Kirsten T Winther

All Publications

• Understanding the Electronic and Structural Effects in ORR Intermediate Binding on Anion-Substituted Zirconia Surfaces CHEMPHYSCHEM Sinha, S., Vegge, T., Winther, K. T., Hansen, H. 2024: e202300865

  Abstract

  For oxygen reduction reaction (ORR), the surface adsorption energies of O* and OH* intermediates are key descriptors for catalytic activity. In this work, we investigate anion-substituted zirconia catalyst surfaces and determine that adsorption energies of O* and OH* intermediates is governed by both structural and electronic effects. When the adsorption energies are not influenced by the structural effects of the catalyst surface, they exhibit a linear correlation with integrated crystal orbital Hamiltonian population (ICOHP) of the adsorbate-surface bond. The influence of structural effects, due to re-optimisation slab geometry after adsorption of intermediate species, leads to stronger adsorption of intermediates. Our calculations show that there is a change in the bond order to accommodate the incoming adsorbate species which leads to stronger adsorption when both structural and electronic effects influence the adsorption phenomena. The insights into the catalyst-adsorbate interactions can guide the design of future ORR catalysts.

  View details for DOI 10.1002/cphc.202300865

  View details for Web of Science ID 001250670400001

  View details for PubMedID 38391116

• Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions CATALYSIS SCIENCE & TECHNOLOGY Martinez-Alonso, C., Vassilev-Galindo, V., Comer, B. M., Abild-Pedersen, F., Winther, K. T., Llorca, J. 2024

  View details for DOI 10.1039/d4cy00491d

  View details for Web of Science ID 001243190900001

• Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance. Chemphyschem : a European journal of chemical physics and physical chemistry Deo, S., Kreider, M., Kamat, G., Hubert, M., Zamora Zeledón, J., Wei, L., Matthews, J., Keyes, N., Singh, I., Jaramillo, T., Abild-Pedersen, F., Burke Stevens, M., Winther, K., Voss, J. 2024: e202400010

  Abstract

  Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

  View details for DOI 10.1002/cphc.202400010

  View details for PubMedID 38547332

• Prediction of O and OH Adsorption on Transition Metal Oxide Surfaces from Bulk Descriptors ACS CATALYSIS Comer, B. M., Bothra, N., Lunger, J. R., Abild-Pedersen, F., Bajdich, M., Winther, K. T. 2024

  View details for DOI 10.1021/acscatal.4c00111

  View details for Web of Science ID 001191165700001

• GPAW: An open Python package for electronic structure calculations JOURNAL OF CHEMICAL PHYSICS Mortensen, J., Larsen, A., Kuisma, M., Ivanov, A. V., Taghizadeh, A., Peterson, A., Haldar, A., Dohn, A., Schafer, C., Jonsson, E., Hermes, E. D., Nilsson, F., Kastlunger, G., Levi, G., Jonsson, H., Hakkinen, H., Fojt, J., Kangsabanik, J., Sodequist, J., Lehtomaki, J., Heske, J., Enkovaara, J., Winther, K., Dulak, M., Melander, M. M., Ovesen, M., Louhivuori, M., Walter, M., Gjerding, M., Lopez-Acevedo, O., Erhart, P., Warmbier, R., Wuerdemann, R., Kaappa, S., Latini, S., Boland, T., Bligaard, T., Skovhus, T., Susi, T., Maxson, T., Rossi, T., Chen, X., Schmerwitz, Y. A., Schiotz, J., Olsen, T., Jacobsen, K., Thygesen, K. 2024; 160 (9)

  Abstract

  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.

