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Publications

The authors are linked to their Scopus page, the title linked to the Scopus abstract, the journal linked to the Scopus journal page, and the DOI is linked to https://doi.org which normally redirects you to the journal page. This page is automatically generated, so it may be incomplete, and the data reflects what is in the Scopus database.

  1. Abdelmaqsoud K., Sinclair D., Karra V.S.S.A., Taheri-Mousavi S.M., Widom M., Webler B.A., Kitchin J.R., Computational design of ductile additively manufactured tungsten-based refractory alloys', Computational Materials Science', 270, 114735, (2026-06-05), doi:10.1016/j.commatsci.2026.114735, .
  2. Amaro R.E., Batista V., Blumberger J., Choong Y.S., Corminboeuf C., Cournia Z., Cui Q., De Vivo M., Evangelista F.A., Gao Y.Q., Ghosh D., He X., Isayev O., Khalid S., Kirchmair J., Kitchin J.R., Liu H., Naidoo K.J., Nguyen D., Nunes Alves A., Palermo G., Savoie B., Soares T.A., Tiwary P., Wei G., Zheng X., Zhu T., Merz K.M., Gagliardi L., Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry', Journal of Chemical Theory and Computation', 22(9), p. 4199-4200, (2026-05-12), doi:10.1021/acs.jctc.6c00733, .
  3. Abdelmaqsoud K., Kitchin J.R., Widom M., Electronic structure and elasticity of the Ta-W solid solution', Physical Review Materials', 10(5), 053605, (2026-05-01), doi:10.1103/ph3y-8lnq, .
  4. Medford A.J., Whittaker T.N., Kreitz B., Flaherty D.W., Kitchin J.R., Prospects for using artificial intelligence to understand intrinsic kinetics of heterogeneous catalytic reactions', Current Opinion in Chemical Engineering', 51, 101232, (2026-03-01), doi:10.1016/j.coche.2026.101232, .
  5. Xin H., Kitchin J.R., Lopez N., Schweitzer N.M., Artrith N., Che F., Grabow L.C., Gunasooriya G.T.K.K., Kulik H.J., Laino T., Li H., Linic S., Medford A.J., Meyer R.J., Peng J., Phillips C., Qian J., Qi L., Shaw W.J., Ulissi Z.W., Wang S., Wang X., Roadmap for transforming heterogeneous catalysis with artificial intelligence', Nature Catalysis', 9(2), p. 102-111, (2026-02-01), doi:10.1038/s41929-026-01479-x, .
  6. Lueg L.R., Alves V., Schicksnus D., Kitchin J.R., Laird C.D., Biegler L.T., A simultaneous approach for training neural differential-algebraic systems of equations', Computational Optimization and Applications', None, no pages found, (2026-01-01), doi:10.1007/s10589-026-00823-y, .
  7. Xin H., Kitchin J.R., Lopez N., Schweitzer N.M., Abolhasani M., Artrith N., Arnadottir L., Choudhary K., Ding R., Frenkel A.I., Gauthier J.A., Goldsmith B.R., Farimani A.B., Grabow L.C., Kalhara Gunasooriya G.T.K., Hu G., Josephson T.R., Kulik H.J., Kumar R., Laino T., Li H., Li X.-Y., Li W.-L., Linic S., Liu C., Liu C., Liu F., Liu M., Ma P., Medford A.J., Mukhopadhyay S., Ou P., Paolucci C., Peng J., Phillips C., Porosoff M.D., Qi L., Sun S., Szilvasi T., Voss J., Wang X., Winther K.T., Wu Q., Zhang D., Zhang Z., Transparent reporting for agentic catalysis enabled by artificial intelligence: Community guidelines and a publication checklist', Chem Catalysis', 6(8), 101755, (2026-08-20), doi:10.1016/j.checat.2026.101755, .
  8. Vinchurkar T., Abdelmaqsoud K., Kitchin J.R., Uncertainty quantification in graph neural networks with shallow ensembles', Machine Learning: Science and Technology', 6(4), 045007, (2025-12-30), doi:10.1088/2632-2153/ae0bf0, .
