log π. Our method approximates the Wasserstein gradient flow on KL($f_t$∥π) by learning the time-varying score ∇ log $f_t$ on the fly using score matching. While having the same marginal distribution as Langevin dynamics, our method produces smooth deterministic trajectories, resulting in monotone noise-free convergence. We prove that our method dissipates relative entropy at the same rate as the exact gradient flow, provided sufficient training. Numerical experiments validate our theoretical findings: our method converges at the optimal rate, has smooth trajectories, and is often more sample efficient than its stochastic counterpart. Experiments on high-dimensional image data show that our method produces high-quality generations in as few as 15 steps and exhibits natural exploratory behavior. The memory and runtime scale linearly in the sample size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MATHUSLA: An External Long-Lived Particle Detector to Maximize the Discovery Potential of the HL-LHC

We present the current status of the MATHUSLA (MAssive Timing Hodoscope for Ultra-Stable neutraL pArticles) long-lived particle (LLP) detector at the HL-LHC, covering the design, fabrication and installation at CERN Point 5. MATHUSLA40 is a 40 m-scale detector with an air-filled decay volume that is instrumented with scintillator tracking detectors, to be located near CMS. Its large size, close proximity to the CMS interaction point and about 100 m of rock shielding from LHC backgrounds allows it to detect LLP production rates and lifetimes that are one to two orders of magnitude beyond the ultimate reach of the LHC main detectors. This provides unique sensitivity to many LLP signals that are highly theoretically motivated, due to their connection to the hierarchy problem, the nature of dark matter, and baryogenesis. Data taking is projected to commence with the start of HL-LHC operations. We summarize the new 40m design for the detector that was recently presented in the MATHUSLA Conceptual Design Report, alongside new realistic background and signal simulations that demonstrate high efficiency for the main target LLP signals in a background-free HL-LHC search. We argue that MATHUSLA's uniquely robust expansion of the HL-LHC physics reach is a crucial ingredient in CERN's mission to search for new physics and characterize the Higgs boson with precision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Drop clustering and drop size correlations from holographic imagery suggest cloud droplet spectral broadening via entrainment-mixing

The question of how droplets rapidly grow large enough to initiate collision-coalescence has persisted for decades. Many theories explaining the production of sufficiently large drops (i.e., those in the “bottleneck” size range; ∼ 25–50 µm diameters) involve drop clustering on millimeter scales. A novel method is introduced to evaluate drop clustering trends particle-by-particle (i.e., the number/proximity of neighboring drops for given droplets; defined as drop clustering fields) which are diagnosed relative to drops within their shared drop environments – in contrast to previous studies which diagnose drop clustering of defined sample volumes, or in terms of absolute length scales. Specifically, this study evaluates the statistical likelihood that drops of a given size are associated with either a significant number of neighboring drops, or are significantly isolated from neighboring drops. Observations are acquired from the HOLODEC during the Cloud System Evolution in the Trades campaign, which sampled subtropical marine clouds. The HOLODEC measures drop size distributions and the 3D spatial coordinates of droplets. Results show drops within the bottleneck size range (diameters of ∼ 25–50 µm) are most likely to be significantly isolated from neighboring drops. This “isolated large drop trend” is primarily observed at subsaturated conditions, suggesting entrainment is the contributing factor. Holograms associated with this trend are more likely to have broader drop size distributions, larger maximum drop sizes and overly regions where precipitation reaches the lowest altitudes from the sampled cloud, suggesting entrainment-mixing drop size distribution broadening is a relevant precipitation-initiation mechanism.

D'Alessandro, John J. [Univ. of Washington, Seattl↗
DOE OSTI2026

Applying corrective machine learning in the E3SM atmosphere model in C++ (EAMxx)

The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of earth system models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous computing architectures. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years to a few decades, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3GFS earth system model, this study explores the effects of implementing corrective-ML in EAMxx-SCREAM. We also address the computational challenges of integrating our implementation of corrective-ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as potential reasons why this approach has not proved as effective for EAMxx-SCREAM as for FV3GFS.