  View details for DOI 10.1063/5.0182685

  View details for Web of Science ID 001182307500002

  View details for PubMedID 38450733

• Synergistic effects of mixing and strain in high entropy spinel oxides for oxygen evolution reaction. Nature communications Baek, J., Hossain, M. D., Mukherjee, P., Lee, J., Winther, K. T., Leem, J., Jiang, Y., Chueh, W. C., Bajdich, M., Zheng, X. 2023; 14 (1): 5936

  Abstract

  Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

  View details for DOI 10.1038/s41467-023-41359-7

  View details for PubMedID 37741823

  View details for PubMedCentralID PMC10517924

• Efficient and Stable Acidic Water Oxidation Enabled by Low-Concentration, High-Valence Iridium Sites ACS ENERGY LETTERS Shi, X., Peng, H., Hersbach, T. J. P., Jiang, Y., Zeng, Y., Baek, J., Winther, K. T., Sokaras, D., Zheng, X., Bajdich, M. 2022

  View details for DOI 10.1021/acsenergylett.2c00578

  View details for Web of Science ID 000821179900001

• Unraveling Electronic Trends in O* and OH* Surface Adsorption in the MO2 Transition-Metal Oxide Series JOURNAL OF PHYSICAL CHEMISTRY C Comer, B. M., Li, J., Abild-Pedersen, F., Bajdich, M., Winther, K. T. 2022; 126 (18): 7903-7909

  View details for DOI 10.1021/acs.jpcc.2c02381

  View details for Web of Science ID 000800028300011

• Theory-Aided Discovery of Metallic Catalysts for Selective Propane Dehydrogenation to Propylene ACS CATALYSIS Wang, T., Cui, X., Winther, K. T., Abild-Pedersen, F., Bligaard, T., Norskov, J. K. 2021; 11 (10): 6290-6297

  View details for DOI 10.1021/acscatal.0c05711

  View details for Web of Science ID 000656056200039

• A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts NPJ COMPUTATIONAL MATERIALS Mamun, O., Winther, K. T., Boes, J. R., Bligaard, T. 2020; 6 (1)

  View details for DOI 10.1038/s41524-020-00447-8

  View details for Web of Science ID 000591956800001

• Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction CHEMISTRY OF MATERIALS Flores, R. A., Paolucci, C., Winther, K. T., Jain, A., Torres, J., Aykol, M., Montoya, J., Norskov, J. K., Bajdich, M., Bligaard, T. 2020; 32 (13): 5854–63

  View details for DOI 10.1021/acs.chemmater.0c01894

  View details for Web of Science ID 000551412800045

• Machine Learning for Computational Heterogeneous Catalysis CHEMCATCHEM Lamoureux, P., Winther, K. T., Torres, J., Streibel, V., Zhao, M., Bajdich, M., Abild-Pedersen, F., Bligaard, T. 2019; 11 (16): 3579–99

  View details for DOI 10.1002/cctc.201900595

  View details for Web of Science ID 000498036500004

• Catalysis-Hub.org, an open electronic structure database for surface reactions. Scientific data Winther, K. T., Hoffmann, M. J., Boes, J. R., Mamun, O., Bajdich, M., Bligaard, T. 2019; 6 (1): 75

  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 andcalculational 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.

  View details for DOI 10.1038/s41597-019-0081-y

  View details for PubMedID 31138816

• High-throughput calculations of catalytic properties of bimetallic alloy surfaces. Scientific data Mamun, O., Winther, K. T., Boes, J. R., Bligaard, T. 2019; 6 (1): 76

  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, CHx, OH, NH, and SH - 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.

  View details for DOI 10.1038/s41597-019-0080-z

  View details for PubMedID 31138814

• Catalysis-hub.org: An open electronic structure database for surface reactions and catalytic materials Winther, K., Hoffmann, M., Mamun, O., Boes, J., Bajdich, M., Bligaard, T. AMER CHEMICAL SOC. 2019

  View details for Web of Science ID 000478860501774

• Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation JOURNAL OF PHYSICAL CHEMISTRY A Boes, J. R., Mamun, O., Winther, K., Bligaard, T. 2019; 123 (11): 2281–85

  View details for DOI 10.1021/acs.jpca.9b00311

  View details for Web of Science ID 000462260800012

• Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation. The journal of physical chemistry. A Boes, J. R., Mamun, O., Winther, K., Bligaard, T. 2019

  Abstract

  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. 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.

  View details for PubMedID 30802053