  9. Canty R.B., Bennett J.A., Brown K.A., Buonassisi T., Kalinin S.V., Kitchin J.R., Maruyama B., Moore R.G., Schrier J., Seifrid M., Sun S., Vegge T., Abolhasani M., Science acceleration and accessibility with self-driving labs', Nature Communications ', 16(1), 3856, (2025-12-01), doi:10.1038/s41467-025-59231-1, .
  10. Colombo Tedesco C., Laird C.D., Kitchin J.R., Khair A.S., Multiscale Perturbation Methods for Dynamic/Programmable Catalysis', Industrial and Engineering Chemistry Research', 64(45), p. 21438-21448, (2025-11-12), doi:10.1021/acs.iecr.5c03023, .
  11. Sunshine E.M., Colombo Tedesco C., Akhade S.A., McNenly M.J., Kitchin J.R., Laird C.D., Hyperplane decision trees as piecewise linear surrogate models for chemical process design', Computers and Chemical Engineering', 202, 109204, (2025-11-01), doi:10.1016/j.compchemeng.2025.109204, .
  12. Orouji N., Bennett J.A., Canty R.B., Qi L., Sun S., Majumdar P., Liu C., Lopez N., Schweitzer N.M., Kitchin J.R., Xin H., Abolhasani M., Autonomous catalysis research with human\xe2\x80\x93AI\xe2\x80\x93robot collaboration', Nature Catalysis', 8(11), p. 1135-1145, (2025-11-01), doi:10.1038/s41929-025-01430-6, .
  13. Bender J.T., Sanspeur R.Y., Bueno Ponce N., Valles A.E., Uvodich A.K., Milliron D.J., Kitchin J.R., Resasco J., How Electrolyte pH Affects the Oxygen Reduction Reaction', Journal of the American Chemical Society', 147(41), p. 37819-37832, (2025-10-15), doi:10.1021/jacs.5c14208, .
  14. Alves V., Laird C.D., Lima F.V., Kitchin J.R., Mapping uncertainty using differentiable programming', AIChE Journal', 71(10), e18940, (2025-10-01), doi:10.1002/aic.18940, .
  15. Xin H., Kitchin J.R., Kulik H.J., Towards agentic science for advancing scientific discovery', Nature Machine Intelligence', 7(9), p. 1373-1375, (2025-09-01), doi:10.1038/s42256-025-01110-x, .
  16. Xu W., Sanspeur R.Y., Kolluru A., Deng B., Harrington P., Farrell S., Reuter K., Kitchin J.R., Spin-informed universal graph neural networks for simulating magnetic ordering', Proceedings of the National Academy of Sciences of the United States of America', 122(27), e2422973122, (2025-07-08), doi:10.1073/pnas.2422973122, .
  17. Kitchin J.R., Solving an inverse problem with generative models', Digital Discovery', 4(7), p. 1856-1869, (2025-06-17), doi:10.1039/d5dd00137d, .
  18. Chilkunda C.R., Kitchin J.R., Tilton R.D., A classification-based methodology for the estimation of binary surfactant critical micelle concentrations', Digital Discovery', 4(6), p. 1449-1456, (2025-04-11), doi:10.1039/d5dd00058k, .
  19. Yuan M., Bucci G., Chatterjee T., Deo S., Kitchin J.R., Laird C.D., Li W., Moore T., Myers C., Sun W., Sunshine E.M., Wang B.-X., McNenly M.J., Akhade S.A., Integrated Systems-to-Atoms (S2A) Framework for Designing Resilient and Efficient Hydrogen Infrastructure Solutions', Energy and Fuels', 39(14), p. 7119-7128, (2025-04-10), doi:10.1021/acs.energyfuels.4c05903, .
  20. Wander B., Shuaibi M., Kitchin J.R., Ulissi Z.W., Zitnick C.L., CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks', ACS Catalysis', 15(7), p. 5283-5294, (2025-04-04), doi:10.1021/acscatal.4c04272, .
