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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An experimental and kinetic modeling study of the pyrolysis of isoprene, a significant biogenic hydrocarbon in naturally occurring vegetation fires

Isoprene dominates the carbon flux emitted by vegetation and constitutes 40% of non-methane biogenic emissions worldwide. Despite pyrolysis experiments at temperatures above 1000 K showing a link between isoprene combustion and aromatic species formation, comprehensive mechanistic research on isoprene is scarce in the literature. Here, in this work, we carry out an experimental and theoretical study to build, for the first time, a chemical kinetic model describing isoprene pyrolysis. The formation of polycyclic aromatic hydrocarbon (PAH) precursor species, often observed in vegetation fire plumes, is partially explained by isoprene pyrolysis experiments and theoretical modeling. Molecular dynamics (MD) simulations unveil reaction pathways from allylic isoprenyl radicals to allene and cyclopentadiene (CPD) intermediates, two relevant species detected in the experiments. Rate constants for these identified pathways are calculated using variational transition state theory to update the kinetic model, which is validated against single-pulse shock tube (SPST), and jet-stirred reactor (JSR) experimental data in the temperature range of 850–1690 K. The kinetic model presents satisfactory agreement with the SPST experimental data, and a reaction pathway analysis shows that association of propargyl radicals results in benzene formation. The JSR pathway analysis also identifies the prominent reactions for CPD, benzene, styrene, and toluene formation. Our model does not reproduce the CPD experimental profiles, indicating that additional studies are necessary. Overall, our findings advance the understanding of isoprene pyrolysis and its related atmospheric pollutants in naturally occurring vegetation fires where smoldering and oxygen-deficient combustion processes are present.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A comprehensive experimental and kinetic modeling study of di-isobutylene isomers: Part 1

We report di-isobutylene has received significant attention as a promising fuel blendstock, as it can be synthesized via biological routes and is a short-listed molecule from the Co-Optima initiative. Di-isobutylene is also popularly used as an alkene representative in multi-component surrogate models for engine studies of gasoline fuels. However, there is limited experimental data available in the literature for neat di-isobutylene under engine-like conditions. Hence, most existing di-isobutylene models have not been extensively validated, particularly at lower temperatures (< 1000 K). Most gasoline surrogate models include the di-isobutylene sub-mechanism published by Metcalfe et al. with little or no modification. The current study is undertaken to develop a detailed kinetic model for di-isobutylene and validate the model using a wide range of relevant experimental data. Part 1 of this study exclusively focuses on the low- to intermediate temperature kinetics of di-isobutylene. An upcoming part 2 discusses the high-temperature model development and validation of the relevant experimental targets. Ignition delay time measurements for the di-isobutylene isomers were performed at pressures ranging from 15 - 30 bar at equivalence ratios of 0.5, 1.0, and 2.0 diluted in air and in the temperature range 650 - 900 K using two independent rapid compression machine facilities. In addition, measurements of species identified during the oxidation of these isomers were performed in a jet-stirred reactor and in a rapid compression machine. A detailed kinetic model for the di-isobutylene isomers is developed to capture the wide range of new experimental targets. For the first time, a comprehensive low-temperature chemistry submodel is included. The differences in the important reaction pathways for the accurate prediction of the oxidation of the two DIB isomers are compared using reaction path analysis. In conclusion, the most sensitive reactions controlling the ignition delay times of the DIB isomers under the pressure and temperature conditions necessary for autoignition in engines are identified.

09 BIOMASS FUELS↗
DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗
DOE OSTI2022

A combined experimental and modelling study of granite hydrothermal alteration

Geochemical reactions can induce significant changes of rock reservoir porosity and permeability via mineral dissolution and precipitation processes, affecting the long-term fluid behaviour within various geological systems. Here, the understanding and quantification of these reactions rely on field and experimental studies and on the predictions of reactive transport models. The present study was aimed at assessing the extent to which current geochemical models integrating available mineral dissolution/precipitation rate equations can reproduce the experimental data obtained from 4 to 17-day long hydrothermal alteration experiments of a muscovite-biotite granite and, thus, help provide an accurate description of the evolution of geothermal systems within granitic reservoirs. The experiments were conducted at a constant temperature of 180 °C and over an aqueous fluid pH range of 2 to 8.5, using both mixed-flow and static batch reactors. Modelled major element (K, Al, Si, Ca, and Mg) concentrations were generally in satisfactory agreement with the corresponding measured elemental fluxes – the differences between modelled and experimental values were generally within a factor of 5 – and the predicted identity and mass of formed secondary phases were consistent with the microscopic observations of the reacted solids. However, larger differences between measured and modelled element concentrations were observed when significant amounts of secondary phases formed, notably at pH 2 to 3, and for longer-term batch experiments. Much of this concentration difference stems from the underestimation of the amounts of Al-phases formed at acid to near-neutral pH. Although an idealized rock composition was considered, the observed mismatch between model calculations and experimental data can be attributed to inadequate mineral precipitation reaction rates and a poor description of reactive surface areas in existing geochemical modelling codes. More accurate quantification of precipitation kinetics, including nucleation and growth, and improved descriptions of the temporal change of mineral surface area would enhance the predictive capabilities of reactive transport models and benefit, particularly, the efforts aimed at increasing the sustainability of EGS reservoirs.