Environmental sciences↗
DOE OSTI2025

Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

The evidence for dark matter (DM) remains compelling, although attempts to understand its particle nature remain inconclusive. One promising method to study DM is detecting DM subhalos through their gravitational interactions with stellar streams. In this study, we apply Neural Posterior Estimation (NPE) to constrain subhalo interaction parameters, including mass, scale radius, velocity, and encounter geometry, from stellar stream kinematics. We generate particle spray simulations based on the Lagrange Cloud stripping technique, focusing on the ATLAS-Aliqa Uma stream as a test case. We train multiple NPE models across multiple observational scenarios, quantifying how kinematic completeness affects inference and forecasting constraints from upcoming surveys including LSST, 4MOST, and 10-year Gaia data. Our results demonstrate that NPE can produce accurate and well-calibrated posteriors. In the idealized case with full 6D coordinates, we achieve subhalo mass uncertainties of 15-20% for a $10^7 \, \mathrm{M_\odot}$ subhalo, with 5D coordinates (excluding radial velocities) achieving similar performance. Under realistic observational conditions, mass uncertainties range from 50% (present-day) to 20-40% (future scenarios), with comparable performance between the photometric-only LSST sample and a smaller sample that includes Gaia proper motions and 4MOST radial velocities. Most notably, we find that velocity bimodality emerges when phase space is poorly sampled, whether due to missing kinematic information or limited stellar tracers. Combining large photometric samples with targeted spectroscopic follow-up can effectively resolves this degeneracy. These results demonstrate the power of implicit likelihood inference for optimizing stellar stream observational strategies and forecasting DM subhalo constraints from upcoming surveys.

Nguyen, Tri [Northwestern U. (main); SkAI, Chicago↗
DOE OSTI2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

Aarrestad, Thea Klaeboe [ETH, Zurich (main)]↗
DOE OSTI2026

A Deep Look at the Ultra-Faint Milky Way Satellite Virgo III with Rubin Observatory Data Preview 2

We analyze the ultra-faint Milky Way satellite Virgo III using data from the Vera C. Rubin Observatory Data Preview 2 (DP2). Virgo III was observed in the Rubin "Cosmic Treasure Chest" (M49) First Look field, which contains 924 visits in the u,g,r,i bands comprising ~10.5hrs of exposure time with LSSTCam. These data are considerably deeper than the majority of DP2, with a $5σ$ limiting magnitude that approaches the expected 10-year depth of LSST (~25.2-26.5mag, depending on band). We report the morphological and stellar population parameters of Virgo III measured with the maximum-likelihood-based package ugali. The depth of the Rubin imaging yields more than a factor of four increase in the number of candidate member stars ($N_* = 114^{+11}_{-11}$) relative to the Virgo III discovery results ($N_* = 25^{+5}_{-4}$), enabling significantly more precise morphological constraints. Our best-fit parameters broadly agree with previous measurements, further confirming that Virgo III has properties that are consistent with an ultra-faint dwarf galaxy ($M_V = -2.72^{+0.49}_{-0.70}$; $r_{1/2} = 53^{+10}_{-8}$) located at a heliocentric distance of $D_\odot = 151^{+8}_{-8}$. We also demonstrate that the depth and photometric quality of the DP2 data are sufficient to separate metal-poor and metal-rich stars in color-color space. We further present period estimates for the three known RR Lyrae member stars derived from the DP2 forced photometry; we use theoretical Period-Luminosity-Metallicity (PLZ) and Period-Wesenheit-Metallicity (PWZ) relations to obtain independent distance estimates. We find that our period and distance estimates are broadly consistent with previous measurements for these RR Lyrae. These results demonstrate the power of LSST data for the discovery and characterization of ultra-faint dwarf galaxies and motivate future searches for new satellites across the southern sky.

