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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗
DOE OSTI2020

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Structure-guided discovery of ancestral CRISPR-Cas13 ribonucleases

The RNA-guided ribonuclease CRISPR-Cas13 enables adaptive immunity in bacteria and programmable RNA manipulation in heterologous systems. Cas13s share limited sequence similarity, hindering discovery of related or ancestral systems. Here, to address this, we developed an automated structural-search pipeline to identify an ancestral clade of Cas13 (Cas13an) and further trace Cas13 origins to defense-associated ribonucleases. Despite being one-third the size of other Cas13s, Cas13an mediates robust programmable RNA depletion and defense against diverse bacteriophages. However, unlike its larger counterparts, Cas13an uses a single active site for both CRISPR RNA processing and RNA-guided cleavage, revealing that the ancestral nuclease domain has two modes of activity. Discovery of Cas13an deepens our understanding of CRISPR-Cas evolution and expands opportunities for precision RNA editing, showcasing the promise of structure-guided genome mining.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure

Abstract The distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle. This study provides a high‐resolution portrait of genomic variation in Chinook salmon ( Oncorhynchus tshawytscha ) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered data set of ~13 million single nucleotide polymorphisms (SNPs) from low‐coverage whole genome resequencing of 53 populations (3566 barcoded individuals), we contrasted patterns of genomic structure within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Neutral variation provided support for fine‐scale structure of populations, while allele frequency variation in GREB1L/ROCK1 was highly correlated with mean return timing for early and late migrating populations within each of the lineages ( r 2 = .58–.95; p < .001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream‐type) compared to the other two major lineages, which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.

Horn, Rebekah L.↗
DOE OSTI2020

A conservative phase-space moving-grid strategy for a 1D-2V Vlasov–Fokker–Planck Solver

In this work, we develop a conservative configuration- and velocity-space (i.e., phase-space) moving-grid strategy for the Vlasov–Fokker–Planck (VFP) equation in a planar geometry. The velocity-space grid is normalized and shifted in terms of the thermal speed and the bulk-fluid velocity, respectively. The configuration-space grid is moved according to a mesh-motion-partial-differential equation (MMPDE), which equidistributes a monitor function that is inversely proportional to the gradient-length scales of the macroscopic plasma quantities. The resulting inertial terms in the transformed VFP equations are discretized to ensure the discrete conservation of mass, momentum, and energy. To satisfy the discrete conservation theorems in the presence of phase-space mesh motion, we employ the method of discrete nonlinear constraints – explored in previous studies – but the underlying symmetries are determined in a much more efficient manner than before. The conservative grid-adaptivity strategy provides an efficient scheme that resolves important physical structures in the phase-space while controlling the computational complexity at all times. We demonstrate the favorable features of the proposed algorithm through a set of test cases of increasing complexity. The problems test independent components of the algorithms, as well as the integrated capability on settings relevant to inertial confinement fusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Bone: An Outstanding Composite Material

Bone is an outstanding, well-designed composite. It is constituted by a multi-level structure wherein its properties and behavior are dependent on its composition and structural organization at different length scales. The combination of unique mechanical properties with adaptive and self-healing abilities makes bone an innovative model for the future design of synthetic biomimetic composites with improved performance in bone repair and regeneration. However, the relation between structure and properties in bone is very complex. In this review article, we intend to describe the hierarchical organization of bone on progressively greater scales and present the basic concepts that are fundamental to understanding the arrangement-based mechanical properties at each length scale and their influence on bone’s overall structural behavior. The need for a better understanding of bone’s intricate composite structure is also highlighted.

Rosa, Natacha (ORCID:0000000199008851)↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2022

Training neural networks using Metropolis Monte Carlo and an adaptive variant

Abstract We examine the zero-temperature Metropolis Monte Carlo (MC) algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, Metropolis MC can train a neural net with an accuracy comparable to that of gradient descent (GD), if not necessarily as quickly. The Metropolis algorithm does not fail automatically when the number of parameters of a neural network is large. It can fail when a neural network’s structure or neuron activations are strongly heterogenous, and we introduce an adaptive Monte Carlo algorithm (aMC) to overcome these limitations. The intrinsic stochasticity and numerical stability of the MC method allow aMC to train deep neural networks and recurrent neural networks in which the gradient is too small or too large to allow training by GD. MC methods offer a complement to gradient-based methods for training neural networks, allowing access to a distinct set of network architectures and principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Influence of extended interactions on spin dynamics in one-dimensional cuprates

Quasi-one-dimensional (1D) materials provide a unique platform for understanding the importance and influence of extended interactions on the physics of strongly correlated systems due to their relative structural simplicity and the existence of powerful theoretical tools well-adapted to one spatial dimension. Recently, this was highlighted by anomalous observations in the single-particle spectral function A(q, ω) of 1D cuprate chain compounds, measured by angle-resolved photoemission spectroscopy (ARPES), which were explained by the presence of a long-range attractive interaction. Such an extended interaction should leave its fingerprints on other observables, notably the dynamical spin structure factor S(q, ω), measured by neutron scattering or resonant inelastic x-ray scattering (RIXS). Starting from a simple Hubbard Hamiltonian in 1D and using time-dependent density matrix renormalization group (tDMRG) methods, we show that the presence of long-range attractive coupling, directly through an instantaneous Coulomb interaction V or retarded electron phonon (el-ph) coupling, can introduce significant spectral weight redistribution in S(q, ω) across a wide range of doping. Here, this underscores the significant impact that extended interactions can have on dynamical correlations among particles, and the importance of properly incorporating this influence in modeling. Our results demonstrate that S(q, ω) can provide a sensitive experimental constraint, which complements ARPES measurements, in identifying key interactions in 1D cuprates, beyond the standard Hubbard model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Adaptation strategies of giant viruses to low-temperature marine ecosystems