58 GEOSCIENCES↗
DOE OSTI2020

Optimal experimental design for prediction based on push-forward probability measures

Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the pushforward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Experimental and numerical investigation of hydrate-guest molecule exchange kinetics

The 2012 Ignik Sikumi #1 Field Trial in Alaska was designed to test at field scale a methane hydrate production methodology that involves injecting carbon dioxide (CO 2 ) in situ to exchange with methane (CH 4 ) within a hydrate structure, thus releasing the methane for production. Since the completion of the field trial, experimental and numerical investigations have sought to better understand observations in terms of CH 4 production and changes to the hydrate bearing formation. Collectively, these insights may lead to more effective strategies for producing CH 4 from naturally occurring hydrates. This study presents results from a laboratory experiment performed at conditions experienced during the field trial (8.27 MPa and 275.15 K) to collect data relating to the CH 4 molecular exchange process. The experiment intentionally isolated the kinetic guest molecule exchange process from additional hydrate formation and mechanical changes to the hydrate bearing sand. Data were used to inform development of numerical models that were then used to fit key hydrate parameters (bound water saturation, kinetic formation constant, kinetic exchange constant, and preferential exchange weighting factors) and analyze experimental results. Results were in agreement with observations of preferential CO 2 /CH 4 guest molecule exchange at Ignik Sikumi #1 and from previous experimental studies while providing quantitative estimates of changing hydrate compositions. Additionally, simulations confirmed hydrate behavior and composition profiles otherwise indirectly evidenced by experimental results.

03 NATURAL GAS↗
DOE OSTI2020

An experimental system and procedure of unsteady-state relative permeability test for gas hydrate-bearing sediments

Reliable estimations of the relative permeability of gas and water in hydrate-bearing sediments (HBS) and the dependency of the relative permeability on hydrate saturation are critical to predict the productivity of a hydrate reservoir. Yet, this remains poorly estimated owing to lack of experimental data associated with difficulties in conducting multiphase flow experiments in HBS. Recognizing the experimental challenges, this study intends to develop and validate a new experimental system and procedure of unsteady-state relative permeability test that can generate reliable and reproducible flow measurements in HBS. Furthermore, gas hydrate is considered as a part of solid matrix in the sediment, so one of the challenges is to maintain a constant hydrate saturation, which is achieved in this experimental study using tight pressure-temperature (P-T) control near the hydrate stability boundary. The measured differential pressure across the specimen, methane injection flow rate, and volume of displaced brine are used to calculate the relative permeability by adopting a conventional Buckley-Leverett theory-based interpretation method. Residual brine saturation calculated for the hydrate-bearing specimen is higher than that of hydrate-free specimen, presumably due to decrease in pore size, increase in heterogeneity of solid matrix, and increase in size distribution of solid matrix and pore in the presence of hydrates. Further studies are necessary to represent the results of the unsteady-state flow experiment in HBS with a gas hydrate-dependent relative permeability model.

03 NATURAL GAS↗
DOE OSTI2016

Dependence of short and intermediate-range order on preparation in experimental and modeled pure a-Si

Variability in the short-to-intermediate range order of pure amorphous silicon prepared by different experimental and computational techniques is probed by measuring mass density, atomic coordination, bond-angle deviation, and dihedral angle deviation. It is found that there is significant variability in order parameters at these length scales in this archetypal covalently bonded, monoatomic system. This diversity strongly reflects preparation technique and thermal history in both experimental and simulated systems. Experiment and simulation do not fully quantitatively agree, partly due to differences in the way parameters are accessed. However, qualitative agreement in the trends is identified. Relaxed forms of amorphous silicon closely resemble continuous random networks generated by a hybrid method of bond-switching Monte Carlo and molecular dynamics simulation. As-prepared ion implanted amorphous silicon can be adequately modeled using a structure generated from amorphization via ion bombardement using energetic recoils. Preparation methods which narrowly avoid crystallization such as experimental pressure-induced amorphization or simulated melt-quenching result in inhomogeneous structures that contain regions with significant variations in atomic ordering. Ad hoc simulated structures containing small (1 nm) diamond cubic crystal inclusions were found to possess relatively high bond-angle deviations and low dihedral angle deviations, a trend that could not be reconciled with any experimental material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interpreting experimental measurements of helium bubbles using stochastic cluster dynamics models of heterogeneous nucleation and growth in irradiated ferritic alloys