Pai, Aashay [Chicago U.; Chicago U., KICP; SkAI, C↗
DOE OSTI2024

Optimal Stopping Ages for Colorectal Cancer Screening

Importance Prior studies have shown that the benefits, harms, and costs of colorectal cancer (CRC) screening at older ages are associated with a patient’s sex, health, and screening history. However, these studies were hypothetical exercises and not directly informed by data on CRC risk. Objective To identify the optimal stopping ages for CRC screening by sex, comorbidity, and screening history from a cost-effectiveness perspective. Design, Setting, and Participants This economic evaluation first validated the MISCAN-Colon (Microsimulation Screening Analysis–Colon) model against community-based CRC incidence and mortality rates for 2 subcohorts of the PRECISE (Optimizing Colorectal Cancer Screening Precision and Outcomes in Community-Based Populations) cohort. Subsequently, different CRC screening scenarios were simulated in older individuals. Cohorts of US adults aged 76 to 90 years varied by sex and comorbidity status (none, low, moderate, or severe). Statistical and sensitivity analyses were performed from March 2023 to May 2024. Exposures CRC screening histories including fecal immunochemical test (FIT) or colonoscopy, such as a negative colonoscopy result from 10, 15, 20, 25, or 30 years before the index age; 1 to 5 negative FIT results within 5 years of the index age, with different patterns of recency; or a combination of negative colonoscopy and negative FIT results. Main Outcomes and Measures The main outcomes included estimated lifetime clinical outcomes, incremental costs, and quality-adjusted life-years gained (QALYG) associated with 1 additional FIT or colonoscopy. Optimal stopping age for screening, defined as the oldest age for which the incremental cost-effectiveness ratio was still below the willingness-to-pay threshold of $\$$100 000 per QALYG, was evaluated. Results The first of the 2 PRECISE subcohorts used in validating the simulation model included 25 974 adults (15 060 females [58.0%]; 54.7% aged 76 to 80 years) with a negative colonoscopy result 10 years before the index date. The second subcohort consisted of 118 269 adults (67 058 females [56.7%]; 90.5% aged 76 to 80 years) with a negative FIT result 1 year before the index date. Older age, male sex, higher comorbidity levels, and recent CRC screenings were associated with reduced incremental benefit and cost-effectiveness of additional screening. For the reference cohort of 76-year-old females without comorbidities and a negative colonoscopy result 10 years before the index age, 1 additional colonoscopy cost $\$$38 226 per QALYG. For cohorts with otherwise equivalent characteristics, associated costs increased to $\$$1 689 945 per QALYG for females at age 90 years without comorbidities and a negative colonoscopy results 10 years before the index age, $\$$51 604 per QALYG for males at age 76 years without comorbidities and a negative colonoscopy result 10 years before the index age, and $\$$108 480 per QALYG for females at age 76 years with severe comorbidities and a negative colonoscopy result 10 years before the index age and decreased to $\$$16 870 per QALYG for females without comorbidities and a negative colonoscopy result 30 years before the index age. The optimal stopping ages across different cohorts ranged from younger than 76 to 86 years for colonoscopy and younger than 76 to 88 years for FIT. Conclusions and Relevance In this economic evaluation, age, sex, screening history, comorbidity, and future screening modality were associated with the clinical outcomes, cost-effectiveness, and optimal stopping age for CRC screening. These results can inform guideline development and patient-directed informed decision-making.

Harlass, Matthias [Erasmus Erasmus University Medi↗
DOE OSTI2024

Strain‐Driven Stabilization of a Room‐Temperature Chiral Multiferroic with Coupled Ferroaxial and Ferroelectric Order

