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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

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

ScaleML: Machine Learning based Heap Memory Object Scaling Prediction

Memory subsystem contributes 28-40% of total energy consumption. Several studies investigated energy prediction and consumption via profiling memory object access patterns. However, such profiling leads to higher energy consumption due to intense memory object-level profiling to achieve high prediction accuracy. Further, memory object access pattern prediction has been considered through analyzing the variation between memory object access patterns, referred to as scaling rate. The existing techniques for scaling rate prediction, such as Linear Scaling Rate (LSR), suffer from a high error rate in prediction with changes in access patterns, which leads to a high error rate of energy consumption prediction. In this paper, we compare and evaluate several memory object access pattern prediction models including LSR and machine learning (ML) models. Further, we propose SCALEML, a heap memory object scaling rate prediction mechanism that employs an ML model to achieve high access pattern prediction accuracy with variations in memory object access patterns. We evaluate SCALEML using various application benchmarks. The experimental results show that SCALEML achieves about 20% higher accuracy than the LSR model for predicting the scaling rate of object access patterns and energy estimation.

Park, Joongeon↗
DOE OSTI2020

Predicting Excitation Energies of Twisted Intramolecular Charge-Transfer States with the Time-Dependent Density Functional Theory: Comparison with Experimental Measurements in the Gas Phase and Solvents Ranging from Hexanes to Acetonitrile

Electronically excited states characterized by intramolecular charge transfer play an essential role in many biological processes and optical devices. The ability to make quantitative ab initio predictions of the relative energetics involved is a challenging yet desirable goal, especially for large molecules in solution. In this work, we present a data set of 61 experimental measurements of absorption and emission processes, both in the gas phase and in solvents representing a broad range of polarities, which involve intramolecular charge transfer mediated by a nonzero, "twisted" dihedral angle between one or more donor and acceptor subunits. Among a variety of density functionals investigated within the framework of linear-response theory, the "optimally tuned" LRC-ωPBE functional, which utilizes a system-specific yet nonempirical procedure to specify the range-separation parameter, emerges as the preferred choice. For the entire set of excitation energies, involving changes in dipole moment ranging from 4 to >20 Debye, the mean signed and absolute errors are 0.02 and 0.18 eV, respectively (compared, e.g., to -0.30 and 0.30 for PBE0, 0.44 and 0.47 for LRC-ωPBEh, 0.83 and 0.83 for ωB97X-V). We analyze the performance of polarizable continuum solvation models available in Q-Chem that partition the solvent response into fast and slow time scales, and clear trends emerge when measurements corresponding to the four small 4-(dimethylamino)benzonitrile (DMABN)-like molecules and a charged species are excluded. We make the case that the large errors found only for small molecules in the gas phase and weak solvents cannot be expected to improve via the optimal tuning procedure, which enforces a condition that is exact only in the well-separated donor-acceptor limit, and present empirical evidence implicating the outsized importance for small donor-acceptor systems of relaxation effects that cannot be accounted for by the linear-response time-dependent density functional theory within the adiabatic approximation. Finally, we demonstrate the utility of the optimally tuned density functional approach by targeting the charge-transfer states of a large biomimetic model system for light-harvesting structures in Photosystem II.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Measurement and prediction of Energy Efficient Engine noise

The NASA/GE Energy Efficient Engine (E3) static noise levels were measured in an acoustic arena on the Integrated Core and Low Spool Test System. These measured levels were scaled to the appropriate size to power four study aircraft and were projected to flight for evaluation of noise levels relative to FAR36, Stage III limits. As a result of these evaluations, it is predicted that the NASA/GE E3 engine with a wide spacing cut-on blade/vane ratio fan and a forced mixer nozzle can meet FAR36 Stage III limits with sufficient design margin.

Lavin, S. P.↗
NASA NTRS2012

Method for Predicting the Energy Characteristics of Li-Ion Cells Designed for High Specific Energy

Novel electrode materials with increased specific capacity and voltage performance are critical to the NASA goals for developing Li-ion batteries with increased specific energy and energy density. Although performance metrics of the individual electrodes are critically important, a fundamental understanding of the interactions of electrodes in a full cell is essential to achieving the desired performance, and for establishing meaningful goals for electrode performance in the first place. This paper presents design considerations for matching positive and negative electrodes in a viable design. Methods for predicting cell-level performance, based on laboratory data for individual electrodes, are presented and discussed.

Bennett, William, R.↗
DOE OSTI2023

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

25 ENERGY STORAGE↗
DOE OSTI2020

Predicting Dexter Energy Transfer Interactions from Molecular Orbital Overlaps

Dexter energy transfer (DET), also known as spin forbidden energy transfer (e.g., 3 D 1 A → 1 D 3 A), is undergoing renewed interest because of its utility in energy science, photocatalysis, and biomedical imaging. The DET reactions enable exciton fission and fusion and have been recently used to facilitate nanoscale imaging of protein assemblies on cell surfaces. While DET and Förster energy transfer rates are often well described using Fermi’s golden rule, the donor–acceptor couplings for DET may be bridge-state-mediated and depend on the electronic structure of and interactions among the donor, bridge, and acceptor. We provide a simple approach to approximate DET couplings using a product of molecular orbital overlap factors. This approach may be used to guide the design of DET systems with targeted energy-transfer kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