Kirsten T Winther

All Publications

• Descriptor of NonlocalEffects in Oxidative Adsorptionon Metal Oxides JOURNAL OF PHYSICAL CHEMISTRY C Bothra, N., Cho, A., Comer, B. M., Winther, K. T., Bajdich, M. 2026

  View details for DOI 10.1021/acs.jpcc.6c03326

  View details for Web of Science ID 001853658900001

• Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis ACS CATALYSIS Shin, D., Mahajan, R., Lian, Z., Heinlein, J., Cargnello, M., Tassone, C. J., Winther, K. T. 2026

  View details for DOI 10.1021/acscatal.6c03456

  View details for Web of Science ID 001845329400001

• Quantifying uncertainty in catalyst activity and deactivation during CO hydrogenation via round-robin testing for data-driven modelling NATURE CATALYSIS Bac, S., Shin, D., Hong, S., Heinlein, J., Khan, A., Barber, G., Chen, Z., Albrechtsen, M. M., Tassone, C., Rioux, R. M., Cargnello, M., Bare, S. R., Winther, K., Christopher, P., Hoffman, A. S. 2026

  View details for DOI 10.1038/s41929-026-01559-y

  View details for Web of Science ID 001836875400001

• Bridging artificial intelligence with photovoltaics CELL REPORTS PHYSICAL SCIENCE Lee, S., Lee, S., Lee, S., Hwang, J., Oh, W., Winther, K., Lee, D., Kim, D., Nielander, A. C., Jaramillo, T. F., Kang, Y., Lee, H. 2025; 6 (11)

  View details for DOI 10.1016/j.xcrp.2025.102893

  View details for Web of Science ID 001630477800013

• A research database for experimental electrocatalysis: Advancing data sharing and reusability. The Journal of chemical physics Mahajan, R., Aleman, A. M., Crago, C. F., Bhasker-Ranganath, S., Kreider, M. E., Zamora Zeledon, J. A., Schröder, J., Kamat, G. A., Hubert, M. A., Nielander, A. C., Jaramillo, T. F., Stevens, M. B., Voss, J., Winther, K. T. 2025; 163 (12)

  Abstract

  The availability of high-fidelity catalysis data is essential for training machine learning models to advance catalyst discovery. Furthermore, the sharing of data is crucial to ensure the comparability of scientific results. In electrocatalysis, where complex experimental conditions and measurement uncertainties pose unique challenges, structured data collection and sharing are critical to improving reproducibility and enabling robust model development. Addressing these challenges requires standardized approaches to data collection, metadata inclusion, and accessibility. To support this effort, we have developed an extensive data infrastructure that curates and organizes multimodal data from electrocatalysis experiments, making them openly available through the catalysis-hub.org platform. Our datasets, comprising 241 experimental entries, provide detailed information on reaction conditions, material properties, and performance metrics, ensuring transparency and interoperability. By structuring electrocatalysis data in web-based as well as machine-readable formats, we aim to bridge the gap between experimental and computational research, allowing for improved benchmarking and predictive modeling. This work highlights the importance of well-structured, accessible data in overcoming reproducibility challenges and advancing machine learning applications in catalysis. The framework we present lays the foundation for future data-driven research in electrocatalysis and offers a scalable model for other experimental disciplines.

  View details for DOI 10.1063/5.0280821

  View details for PubMedID 41025581

• 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

• Autonomous intelligent agents for accelerated materials discovery CHEMICAL SCIENCE Montoya, J. H., Winther, K. T., Flores, R. A., Bligaard, T., Hummelshoj, J. S., Aykol, M. 2020; 11 (32): 8517-8532

  Abstract

  We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

  View details for DOI 10.1039/d0sc01101k

  View details for Web of Science ID 000561022500016

  View details for PubMedID 34123112

  View details for PubMedCentralID PMC8163357

• 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

• Local Plasmon Engineering in Doped Graphene ACS NANO Hage, F., Hardcastle, T. P., Gjerding, M. N., Kepaptsoglou, D. M., Seabourne, C. R., Winther, K. T., Zan, R., Amani, J., Hofsaess, H. C., Bangert, U., Thygesen, K. S., Ramasse, Q. M. 2018; 12 (2): 1837-1848

  Abstract

  Single-atom B or N substitutional doping in single-layer suspended graphene, realized by low-energy ion implantation, is shown to induce a dampening or enhancement of the characteristic interband π plasmon of graphene through a high-resolution electron energy loss spectroscopy study using scanning transmission electron microscopy. A relative 16% decrease or 20% increase in the π plasmon quality factor is attributed to the presence of a single substitutional B or N atom dopant, respectively. This modification is in both cases shown to be relatively localized, with data suggesting the plasmonic response tailoring can no longer be detected within experimental uncertainties beyond a distance of approximately 1 nm from the dopant. Ab initio calculations confirm the trends observed experimentally. Our results directly confirm the possibility of tailoring the plasmonic properties of graphene in the ultraviolet waveband at the atomic scale, a crucial step in the quest for utilizing graphene's properties toward the development of plasmonic and optoelectronic devices operating at ultraviolet frequencies.

  View details for DOI 10.1021/acsnano.7b08650

  View details for Web of Science ID 000426615600096

  View details for PubMedID 29369611

• Band structure engineering in van der Waals heterostructures via dielectric screening: the GΔW method 2D MATERIALS Winther, K. T., Thygesen, K. S. 2017; 4 (2)

  View details for DOI 10.1088/2053-1583/aa6531

  View details for Web of Science ID 000398424400006

• Interlayer Excitons and Band Alignment in MoS₂/hBN/WSe₂ van der Waals Heterostructures NANO LETTERS Latini, S., Winther, K. T., Olsen, T., Thygesen, K. S. 2017; 17 (2): 938-945

  Abstract

  van der Waals heterostructures (vdWH) are ideal systems for exploring light-matter interactions at the atomic scale. In particular, structures with a type-II band alignment can yield detailed insight into carrier-photon conversion processes, which are central to, for example, solar cells and light-emitting diodes. An important first step in describing such processes is to obtain the energies of the interlayer exciton states existing at the interface. Here we present a general first-principles method to compute the electronic quasi-particle (QP) band structure and excitonic binding energies of incommensurate vdWHs. The method combines our quantum electrostatic heterostructure (QEH) model for obtaining the dielectric function with the many-body GW approximation and a generalized 2D Mott-Wannier exciton model. We calculate the level alignment together with intra- and interlayer exciton binding energies of bilayer MoS2/WSe2 with and without intercalated hBN layers, finding excellent agreement with experimental photoluminescence spectra. A comparison to density functional theory calculations demonstrates the crucial role of self-energy and electron-hole interaction effects.

  View details for DOI 10.1021/acs.nanolett.6b04275

  View details for Web of Science ID 000393848800048

  View details for PubMedID 28026961

• Efficient many-body calculations for two-dimensional materials using exact limits for the screened potential: Band gaps of MoS₂, <i>h</i>-BN, and phosphorene PHYSICAL REVIEW B Rasmussen, F. A., Schmidt, P. S., Winther, K. T., Thygesen, K. S. 2016; 94 (15)

  View details for DOI 10.1103/PhysRevB.94.155406

  View details for Web of Science ID 000385241800006