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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anomalous lattice thermal conductivity increase with temperature in cubic GeTe correlated with strengthening of second-nearest neighbor bonds

Understanding thermal transport mechanisms in phase change materials is critical to elucidating the microscopic picture of phase transitions and advancing thermal energy conversion and storage. Experiments consistently show that cubic phase germanium telluride (GeTe) has an unexpected increase in lattice thermal conductivity with rising temperature. Despite its ubiquity, resolving its origin has remained elusive. In this work, we carry out temperature-dependent lattice thermal conductivity calculations for cubic GeTe through efficient, high-order machine-learned models and additional corrections for coherence effects. We corroborate the calculated phonon properties with our inelastic X-ray scattering measurements. Our calculated lattice thermal conductivity values agree well with experiments and show a similar increasing trend. Through additional bonding strength calculations, we propose that a major contributor to the increasing lattice thermal conductivity is the strengthening of second-nearest neighbor interactions. The findings herein serve to deepen our understanding of thermal transport in phase-change materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali-counterions can inform natural and synthetic aqueous processes. Here in the study of POMs ([Nb 24 O 72 H 9 ] 15- , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ-profile) and use an artificial neural network (ANN) to predict all three described alkali-anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14- , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae . Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators ( STE12-TEC1 and RAP1-HAP1-MSN4 ). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Using Machine Learning to Analyze Factors Determining Cycle-to-Cycle Variation in a Spark-Ignited Gasoline Engine

In this work, we have applied a machine learning (ML) technique to provide insights into the causes of cycle-to-cycle variation (CCV) in a gasoline spark-ignited (SI) engine. The analysis was performed on a set of large eddy simulation (LES) calculations of a single cylinder of a four-cylinder port-fueled SI engine. The operating condition was stoichiometric, without significant knock, at a load of 16 bar brake mean effective pressure (BMEP), at an engine speed of 2500 revolutions per minute. A total of 123 cycles was simulated. Of these, 49 were run in sequence, while 74 were run in parallel. For the parallel approach, each cycle is initialized with its own synthetic turbulent field to generate CCV, as part of another work performed by us. In the current work, we used 3D information from all 123 cycles to compute flame topology and pre-ignition flow-field metrics. We then evaluated correlations between these metrics, and peak cylinder pressure (PCP) employing an ML technique called random forest. The computed metrics form the inputs to the random forest model, and PCP is the output. This model captures the effect of all inputs, as well as interactions between them owing to its decision-tree structure. The goal of this work is to demonstrate (as a first step) that ML models can implicitly learn complex relationships between pre-ignition flow-fields, flame shapes, and the eventual outcome of the cycle (whether a cycle will be a high or a low cycle).

42 ENGINEERING↗
DOE OSTI2021

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Untangling Sources of Error in the Density-Functional Many-Body Expansion

The many-body expansion provides a framework for data-driven applications of electronic structure theory, including parametrization of classical force fields and machine learning. In this article, we demonstrate that its use significantly amplifies quadrature grid errors when modern density-functional approximations are employed. Standard grids that work well in conventional density-functional calculations result in runaway error accumulation when used with the many-body expansion. At the same time, delocalization error is also exacerbated, leading to exaggerated estimates of nonadditive n-body interactions. This is illustrated for anion–water clusters using the SCAN, r2SCAN, ωB97X-V and ωB97M-V functionals. By employing dense quadrature grids, the inherent self-interaction error is exposed, which can then be mitigated using a variety of other strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff–Brenner type bond order potential (BOP) for a Co–C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co2C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure–property relationships, and temperature/pressure response of Co–C systems, as well as design organic–inorganic hybrid structures with a desired set of properties.