  21. Sunshine E.M., Bucci G., Chatterjee T., Deo S., Ehlinger V.M., Li W., Moore T., Myers C., Sun W., Wang B.-X., Yuan M., Kitchin J.R., Laird C.D., McNenly M.J., Akhade S.A., Multiscale optimization of formic acid dehydrogenation process via linear model decision tree surrogates', Computers and Chemical Engineering', 194, 108921, (2025-03-01), doi:10.1016/j.compchemeng.2024.108921, .
  22. Wander B., Musielewicz J., Cheula R., Kitchin J.R., Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis', Journal of Physical Chemistry C', 129(7), p. 3510-3521, (2025-02-20), doi:10.1021/acs.jpcc.4c07477, .
  23. Musielewicz J., Lan J., Uyttendaele M., Kitchin J.R., Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances', Journal of Physical Chemistry C', 128(49), p. 20799-20810, (2024-12-12), doi:10.1021/acs.jpcc.4c04972, .
  24. Sharma U., Nguyen A., Kitchin J.R., Ulissi Z.W., Janik M.J., Enumeration of surface site nuclearity and shape in a database of intermetallic low-index surface facets', Journal of Catalysis', 440, 115795, (2024-12-01), doi:10.1016/j.jcat.2024.115795, .
  25. Huang Y., Wang S.-H., Kamanuru M., Achenie L.E.K., Kitchin J.R., Xin H., Unifying theory of electronic descriptors of metal surfaces upon perturbation', Physical Review B', 110(12), L121404, (2024-09-15), doi:10.1103/PhysRevB.110.L121404, .
  26. Bender J.T., Sanspeur R.Y., Valles A.E., Uvodich A.K., Milliron D.J., Kitchin J.R., Resasco J., The Potential of Zero Total Charge Predicts Cation Effects for the Oxygen Reduction Reaction', ACS Energy Letters', 9(9), p. 4724-4733, (2024-09-13), doi:10.1021/acsenergylett.4c01897, .
  27. Abdelmaqsoud K., Radetic M., Fernandez-Caban C., Widom M., Kitchin J.R., Gellman A.J., Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces', Journal of Physical Chemistry C', 128(33), p. 13879-13887, (2024-08-22), doi:10.1021/acs.jpcc.4c04224, .
  28. Abdelmaqsoud K., Shuaibi M., Kolluru A., Cheula R., Kitchin J.R., Investigating the error imbalance of large-scale machine learning potentials in catalysis', Catalysis Science and Technology', 14(20), p. 5899-5908, (2024-08-14), doi:10.1039/d4cy00615a, .
  29. Broderick K., Burnley R.A., Gellman A.J., Kitchin J.R., Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data', ChemPhysChem', 25(13), e202400073, (2024-07-02), doi:10.1002/cphc.202400073, .
  30. Abed J., Heras-Domingo J., Sanspeur R.Y., Luo M., Alnoush W., Meira D.M., Wang H., Wang J., Zhou J., Zhou D., Fatih K., Kitchin J.R., Higgins D., Ulissi Z.W., Sargent E.H., Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution', Journal of the American Chemical Society', 146(23), p. 15740-15750, (2024-06-12), doi:10.1021/jacs.4c01353, .
  31. Tedesco C.C., Kitchin J.R., Laird C.D., Cyclic Steady-State Simulation and Waveform Design for Dynamic/Programmable Catalysis', Journal of Physical Chemistry C', 128(22), p. 8993-9002, (2024-06-06), doi:10.1021/acs.jpcc.4c01543, .
  32. Brown C., Bilynsky C.S.M., Gainey M., Young S., Kitchin J., Wayne E.C., Exploratory mapping of tumor associated macrophage nanoparticle article abstracts using an eLDA topic modeling machine learning approach', PLoS ONE', 19(6 June), e0304505, (2024-06-01), doi:10.1371/journal.pone.0304505, .