Noncollinear ferroic materials are sought after as testbeds to explore the intimate connections between topology and symmetry, which result in electronic, optical, and magnetic functionalities not observed in collinear ferroic materials. For example, ferroaxial materials have rotational structural distortions that break mirror symmetry and induce chirality. When ferroaxial order is coupled with ferroelectricity arising from a broken inversion symmetry, it offers the prospect of electric-field-control of the ferroaxial distortions and opens up new tunable functionalities. However, chiral multiferroics, especially ones stable at room temperature, are rare. A strain-stabilized, room-temperature chiral multiferroic phase in single crystals of BaTiS 3 is reported here. Using first-principles calculations, the stabilization of this multiferroic phase having P6 3 space group for biaxial tensile strains exceeding 1.5% applied on the basal ab-plane of the room temperature P6 3 cm phase of BaTiS 3 is predicted. The chiral multiferroic phase is characterized by rotational distortions of TiS 6 octahedra around the long c-axis and polar displacement of Ti atoms along the c-axis. Furthermore, the ferroaxial and ferroelectric distortions and their domains in P6 3 -BaTiS 3 are directly resolved using atomic resolution scanning transmission electron microscopy. Landau-based phenomenological modeling predicts a strong coupling between the ferroelectric and the ferroaxial order making P6 3 -BaTiS 3 an attractive test bed for achieving electric-field-control of chirality.

Chalcogenide↗
DOE OSTI2025

Role of Histidine‐Containing Peptoids in Accelerating the Kinetics of Calcite Growth

Carbonate mineralization, the conversion of CO 2 into stable, thermodynamically favorable carbonate minerals, offers a promising strategy for permanent and environmentally friendly carbon storage, with minimal risk of long-term leakage and minimal monitoring requirements. Drawing inspiration from carbonic anhydrase (CA), a family of zinc-containing metalloenzymes that catalyze the hydration of CO 2 to bicarbonate and promote carbonate precipitation, a class of histidine-containing peptoids was designed that is capable of coordinating with Zn 2+ ions to act as CA mimetics for accelerating calcite step growth. In situ atomic force microscopy (AFM) measurements reveal that these peptoids significantly enhance step advancement, with a more pronounced effect observed when combined with Zn 2+ ions and under higher calcium-to-carbonate activity ratios, indicating that peptoids facilitate the incorporation of CO 3 2− ions at step edges. Solution NMR and 3D atomic force microscopy (3D AFM) analyses show that the coordination of peptoids with Zn 2+ promotes both the deprotonation of HCO 3 − to CO 3 2− and restructures the interfacial hydration layers of calcite, collectively lowering the activation barrier for step growth. These findings establish a design framework for sequence-defined polymers to regulate carbonate mineralization, offering promising applications in CO 2 capture and long-term storage.

CO2 mineralization↗
DOE OSTI2025

Nonresonant Raman Control of Ferroelectric Polarization

Important advances is recently made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light–matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes is experimentally observed and proposed for dynamic material control, but the resulting atomic excursion is limited to perturbative levels. Here, in this study, this challenge is overcome by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, ferroelectric reversal is induced in lithium niobate, and the large-amplitude mode displacements are characterized through femtosecond stimulated Raman scattering and second harmonic generation. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds.

Shi, Jiaojian [Stanford Univ., CA (United States);↗
DOE OSTI2025

Increased Voltage in CdSe Solar Cells by Mitigation of Charge Carrier Trapping Due to Se Vacancies

Cadmium selenide (CdSe), with a 1.7 eV bandgap, is a promising high-bandgap semiconductor for tandem solar cells, yet device efficiencies are hindered by rapid minority carrier recombination. Here, in this study, polycrystalline CdSe solar cells are investigated using radiative emission spectroscopy, time-resolved photoluminescence, and density functional theory, revealing fast (sub-nanosecond) minority carrier trapping by selenium vacancy-related defect states with densities of (5–50) × 10 17 cm −3 , limiting carrier mobility and increasing recombination. By reducing absorber thickness to ≈0.5 µm, trapping effects are mitigated, achieving a record open-circuit voltage of 917 mV, a 165 mV improvement over prior reports. These findings clarify the role of Se vacancies in limiting CdSe solar cell performance and provide insights applicable to CdSe and CdSeTe thin-film photovoltaics. This work advances understanding of defect-mediated losses in II–VI semiconductors and suggests pathways for improving solar cell performance through defect control.