Narayanan, Badri↗
DOE OSTI2025

Incubating advances in integrated photonics with emerging sensing and computational capabilities

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Jain, Sourabh (ORCID:0000000279923275)↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2024

Understanding the Interactions of Multiple Pits Under Freely Corroding Conditions

The interactions of two propagating pits on a single cathode surface were evaluated across variations in chloride concentration, water layer (WL), pit sizes, separation distance (x 2 ), and cathode size (L Cath ) under freely corroding conditions using Finite Element Methods (FEM). Calculated FEM current was utilized to predict stability based on the Galvele pit stability product. FEM predictions were utilized to train a neural network machine learning model for rapid stability predictions. Pit one is in the center of a circular cathode while pit two moves radially from the center pit. With two pits, the overall current in each pit is decreased with respect to a single pit, however, the total current is increased. Increasing WL and L Cath generally increased overall current in each pit and increased predicted maximum pit sizes. Increasing x 2 decreased current in pit two due to less cathode being available to support dissolution in proximity to pit two. Increasing chloride concentration from 0.6 to 3 M NaCl increased current, while increasing from 3 to 5.3 M NaCl decreased current. An overall increase in predicted pit size with increase in chloride concentration is predicted. A machine learning model was created to predict current and maximum pit size and captured underlying physics and predicted stability across the multidimensional parameter space.

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

Enabling Predictive Scale-Bridging Simulations through Active Learning (Institutional Computing Annual Report (Project w20_alscalebridging)) [Slides]

The goal of this project was to develop, demonstrate, and provide a new capability to achieve greater physical fidelity in large-scale simulations, rather than the usual brute-force increases in the number of mesh elements or particles. This was done by using machine learning (ML) techniques to develop emulators for subscale physics that can be used in coarse-scale continuum simulations, trained on fine-scale molecular dynamics (MD) simulations that are launched on-the-fly via active learning. raditional inertial confinement fusion (ICF) simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions; our approach directly accounts for this physics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Multiscale ACI Satellite Database

The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.

Christensen, Matthew [Pacific Northwest National L↗
DOE OSTI2020

Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems

The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.

Franklin, D. Michael↗
DOE OSTI2023

Exploring the Complex Chemistry of Uranium within Molten Chloride Salts

Harsh environments represent a unique opportunity to explore new frontiers in chemistry while developing novel tools to meet global needs. Exploring the chemistry of uranium within molten salts is a key example. Actinide chemistry within the highly ionic environment of a molten salt is poorly understood, particularly in the presence of common salt impurities or without active oxidation state control. Delving into this chemistry can provide new insight into actinide and f-electron interactions. Furthermore, expanding our chemical knowledge can also enable advances in and deployment of molten salt reactors or molten salt recycle schemes. Both molten salt applications aim towards providing green, reliable, and equitable energy as well as critical materials for the world. Here the utilization of visible absorbance and Raman spectroscopies to understand and quantify U within chloride-based salt eutectics is discussed. Furthermore, machine learning techniques in the form of chemometric modelling are developed and described, providing advanced analytical tools to quantify and characterize the U present. In conclusion, these tools are then leveraged to monitor and explore the dynamic fundamental chemistry of U within chloride-based salt melts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. Finally, this work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches

Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Analysis of phase stability and chemical segregation in the Mo-V alloys using a generalized embedded atom method potential

A new interatomic potential for the Mo-V system is introduced to facilitate the study of phase stability and mechanical properties at lower temperatures. This potential is based on a generalization of the embedded atom method and includes contributions from embedding energy, explicit two- and three-body interactions and nonlocal many-body interaction terms. The parameters of the potential are optimized by using data from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, such as elastic constants, equation of states, phonon dispersion curves, point defect properties and melting temperatures for different compositions. Even though our potential is trained on a small dataset, its accuracy is comparable to available machine learning potentials for Mo and V. Furthermore, our results show that an ordered B2 phase is stable at low temperatures in alloys containing 50% V, but the solid solution phase is stable above 800 K. However, such long-range ordering is not observed in V-rich or Mo-rich alloys. In addition, our results show that V segregates to dislocation cores and grain boundaries.

36 MATERIALS SCIENCE↗