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DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Accuracy of Thermodynamic Databases for Hydroxyapatite Dissolution Constant

We report that discrepancies have been noted in the solubility constant values of calcium phosphate minerals between various databases employed in widely-used aqueous speciation calculation software programs. This can cause serious errors in the calculated speciation of waters using these software programs. The aim of the present communication was to bring to light these discrepancies. Experimental determinations of the hydroxyapatite solubility product vary by as much as ten orders of magnitude as a result of experimental challenges related to the presence of impurities in the hydroxyapatite used, incongruent dissolution and the contamination of solutions with dissolved carbon dioxide. Finally, it is suggested that the value used in the database Thermo.dat is consistent with experimental data devoid of common experimental problems, while other common databases use values that lead to a vastly overestimated solubility of hydroxyapatite.

58 GEOSCIENCES↗

DOE OSTI2023

An Overview of the Molten Salt Thermal Properties Database–Thermophysical, Version 2.1.1 (MSTDB-TP v.2.1.1)

The current status of the Molten Salt Thermodynamic Database- Thermal Physical (MSTDB-TP) is reported. Building off of MSTDB-TP and through the direction of the Roadmap for thermal property measurement of Molten Salt Reactor systems, MSTDB-TP 2.1.1 has now been released containing a total 448 salt entries (data from 140+independent studies) compared to the original commit of 62, containing thermophysical properties including density, viscosity, thermal conductivity, and heat capacity. Along with the new database release, further advancement of Saline, a C++application programming interface (API), and a graphical user interface (GUI) has facilitated increased user/developer interaction with the database. Furthermore, estimation techniques, first principles calculations (Ab-Initio) and interpolation/extrapolation methods (Redlich-Kister/Muggianu), have shown great promise in the future of thermophysical property determination for filling out compositional spaces and investigating experimentally difficult salts (hazardous/expensive). This report describes the database composition, development and advancement of database tools, and the strategy of advancing and implementing estimation data into future iterations of the database for MSTDB-TP.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗

DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1992

Interval prediction in structural dynamic analysis

Methods for assessing the predictive accuracy of structural dynamic models are examined with attention given to the effects of modal mass, stiffness, and damping uncertainties. The methods are based on a nondeterministic analysis called 'interval prediction' in which interval variables are used to describe parameters and responses that are unknown. Statistical databases for generic modeling uncertainties are derived from experimental data and incorporated analytically to evaluate responses. Covariance matrices of modal mass, stiffness, and damping parameters are propagated numerically in models of large space structures by means of three methods. The test data tend to fall within the predicted intervals of uncertainty determined by the statistical databases. The present findings demonstrate the suitability of using data from previously analyzed and tested space structures for assessing the predictive accuracy of an analytical model.

Hasselman, Timothy K.↗

DOE OSTI2021

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R 2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Quantum material topology via defect engineering

Current approaches of discovering new topological quantum materials, such as (magnetic) Weyl materials, by screening material databases or by rational design suffer from experimental challenges in growing the material in the desired geometry, hampering their potential applications. We take an approach of defect-engineering a topology starting from systems with experimentally well-controlled growth . Using SnTe, an experimentally viable topological crystalline insulator, as our model system, we elucidate the different effects of breaking time-reversal, inversion, and mirror symmetries using magnetic dopant defects, to controllably induce a topological phase transition. To this effect, we compute the thermodynamics, magnetism, and band-structure topology of magnetically doped SnTe using a full ab initio approach. In this process, we have discovered that Cr-doped SnTe is a Weyl semimetal. We have computed its classic Fermi-arc experimental signature on the (001) surface and also showed that it has a large anamolous Hall conductivity (AHC) of ~ 250 Ω - 1 cm - 1 . We predict a large Curie temperature ( T c ) of 62 K even at 3.3 % doping, with both T c and AHC being tunable with Cr concentration, suggesting potential for room-temperature applications. Our study in general paves the way for defect-designing room-temperature topological quantum phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1997