Among a number of other advantageous features, ferritic/martensitic steels are being considered as fusion reactor structural materials due to their low intrinsic swelling under irradiation. However, under high-energy neutron irradiation, He produced through (n, α) reactions stabilizes vacancy clusters, which then act as seeds for bubble formation and growth, negating the intrinsic swelling resistance of these alloys. Standard models of irradiation damage accumulation and microstructural evolution consider homogeneous nucleation as the basis for bubble formation and growth. However, this generally does not explain the large bubble densities and sizes observed experimentally under a number of different conditions. Here, we propose a more realistic physical model of bubble nucleation, formation, and growth designed to capture recent experimental measurements of He-bubble formation and evolution during co-implantation of He and Fe ions in model ferritic alloys. We find that experimental results are explained only when the following three features are considered simultaneously: (i) heterogeneous nucleation of He-vacancy bubbles at defect sinks (e.g., dislocations, grain boundaries, and second-phase precipitates), (ii) nucleation and growth of bubbles via the ‘trap mutation’ mechanism (i.e., spontaneous production of Frenkel pairs due to absorption of He atoms), and (iii) transition from a growth-limited, He-stabilized bubble regime to a ‘runaway’ void-type growth scenario. The model is implemented into a stochastic cluster dynamics framework that takes advantage of cluster size grouping methods to accelerate the simulations, allowing us to reach 10 dpa of simulated irradiated dose, and to capture cluster sizes in excess of 20 nm. Further, a careful extrapolation exercise conducted assuming classical nucleation theory leads to excellent agreement with the experimental measurements at 50 dpa of irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗
DOE OSTI2016

Review of the theoretical and experimental status of dark matter identification with cosmic-ray antideuterons

Recent years have seen increased theoretical and experimental effort towards the first-ever detection of cosmic-ray antideuterons, in particular as an indirect signature of dark matter annihilation or decay. In contrast to indirect dark matter searches using positrons, antiprotons, or γ-rays, which suffer from relatively high and uncertain astrophysical backgrounds, searches with antideuterons benefit from very suppressed conventional backgrounds, offering a potential breakthrough in unexplored phase space for dark matter. This article is based on the first dedicated cosmic-ray antideuteron workshop, which was held at UCLA in June 2014. It reviews broad classes of dark matter candidates that result in detectable cosmic-ray antideuteron fluxes, as well as the status and prospects of current experimental searches. The coalescence model of antideuteron production and the influence of antideuteron measurements at particle colliders are discussed. This is followed by a review of the modeling of antideuteron propagation through the magnetic fields, plasma currents, and molecular material of our Galaxy, the solar system, the Earth’s geomagnetic field, and the atmosphere. Lastly, the three ongoing or planned experiments that are sensitive to cosmic-ray antideuterons, BESS, AMS-02, and GAPS, are detailed. As cosmic-ray antideuteron detection is a rare event search, multiple experiments with orthogonal techniques and backgrounds are essential. Furthermore, the combination of AMS-02 and GAPS antideuteron searches is highly desirable. Many theoretical and experimental groups have contributed to these studies over the last decade, this review aims to provide the first coherent discussion of the relevant dark matter theories that antideuterons probe, the challenges to predictions and interpretations of antideuteron signals, and the experimental efforts toward cosmic antideuteron detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Review and Outlook on Experimental Advances and Innovations in Geological CO 2 Storage: Insights from Depleted Gas Reservoirs and Saline Aquifers