  33. Wang X., Musielewicz J., Tran R., Kumar Ethirajan S., Fu X., Mera H., Kitchin J.R., Kurchin R.C., Ulissi Z.W., Generalization of graph-based active learning relaxation strategies across materials', Machine Learning: Science and Technology', 5(2), 025018, (2024-06-01), doi:10.1088/2632-2153/ad37f0, .
  34. Sanspeur R.Y., Kitchin J.R., Circumventing data imbalance in magnetic ground state data for magnetic moment predictions', Machine Learning: Science and Technology', 5(1), 015023, (2024-03-01), doi:10.1088/2632-2153/ad23fb, .
  35. Porter W.N., Mera H.A., Liao W., Lin Z., Liu P., Kitchin J.R., Chen J.G., Controlling Bond Scission Pathways of Isopropanol on Fe- and Pt-Modified Mo2N Model Surfaces and Powder Catalysts', ACS Catalysis', 14(3), p. 1653-1662, (2024-02-02), doi:10.1021/acscatal.3c04700, .
  36. Garrison A.G., Heras-Domingo J., Kitchin J.R., dos Passos Gomes G., Ulissi Z.W., Blau S.M., Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set', Journal of Chemical Information and Modeling', 63(24), p. 7642-7654, (2023-12-25), doi:10.1021/acs.jcim.3c01226, .
  37. Sunshine E.M., Shuaibi M., Ulissi Z.W., Kitchin J.R., Chemical Properties from Graph Neural Network-Predicted Electron Densities', Journal of Physical Chemistry C', 127(48), p. 23459-23466, (2023-12-07), doi:10.1021/acs.jpcc.3c06157, .
  38. Bhat M., Kitchin J.R., Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications', Industrial and Engineering Chemistry Research', 62(37), p. 15326-15339, (2023-09-20), doi:10.1021/acs.iecr.3c02362, .
  39. Alves V., Kitchin J.R., Lima F.V., An inverse mapping approach for process systems engineering using automatic differentiation and the implicit function theorem', AIChE Journal', 69(9), e18119, (2023-09-01), doi:10.1002/aic.18119, .
  40. Ock J., Tian T., Kitchin J., Ulissi Z., Beyond independent error assumptions in large GNN atomistic models', Journal of Chemical Physics', 158(21), 214702, (2023-06-07), doi:10.1063/5.0151159, .
  41. Sanspeur R.Y., Heras-Domingo J., Kitchin J.R., Ulissi Z., WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions', Journal of Chemical Information and Modeling', 63(8), p. 2427-2437, (2023-04-24), doi:10.1021/acs.jcim.3c00142, .
  42. Bhat M., Simon Z.C., Talledo S., Sen R., Smith J.H., Bernhard S., Millstone J.E., Kitchin J.R., High throughput discovery of ternary Cu-Fe-Ru alloy catalysts for photo-driven hydrogen production', Reaction Chemistry and Engineering', 8(7), p. 1738-1746, (2023-04-12), doi:10.1039/d3re00059a, .
  43. Broderick K., Lopato E., Wander B., Bernhard S., Kitchin J., Ulissi Z., Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions', Applied Catalysis B: Environmental', 320, 121959, (2023-01-01), doi:10.1016/j.apcatb.2022.121959, .
  44. Yang Y., Liu M., Kitchin J.R., Neural network embeddings based similarity search method for atomistic systems', Digital Discovery', 1(5), p. 636-644, (2022-10-01), doi:10.1039/d2dd00055e, .
  45. Kolluru A., Shuaibi M., Palizhati A., Shoghi N., Das A., Wood B., Zitnick C.L., Kitchin J.R., Ulissi Z.W., Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery', ACS Catalysis', 12(14), p. 8572-8581, (2022-07-15), doi:10.1021/acscatal.2c02291, .
  46. Zhan N., Kitchin J.R., Model-Specific to Model-General Uncertainty for Physical Properties', Industrial and Engineering Chemistry Research', 61(24), p. 8368-8377, (2022-06-22), doi:10.1021/acs.iecr.1c04706, .