Organometallic Polymeric Conductors

For aerospace applications, the use of polymers can result in tremendous weight savings over metals. Suitable polymeric materials for some applications like EMI shielding, spacecraft grounding, and charge dissipation must combine high electrical conductivity with long-term environmental stability, good processability, and good mechanical properties. Recently, other investigators have reported hybrid films made from an electrically conductive polymer combined with insulating polymers. In all of these instances, the films were prepared by infiltrating an insulating polymer with a precursor for a conductive polymer (either polypyrrole or polythiophene), and oxidatively polymerizing the precursor in situ. The resulting composite films have good electrical conductivity, while overcoming the brittleness inherent in most conductive polymers. Many aerospace applications require a combination of properties. Thus, hybrid films made from polyimides or other engineering resins are of primary interest, but only if conductivities on the same order as those obtained with a polystyrene base could be obtained. Hence, a series of experiments was performed to optimize the conductivity of polyimide-based composite films. The polyimide base chosen for this study was Kapton. 3-MethylThiophene (3MT) was used for the conductive phase. Three processing variables were identified for producing these composite films, namely time, temperature, and oxidant concentration for the in situ oxidation. Statistically designed experiments were used to examine the effects of these variables and synergistic/interactive effects among variables on the electrical conductivity and mechanical strength of the films. Multiple linear regression analysis of the tensile data revealed that temperature and time have the greatest effect on maximum stress. The response surface of maximum stress vs. temperature and time (for oxidant concentration at 1.2 M) is shown. Conductivity of the composite films was measured for over 150 days in air at ambient temperature. The conductivity of the films dropped only half an order of magnitude in that time. Films aged under vacuum at ambient temperature diminished slightly in conductivity in the first day, but did not change thereafter. An experimental design approach will be applied to maximize the efficiency of the laboratory effort. The material properties (initial and long term) will also be monitored and assessed. The experimental results will add to the existing database for electrically conductive polymer materials. Attachments: 1) Synthesis Crystal Structure, and Polymerization of 1,2:5,6:9,10-Tribenzo-3,7,11,13-tetradehydro(14) annulene. 2) Reinvestigation of the Photocyclization of 1,4-Phenylene Bis(phenylmaleic anhydride): Preparation and Structure of (5)Helicene 5,6:9,10-Dianhydride. 3) Preparation and Structure Charecterization of a Platinum Catecholate Complex Containing Two 3-Ethynyltheophone Groups. and 4) Rigid-Rod Polymers Based on Noncoplanar 4,4'-Biphenyldiamines: A Review of Polymer Properties vs Configuration of Diamines.

Youngs, Wiley J.↗

NASA NTRS2000

Cyclic Oxidation Testing and Modelling: A NASA Lewis Perspective

The Materials Division of the NASA Lewis Research Center has been heavily involved in the cyclic oxidation of high temperature materials for 30 years. Cyclic furnace and burner rig apparati have been developed, refined, and replicated to provide a large scale facility capable of evaluating many materials by a standard technique. Material behavior is characterized by weight change data obtained throughout the test, which has been modelled in a step-wise process of scale growth and spallation. This model and a coupled diffusion model have successfully described cyclic behavior for a number of systems and have provided insights regarding life prediction and variations in the spalling process. Performance ranking and mechanistic studies are discussed primarily for superalloys and coating alloys. Similar cyclic oxidation studies have been performed on steels, intermetallic compounds, thermal barrier coatings, ceramics, and ceramic composites. The most common oxidation test was performed in air at temperatures ranging from 800 deg. to 1600 C, for times up to 10000 h, and for cycle durations of 0.1 to 1000 h. Less controlled, but important, test parameters are the cooling temperature and humidity level. Heating and cooling rates are not likely to affect scale spallation. Broad experience has usually allowed for considerable focus and simplification of these test parameters, while still revealing the principal aspects of material behavior and performance. Extensive testing has been performed to statistically model the compositional effects of experimental alloys and to construct a comprehensive database of complex commercial alloys.

Smialek, J. L.↗

NASA NTRS2010

Spontaneous Raman Scattering Diagnostics: Applications in Practical Combustion Systems

In this chapter, the recent advancements and practical aspects of laser SRS diagnostics have been reviewed wi til regards to applications in practical combustion systems. Clearly, SRS represents a theoretically and experimentally mature diagnostic technology that has become an essential tool for multiscalar measurements in turbulent combustion at elevated pressures. Today, time-, space-, spectrally, and even polarization-resolved S RS diagnostics is at hand, with aid from recent innovations in theoretical and technological developments on electro-optical or electromechanical devices. Whilst a linear increase in SRS signals can be expected in high-pressure systems (this is perhaps one of the most important advantages for using SRS in high-pressure systems), there are practical (often severe) restrictions associated with pressurized vessels, due mainly to the limited degree of optical access. This narrows ti,e available choice of diagnostics that can be employed at any given time. Point-wise SRS diagnostics provides the highest accuracy on the chemical species and temperature measurements, and will continue to remain a vital approach for the study in such harsh environments. The practical design considerations and hands-on set-up guide for SRS diagnostics provided in this chapter are rarely presented elsewhere. Although the second-harmonic Nd:YAG pulsed laser (532 nm), combined with pulse-stretching optics or the recently introduced White Cell-based laser, seems to be the most favored excitation source of choice by the research community, UV excitation will undoubtedly continue to be used on many occasions, and especially in sooting flames. Detection methods may be divided into ICCD-based nanosecond-gate detection or a rotary-chopper electromechanical shutter-based CCD array detection, and the levels of background flame emission in individual cases would determine this critical design choice. Here, a process of Raman signal calibration based on ti,e crosstalk matrix formalism was explained step-by-step. As tI,is process may be very time-consuming and expensive, a well-planned experimental approach (01' building a transferable calibration database or library (at least with in a user's own facility over a series of different testing and runs) is vitally important. Hands on advice on the design and construction of flow control systems for high pressure burner facilities were also presented.