Abstract Microbes in marine ecosystems have evolved their gene content to thrive successfully in the cold. Although this process has been reasonably well studied in bacteria and selected eukaryotes, less is known about the impact of cold environments on the genomes of viruses that infect eukaryotes. Here, we analyzed cold adaptations in giant viruses (Nucleocytoviricota and Mirusviricota) from austral marine environments and compared them with their Arctic and temperate counterparts. We recovered giant virus metagenome-assembled genomes (98 Nucleocytoviricota and 12 Mirusviricota MAGs) from 61 newly sequenced metagenomes and metaviromes from sub-Antarctic Patagonian fjords and Antarctic seawater samples. When analyzing our data set alongside Antarctic and Arctic giant viruses MAGs already deposited in the Global Ocean Eukaryotic Viral database, we found that Antarctic and Arctic giant viruses predominantly inhabit sub-10°C environments, featuring a high proportion of unique phylotypes in each ecosystem. In contrast, giant viruses in Patagonian fjords were subject to broader temperature ranges and showed a lower degree of endemicity. However, despite differences in their distribution, giant viruses inhabiting low-temperature marine ecosystems evolved genomic cold-adaptation strategies that led to changes in genetic functions and amino acid frequencies that ultimately affect both gene content and protein structure. Such changes seem to be absent in their mesophilic counterparts. The uniqueness of these cold-adapted marine giant viruses may now be threatened by climate change, leading to a potential reduction in their biodiversity.

Environmental Sciences & Ecology↗
DOE OSTI2024

Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues

Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Driving Electron Transfer in Photosystem I Using Far-Red Light: Overall Perspectives

Photosystem I (PSI) is a photosynthetic protein–pigment complex that, upon photoexcitation, transfers electrons to ferredoxin, facilitating the production of NADPH. Isolated PSI reaction centers (RCs) have also been used in hybrid systems to reduce protons and produce ‘biohydrogen’. This review article examines how various cyanobacteria with similar photosynthetic machinery utilize different wavelengths of light to execute photosynthetic electron transport through PSI. Key factors, such as, the structure of the electron transfer cofactors, the protein environment surrounding the primary donor pigments and hydrogen-bonding interactions with the surrounding protein matrix are analyzed to understand their roles in maintaining efficient electron transfer when it is driven using photons of different energies. We compare PSI complexes with known atomic structures from four species of cyanobacteria, Thermosynechococcus elongatus, Acaryochloris marina, Halomicronema hongdechloris, and Fischerella thermalis. T. elongatus is typical of most oxygenic photosynthetic organisms in that it requires visible light and uses only chlorophyll a (Chl a ) in PSI. In contrast, H. hongdechloris and F. thermalis are photoacclimating species capable of producing Chl f and Chl d that use red light when little visible light is available. A. marina , on the other hand, is adapted to red light conditions and consistently utilizes Chl d as its primary photosynthetic pigment, maintaining a stable pigment composition. Here, we explore the structural and functional differences between the PSI RCs of these organisms and the impact of these differences on electron transport. The structural differences in the cofactors influence both the absorption wavelengths of the cofactors and the energy levels of the intermediate states of electron transfer. An analysis of the surrounding protein shows how it has been adapted and underscores the interplay between the pigment structure, protein environment, and hydrogen bonding networks in tuning the efficiency and adaptability of photosynthetic mechanisms across different species of cyanobacteria.

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

Bridging the Gap Between Astronomical Datasets: From Proof-of-Concept to AI Model Deployment with Domain Adaptation

Artificial Intelligence is transforming astrophysics, from studying stars and galaxies to analyzing cosmic large-scale structures. However, a critical challenge arises when AI models trained on simulations or past observational data are applied to new observation— leading to domain shifts, reduced robustness, and increased uncertainty of model predictions. This talk will explore these issues, highlighting examples such as galaxy morphology classification and cosmological parameter inference, where AI struggles to adapt across different datasets. We will discuss domain adaptation as a strategy to improve model generalization and mitigate biases—essential for making AI-driven discoveries reliable. Notably, these challenges extend beyond astrophysics, affecting AI applications across physics and other scientific domains. Addressing them is essential for maximizing AI’s impact in advancing scientific research.

Ćiprijanović, Aleksandra [Fermilab]↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
DOE OSTI2021

Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Genotype to ecotype in niche environments: adaptation of Arthrobacter to carbon availability and environmental conditions

Niche environmental conditions influence both the structure and function of microbial communities and the cellular function of individual strains. The terrestrial subsurface is a dynamic and diverse environment that exhibits specific biogeochemical conditions associated with depth, resulting in distinct environmental niches. Here, we present the characterization of seven distinct strains belonging to the genus Arthrobacter isolated from varying depths of a single sediment core and associated groundwater from an adjacent well. We characterized genotype and phenotype of each isolate to connect specific cellular functions and metabolisms to ecotype. Arthrobacter isolates from each ecotype demonstrated functional and genomic capacities specific to their biogeochemical conditions of origin, including laboratory-demonstrated characterization of salinity tolerance and optimal pH, and genes for utilization of carbohydrates and other carbon substrates. Analysis of the Arthrobacter pangenome revealed that it is notably open with a volatile accessory genome compared to previous pangenome studies on other genera, suggesting a high potential for adaptability to environmental niches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.

Bayesian optimization↗