14 SOLAR ENERGY↗
DOE OSTI2025

Soft and Stretchable Thienopyrroledione–Based Polymers via Direct Arylation

π-conjugated polymers (CPs) that are concurrently soft and stretchable are needed for deformable electronics. Molecular-level modification of indacenodithiophene (IDT) copolymers, a class of CPs that exhibit high hole mobilities (μ hole ), is an approach that can help realize intrinsically soft and stretchable CPs. Numerous examples of design strategies to adjust the stretchability of CPs exist, but imparting softness is comparatively less studied. In this study, a systematic molecular weight (MW) series is constructed on a promising candidate for soft CPs, poly(indacenodithiophene-co-thienopyrroledione) (p(IDT C16- TPD C8 )), by optimizing direct arylation polymerization conditions in hopes of improving stretchability and μhole without significantly impacting softness. We found p(IDT C16- TPD C8 ) at a degree of polymerization of 32 shows high stretchability (crack onset strain, CoS > 100%) without significantly impacting softness (elastic modulus, E = 32 MPa), which to the best of our knowledge outperforms previously reported stretchable and soft CPs. To further study how molecular-level modifications impact polymer properties, a MW series of a new extended donor unit polymer, poly(indacenodithienothiophene-co-thienopyrroledione) (p(IDTT C16- TPD C8 )), was synthesized. The IDTT C16 copolymers did not result in a greater average μ hole when comparing between p(IDTT C16- TPD C8 ) and p(IDT C16- TPD C8 ) despite their higher crystallinity observed by GIWAXS. While these findings warrant further investigation, this study points toward unique charge transport properties of IDT-based polymers.

direct arylation polymerization↗
DOE OSTI2025

Performance Comparisons for Artificially Propagated and Wild Pacific Lamprey Juveniles and Larvae

ABSTRACT Artificially propagated Pacific lamprey ( Entosphenus tridentatus ) are produced for restoration and for use in dam passage studies to reduce the demand for wild fish. Such uses require that animals are representative of their wild counterparts. Previous work indicated that this is true for Pacific lamprey larvae and juveniles reared in the hatchery with respect to the length of sustained swimming. However, more subtle differences in behaviour and performance that lamprey need to survive have not been assessed. In this study, artificially propagated and wild fish were compared in laboratory tests under no‐flow conditions to examine light avoidance, burrowing speed, burst swim speed, volitional routine swim speed and time to come to rest. Most larvae burrowed in less than a minute, and we found highly significant differences ( p < 0.001) between artificially propagated and wild larvae burrowing times, a critical escape behaviour. This could have implications for studies of larval entrainment at irrigation diversion canals or in turbine boils at dams. Interestingly, all of the wild juveniles tested came to rest quickly after introduction to the chamber (1.5 min), while artificially propagated lamprey swam robotically near the surface and 48% did not come to rest in the first 10 min (median time to rest = 9.5 min). In contrast, wild juveniles quickly (median = 1.47 min) sought areas near the tank bottom and attached strongly with their oral disc. Such behavioural differences could have important survival consequences for artificially propagated lamprey as they approach turbine intakes, bypass screens and irrigation diversion headgates. This study highlights the need to conduct behavioural assays that examine subtleties of fish behaviour that can be missed with traditional swim tunnel comparisons.

Frick, Kinsey [Fish Ecology Division, Northwest Fi↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

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

Viral Dynamic Models During COVID‐19: Are We Ready for the Next Pandemic?

Mathematical models have been used for about 30 years to improve our understanding of virus-host interaction, in particular during chronic infections. During the COVID-19 pandemic, these models have been used to provide insights into the natural history of acute SARS-CoV-2 infection, optimize antiviral treatment strategies, understand factors associated with transmission, and optimize surveillance systems. The impact of modeling has been accelerated by the availability of unprecedented multidimensional immune data from animal and human systems, which enhanced partnerships between experimentalists and theorists and led to exciting new modeling and statistical developments. In this mini review, we examine the lessons learned from the COVID-19 pandemic and discuss the main insights provided by mathematical models of viral dynamics at the different stages of the outbreak. Although we focus on respiratory infection, we also consider the new areas for development in anticipation of future acute infections from new or reemerging pathogens.

59 BASIC BIOLOGICAL SCIENCES↗