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Bligaard, Thomas

Publications and source records attributed to Bligaard, Thomas.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

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

MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation

The predictive power of density functional theory for materials properties can be improved without increasing the overall computational complexity by extending the generalized gradient approximation (GGA) for electronic exchange and correlation to density functionals depending on the electronic kinetic energy density in addition to the charge density and its gradient, resulting in a meta-GGA. Here, we propose an empirical meta-GGA model that is based both on physical constraints and on experimental and quantum chemistry reference data. Overall, the resulting optimized meta-GGA MCML yields improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions of existing meta-GGA approaches.

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

Machine learning with bond information for local structure optimizations in surface science

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. Furthermore, we introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

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 OSTI2020

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO 2 and IrO 3 . The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO 2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO 3 polymorphs and report a previously unknown FeF 3 -type structure as the most stable, termed α-IrO 3 . To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO 2 and IrO 3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H 2 O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO 2 . Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO 3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

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

DFT simulation of the X-ray diffraction pattern of aluminum-ion-intercalated graphite used as the cathode material of the aluminum-ion battery

Many breakthroughs have been achieved in rechargeable aluminum-ion battery technologies in recent years. Most recently, operando X-ray diffraction (XRD) combined with density functional theory (DFT) calculations was reported to study the chloroaluminate anion (AlCl 4 -)-intercalated graphite cathode of the battery. However, there are quite a few discrepancies between the measured and simulated XRD patterns. This work is focused on the simulation of XRD patterns of graphite intercalation compounds (GICs) with DFT calculations. Our results reveal that both the geometry of AlCl 4 - in graphite and the gallery height of GICs are dependent on the intercalant density. At low intercalant density, the gallery height keeps constant, but at high intercalant densities, the gallery height is linearly related to the intercalant density. Our simulated XRD patterns are highly consistent with the measured operando XRD patterns. Not only do the angles of the peaks match very well, but also the relative intensities and the corresponding electrode capacities show reasonable agreement with the experimental results. The DFT simulation of the XRD pattern provides significant information on the stage index and the charge capacity of the GIC electrode.

25 ENERGY STORAGE↗
DOE OSTI2019

Adsorption on transition metal surfaces: Transferability and accuracy of DFT using the ADS41 dataset

We present a benchmarking study of adsorption energies on transition metal surfaces computed with select functionals across different density functional theory codes. In addition to gradient corrected functionals, we evaluate the accuracies of representative metaGGAs, including MS2, SCAN, and SCAN+rVV10 , as well as a short-range screened hybrid functional, HSE06. The study shows that the challenge of finding a functional that can simultaneously capture both covalent and noncovalent molecule-surface interactions persists, with no single functional in the benchmarking study with average errors < 0.2 eV. We find that HSE06 on average does not improve the accuracy compared to PBE for the surface chemistry of transition metals. The BEEF-vdW dispersion-corrected generalized gradient approximation (GGA) and the MS2 metaGGA yield the lowest errors in both chemisorption and dispersion energies, demonstrating that moving up the Jacob's ladder of functionals to screened hybrids does not necessarily improve the description of transition metal surface chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine Learning for Computational Heterogeneous Catalysis

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

Schlexer Lamoureux, Philomena↗
DOE OSTI2019

High-throughput calculations of catalytic properties of bimetallic alloy surfaces

Abstract A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CH x , OH, NH, and S H - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform www.Catalysis-hub.org for easy retrieval of the data for further scientific analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Catalysis-Hub.org, an open electronic structure database for surface reactions

Abstract We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model

We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. In conclusion, our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Genetic algorithms for computational materials discovery accelerated by machine learning

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation

We present a methodology for graph based enumeration of surfaces and unique chemical adsorption structures bonded to those surfaces. Utilizing the graph produced from a bulk structure, we create a unique graph representation for any general slab cleave and further extend that representation to include a large variety of catalytically relevant adsorbed molecules. Here, we also demonstrate simple geometric procedures to generate 3D initial guesses of these enumerated structures. While generally useful for generating a wide variety of structures used in computational surface science and heterogeneous catalysis, these techniques are also key to facilitating an informatics approach to the high-throughput search for more effective catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Atomic-position independent descriptor for machine learning of material properties

The high-throughput screening of periodic inorganic solids using machine learning methods requires atomic positions to encode structural and compositional details into appropriate material descriptors. These atomic positions are not available a priori for new materials, which severely limits exploration of novel materials. In this work, we overcome this limitation by using only crystallographic symmetry information in the structural description of materials. We show that for materials with identical structural symmetry, machine learning is trivial, and accuracies similar to that of density functional theory calculations can be achieved by using only atomic numbers in the material description. For machine learning of formation energies of bulk crystalline solids, this simple material descriptor is able to achieve prediction mean absolute errors of only 0.07 eV/at on a test dataset consisting of more than 85 000 diverse materials. Finally, this atomic-position independent material descriptor presents a new route of materials discovery wherein millions of materials can be screened by training a machine learning model over a drastically reduced subspace of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A first-principle study of H adsorption and absorption under the influence of coverage

The investigations of interactions between hydrogen and transition metal surfaces are of vital importance to the comprehension of principals involved in many chemical processes. In this work, the influences of coverage on H adsorption and absorption on and in a series of transition metals have been studied. High enough hydrogen coverage is needed to make H absorption much more feasibly. The pre-adsorbed H adatoms can modify H binding energies as well as H absorption energetics and can be felt very deeply in the 2nd-subsurface. In conclusion, surface penetration of atomic H and further diffusion into the bulk can be facilitated, and in turn the resurfacing process of absorbed H is blocked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Understanding zeolite-catalyzed benzene methylation reactions by methanol and dimethyl ether at operating conditions from first principle microkinetic modeling and experiments

In methanol-to-hydrocarbon chemistry, methanol and dimethyl ether (DME) can act as methylating agents. Therefore, in this paper we focus on the different reactivity of methanol and DME towards benzene methylation in H-ZSM-5 at operating conditions by combining first principles microkinetic modeling and experiments. Methylation reactions are known to follow either a concerted reaction path or a stepwise mechanism going through a framework-bound methoxide. By constructing a DFT based microkinetic model including the concerted and stepwise reactions, product formation rates can be calculated at conditions that closely mimic the experimentally applied conditions. Trends in measured rates are relatively well reproduced by our DFT based microkinetic model. We find that benzene methylation with DME is faster than with methanol but the difference decreases with increasing temperature. At low temperatures, the concerted mechanism dominates, however at higher temperatures and low pressures the mechanism shifts to the stepwise pathway. This transition occurs at lower temperatures for methanol than for DME, resulting in smaller reactivity differences between methanol and DME at high temperature. Finally, our theory-experiment approach shows that the widely assumed rate law with zeroth and first order in oxygenate and hydrocarbon partial pressure is not generally applicable and depends on the applied temperature, pressure and feed composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