Search NASA⌕ Search

SEARCH · Search NASA

Results for “computational catalysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Thermodynamic and Kinetic Roles of H2 in Structure Evolution of Urchin-like Co: A density functional theory study

Small gas molecules working as good capping agents play important roles in controlling the morphologies and surface structures of the metal nanocrystals. In present work, the thermodynamic and kinetic roles of H2 molecule played on the morphology of Co nanocrystal were systematically studied based on density functional theory (DFT). The Gibbs surface free energies (?) of Co(100), Co(110) and Co(111) under different hydrogen surface coverages were investigated by ab initio thermodynamics, based on which i) the phase diagrams of stable H coverage on each plane were obtained; ii) the morphology evolutions of the Co nanocrystals with various surface hydrogen coverages were further predicted by Wulff construction. It was revealed that addition of H2 could change the facet stability generating diverse morphological Co nucleus. The kinetic role of H2 was further explored by DFT during adatom Co surface diffusions under different H coverages (?H), which suggested that except for Co(100) at ?H of 0.56 ML, the surface H would hinder Co surface diffusions. The projected density of state (PDOS) gave a deeper insight that the electronic structures of the Co adatom could be alerted by addition of the surface H, which thereby affected its surface diffusion ability. X. Wang, N. Liu, Q. Zhang, X. Liang, and B. Chen were financially supported by the National Natural Science Foundation of China (No. 21476012, 21571012, and 91534201). D. Mei was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle. Computing time was granted by the facility user proposal grand challenge of computational catalysis of the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility located at Pacific Northwest National Laboratory (PNNL) and sponsored by DOE’s Office of Biological and Environmental Research.

Nanocrystal Co, Surface free energy, Surface diffu↗
DOE OSTI2025

A Rhenium Bis -tetramethylphenanthroline Catalyst for CO 2 Reduction to Formate

Catalytic CO 2 reduction reactions featuring high selectivity toward formate are relatively rare. In some homogeneous molecular CO 2 -reducing electrocatalysis, using triethylamine (TEA) and isopropanol (IPA) as additives improves catalytic performance in producing formate. In this paper, we investigate whether the rhenium(I) bis-diimine dicarbonyl complexes, cis-[Re(N^N) 2 (CO) 2 ] + , where N^N is 2,2’-bipyridine ([1] + ) or 3,4,7,8-tetramethyl-1,10-phenanthroline ([2] + ), are capable of electrocatalytically reducing CO 2 to formate in acetonitrile containing TEA and IPA. Catalyst [1] + was ineffective at CO 2 reduction, yielding formate quantities comparable to those produced in experiments without the catalyst. Catalyst [2] + , however, is a promising electrocatalyst for the CO 2 reduction reaction in the presence of TEA and IPA, with formate being produced in millimolar concentrations (10.5 mM), as detected by 1 H NMR spectroscopy after 6 h electrolysis (formate Faradaic efficiency = 11%, with the major balance going to H 2 ). Upon more detailed examination, [2] + exhibited a turnover frequency (TOF) of 12 s –1 for formate, comparable to other leading molecular catalysts that competently execute this reduction. Combinations of spectroscopy, electrochemistry, and theory were used to better understand the mechanism of CO 2 reduction by [2] + . Fourier transform infrared spectroelectrochemical (FTIR-SEC) data provided no evidence for CO ligand dissociation or substitution upon one- and two-electron reduction of [2] + , suggesting that a mechanism distinct from one that is metal-hydride-based is operative in catalysis. Computational studies guide mechanistic investigations toward the proposed formation of a hydrophenanthroline-based intermediate responsible for hydride transfer to CO 2 and electrocatalytic formate production from [2] + .

Beverages↗
DOE OSTI2022

Bibliometric review and recent advances in total scattering pair distribution function analysis: 21 years in retrospect

Global research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2020

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

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

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tuning Two-Dimensional Phthalocyanine Dual Site Metal–Organic Framework Catalysts for the Oxygen Reduction Reaction

Metal-organic frameworks (MOFs) offer an interesting opportunity for catalysis, particularly for metal-nitrogen-carbon (M-N-C) motifs by providing an organized porous structural pattern and well-defined active sites for the oxygen reduction reaction (ORR), a key need for hydrogen fuel cells and related sustainable energy technologies. Here, in this work, we leverage electrochemical testing with computational models to study the electronic and structural properties in these systems and their relationship to ORR activity and stability based on dual transitional metal centers. These consists of two M1 metals with amine nodes coordinated to a single M2 metal with a phthalocyanine linker, where M1/M2 = Co, Ni, or Cu. Co-based metal centers, in particular Ni-Co, demonstrate the highest overall activity of all nine tested MOFs. Computationally, we identify the dominance of Co-sites, relative higher importance of the M2 site, and the role of layer M1 interactions on the ORR activity. Selectivity measurements indicate that M1 sites of MOFs, particularly Co, exhibits lowest (< 4%), and Ni demonstrates highest (>46%) two-electron selectivity, in good agreement with computational studies. Direct in-situ stability characterization, measuring dissolved metal ions, and calculations, using an alkaline stability metric, confirm that Co is the most stable metal in the MOF, while Cu exhibits notable instability at the M1. Overall, this study reveals how atomistic coupling of electronic and structural properties affects the ORR performance of dual site MOF catalysts and opens new avenues for tunable design and future development of these systems for practical electrochemical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗
DOE OSTI2022

A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System

Developing strategies to study reactivity and selectivity in flexible catalyst systems has become an important topic of research. Herein, we report a combined experimental and computational study aimed at understanding the mechanistic role of an achiral DABCOnium cofactor in a regio- and enantiodivergent bromocyclization reaction. In this study, it was found that electron-deficient aryl substituents enable rigidified transition states via an anion–π interaction with the catalyst, which drives the selectivity of the reaction. In contrast, electron-rich aryl groups on the DABCOnium result in significantly more flexible transition states, where interactions between the catalyst and substrate are more important. An analysis of not only the lowest-energy transition state structures but also an ensemble of low-energy transition state conformers via energy decomposition analysis and machine learning was crucial to revealing the dominant noncovalent interactions responsible for observed changes in selectivity in this flexible system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