Geological storage of carbon dioxide (CO 2 ) in depleted gas reservoirs and deep saline aquifers is a key part of global decarbonization efforts. As carbon capture and storage advances toward commercial-scale deployment, the credibility and scalability of laboratory experiments are increasingly vital for guiding safe and effective field implementation. This review offers a comprehensive, cross-scale evaluation of experimental methodologies, including core flooding, high-pressure, high-temperature systems, microfluidic visualization, and emerging systems such as multilayer commingled/compartmentalized core flooding, 3D-printed micromodels, and AI-powered digital twins. These innovations are demonstrated to enhance representativeness, reproducibility, and real-time insight, thereby addressing the limitations of conventional workflows. A critical analysis of methodological gaps, such as inconsistent pressure–temperature conditions, oversimplified brine chemistry, and a lack of standardization, reveals experimental sources of scale translation errors and performance uncertainty. By comparing the unique challenges of depleted gas reservoirs (such as low water saturation and legacy well leakage) to those of saline aquifers (including pressure buildup and caprock integrity), this review identifies formation-specific priorities for experimental design. Novel contributions include a synthesis of best practices, integration strategies for model calibration, and recommendations for standardizing core handling, saturation procedures, and reporting protocols. Furthermore, this work serves as a guide for developing robust, field-relevant experimental strategies that can increase the deployment and regulatory acceptance of CO 2 storage technologies at scale.

58 GEOSCIENCES↗
DOE OSTI2020

Experimental and DFT Calculated IR Spectra of Guests in Zeolites: Acyclic Olefins and Host-Guest Interactions

We performed experimental and periodic density functional theory (DFT) IR spectroscopy to investigate the adsorption of acyclic olefins over both acidic and non-acidic zeolites. Two conjugated polyenes, 2,4-dimethyl-1,3-pentadiene (I) and 2,6-dimethyl-2,4,6-octatriene (II) were studied to probe organic intermediates that can form during methanol conversion, and that can lead to deactivating species known collectively as “coke.” We computed vibrational spectra using zeolite-adsorbed and gas-phase models for both neutral and protonated forms of I and II, and compared these DFT results to diffuse reflectance IR Fourier transform (DRIFT) spectra of zeolite-guest systems. Our experimental and computational results are precise enough to pinpoint that the gauche s-cis conformation of species I predominates during adsorption over de-aluminated zeolite beta. Computed zeolite-adsorbed spectra of the protonated species I and II best represent the DRIFT spectra obtained after the adsorption of the olefins on HMOR at 20 ?, with computed bands at 1543 and 1562 cm–1 for molecules I+ and II+, respectively, attributed to the allylic stretching mode, ?(C=C–C+). These computed band frequencies are within 6 cm–1 of experimental data and confirm that the interaction between neutral acyclic olefins and acidic zeolites leads to protonation of the olefin. Comparison of computed spectra of the protonated species in the gas phase to those in the zeolite indicates that the electrostatic interaction between alkenyl and alkadienyl cations and negative zeolite framework does not significantly impact the position of the allylic stretching bands. These results highlight that computed spectroscopy and thermodynamics coupled with experimental spectra can be used for elucidating complex mixtures in zeolites, and certain spectral features of adsorbed olefins can be accurately modeled by gas-phase calculations. ACKNOWLEDGMENTS BM and SMA were supported by the National Science Foundation under Award # CBET-1512442. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support (or partial support) of this research (#589978-ND5, EDH and FCJ). MDB was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Division of Materials Science and Engineering. CJM was supported by the DOE, Office of Science, BES, Division of Chemical Sciences, Geosciences, and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated by Battelle for the U.S. DOE. Computing resources were generously provided through PNNL’s institutional computing (PIC).

Manookian, Babgen↗
DOE OSTI2019

Identifying the Chemical Origin of Oxygen Redox Activity in Li-Rich Anti-Fluorite Lithium Iron Oxide by Experimental and Theoretical X-ray Absorption Spectroscopy

Harnessing oxygen redox reactions is an intriguing route to increasing capacity in Li-ion batteries (LIBs). Despite numerous experimental and theoretical attempts to unravel the mechanism of oxygen redox behavior, the electronic origin of oxygen activities in energy storage of Li-rich LIB materials remains under intense debate. In this work, the onset of oxygen activity was examined using a Li-rich material that has been reported to exhibit oxygen redox, namely, Li 5 FeO 4 . By comparing experimental measurements and first-principles Bethe-Salpeter equation calculations of oxygen K-edge X-ray absorption spectra (XAS), it was found that experimentally-observed changes in XAS originate from the non-bonding oxygen states in cation-disordered delithiated Li 5 FeO 4 , and the spectral features of oxygen dimers were also determined. This combined experimental and theoretical study offers an effective approach to disentangle the intertwined signals in XAS and can be further utilized in broader contexts for characterizing other energy storage and conversion materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to ‘co-design’ chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improved AlphaFold modeling with implicit experimental information

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Abstract Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

59 BASIC BIOLOGICAL SCIENCES↗