Kojima, Jun↗

NASA NTRS1995

NUCFRG2: An evaluation of the semiempirical nuclear fragmentation database

A semiempirical abrasion-ablation model has been successful in generating a large nuclear database for the study of high charge and energy (HZE) ion beams, radiation physics, and galactic cosmic ray shielding. The cross sections that are generated are compared with measured HZE fragmentation data from various experimental groups. A research program for improvement of the database generator is also discussed.

Wilson, J. W.↗

DOE OSTI2022

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. Furthermore, a neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.

Gaussian process↗

DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Advances in Metallic Fuel Database Development and Data Qualification

The Fuels Irradiation and Physics Database (FIPD [1]) is a comprehensive repository of data and documents related to Uranium-Zirconium based metallic fuel test pins. This database stores operational conditions of these pins, calculated using a suite of Argonne National Laboratory analysis codes developed during the Integral Fast Reactor (IFR) program. Key calculated data include axial distributions of power, temperature, fluence, burnup, and isotopic densities. Additionally, the FIPD holds post-irradiation examination (PIE) data such as fission gas release, gas chemistry measurements, and axial distributions derived from profilometry, gamma scanning, and neutron radiography. Complementing these data is an extensive archive of documents related to various pins and experiments. These include raw PIE records, design details, safety analyses, and operational reports. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The recent improvements to the database and the data QA status are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Reflections on one million compounds in the open quantum materials database (OQMD)

Abstract Density functional theory (DFT) has been widely applied in modern materials discovery and many materials databases, including the open quantum materials database (OQMD), contain large collections of calculated DFT properties of experimentally known crystal structures and hypothetical predicted compounds. Since the beginning of the OQMD in late 2010, over one million compounds have now been calculated and stored in the database, which is constantly used by worldwide researchers in advancing materials studies. The growth of the OQMD depends on project-based high-throughput DFT calculations, including structure-based projects, property-based projects, and most recently, machine-learning-based projects. Another major goal of the OQMD is to ensure the openness of its materials data to the public and the OQMD developers are constantly working with other materials databases to reach a universal querying protocol in support of the FAIR data principles.

08 HYDROGEN↗

DOE OSTI2026

Fuel-Cladding Eutectic Study of Legacy Fast Flux Test Facility (FFTF) MFF HT9/U-10Zr Metallic Fuel

This report presents the first systematic investigation of fuel-cladding eutectic interaction (FCEI) in irradiated HT9/U-10Zr metallic fuel from the Fast Flux Test Facility (FFTF) Materials Fuels Form (MFF) program, using differential scanning calorimetry (DSC) coupled with scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). Two irradiated fuel cross-sections, MNT07H (9.5 at% burnup, x/L = 0.78) and MNT08H (7.0 at% burnup, x/L = 0.93), were subjected to three successive isothermal annealing rounds (R1–R3) at 820°C for 20 minutes each, yielding a cumulative transient duration of one hour. This study directly addresses a recognized gap in the existing FCEI database, which previously lacked irradiated HT9/U-10Zr data at burnup levels above 8 at%. Two principal findings emerge from the study. First, for both samples, FCEI remained spatially confined within the pre-existing fuel-cladding chemical interaction (FCCI) zone boundaries after R3, with no measurable eutectic penetration into unaffected cladding beyond the original FCCI layer. This self-limiting behavior is consistent with historical Fuel Behavior Test Apparatus (FBTA) results and is attributed to the near-eutectic phase composition of the FCCI zone, which rapidly absorb the available eutectic-forming constituents and then stall penetration once the FCCI zone is consumed. A comparison with unirradiated surrogate data further supports this mechanism: whereas a U–34 at.% Fe sample would be expected to show ~176 µm of iron penetration under comparable conditions, the irradiated samples exhibited only ~20 µm, a discrepancy attributed to irradiation-induced interfacial porosity and pre-existing FCCI composition gradients. Second, for MNT08H, FCEI was observed exclusively on the half of the cladding circumference where pre-existing steady-state FCCI was present, with no detectable FCEI on the opposite half. Three hypotheses are proposed to explain this asymmetry: the inhibiting role of a zirconium-rich rind at the fuel-cladding interface; the chemical sequestration of iron by redistributed zirconium within the fuel matrix; and the persistence of fuel-cladding gaps on the FCEI-free half that preclude direct contact. All three hypotheses require further experimental investigation. The results extend the empirical FCEI database into higher-burnup territory and demonstrate the viability of DSC-based testing as a substitute for the no-longer-available FBTA apparatus. Future work will include additional cross-section testing, compilation of the full FCEI dataset, model evaluation, and DSC testing of ternary fuel compositions to broaden the experimental basis for safety assessment of sodium-cooled fast reactor systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