  47. Zhan N., Kitchin J.R., Uncertainty quantification in machine learning and nonlinear least squares regression models', AIChE Journal', 68(6), e17516, (2022-06-01), doi:10.1002/aic.17516, .
  48. Yang Y., Achar S.K., Kitchin J.R., Evaluation of the degree of rate control via automatic differentiation', AIChE Journal', 68(6), e17653, (2022-06-01), doi:10.1002/aic.17653, .
  49. Bhat M., Lopato E.M., Simon Z.C., Millstone J.E., Bernhard S., Kitchin J.R., Accelerated optimization of pure metal and ligand compositions for light-driven hydrogen production', Reaction Chemistry and Engineering', 7(3), p. 599-608, (2022-03-01), doi:10.1039/d1re00441g, .
  50. Yang Y., Guo Z., Gellman A.J., Kitchin J.R., Simulating Segregation in a Ternary Cu-Pd-Au Alloy with Density Functional Theory, Machine Learning, and Monte Carlo Simulations', Journal of Physical Chemistry C', 126(4), p. 1800-1808, (2022-02-03), doi:10.1021/acs.jpcc.1c09647, .
  51. Simon Z.C., Lopato E.M., Bhat M., Moncure P.J., Bernhard S.M., Kitchin J.R., Bernhard S., Millstone J.E., Ligand Enhanced Activity of In Situ Formed Nanoparticles for Photocatalytic Hydrogen Evolution', ChemCatChem', 14(2), e202101551, (2022-01-21), doi:10.1002/cctc.202101551, .
  52. Zhan N., Kitchin J.R., Origin of the Stokes\xe2\x80\x93Einstein deviation in liquid Al\xe2\x80\x93Si', Molecular Simulation', 48(4), p. 303-313, (2022-01-01), doi:10.1080/08927022.2021.2012572, .
  53. Yang Y., Jimenez-Negron O.A., Kitchin J.R., Machine-learning accelerated geometry optimization in molecular simulation', Journal of Chemical Physics', 154(23), 234704, (2021-06-21), doi:10.1063/5.0049665, .
  54. Liu M., Yang Y., Kitchin J.R., Semi-grand canonical Monte Carlo simulation of the acrolein induced surface segregation and aggregation of AgPd with machine learning surrogate models', Journal of Chemical Physics', 154(13), 134701, (2021-04-07), doi:10.1063/5.0046440, .
  55. Griego C.D., Kitchin J.R., Keith J.A., Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy', International Journal of Quantum Chemistry', 121(1), e26380, (2021-01-05), doi:10.1002/qua.26380, .
  56. Liu M., Kitchin J.R., SingleNN: Modified Behler-Parrinello Neural Network with Shared Weights for Atomistic Simulations with Transferability', Journal of Physical Chemistry C', 124(32), p. 17811-17818, (2020-08-13), doi:10.1021/acs.jpcc.0c04225, .
  57. Lopato E.M., Eikey E.A., Simon Z.C., Back S., Tran K., Lewis J., Kowalewski J.F., Yazdi S., Kitchin J.R., Ulissi Z.W., Millstone J.E., Bernhard S., Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution', ACS Catalysis', 10(7), p. 4244-4252, (2020-04-03), doi:10.1021/acscatal.9b05404, .
  58. Rose M.E., Kitchin J.R., pybliometrics: Scriptable bibliometrics using a Python interface to Scopus', SoftwareX', 10, 100263, (2019-07-01), doi:10.1016/j.softx.2019.100263, .
  59. Gao T., Kitchin J.R., Modeling palladium surfaces with density functional theory, neural networks and molecular dynamics', Catalysis Today', 312, p. 132-140, (2018-08-15), doi:10.1016/j.cattod.2018.03.045, .
  60. Wang C., Tharval A., Kitchin J.R., A density functional theory parameterised neural network model of zirconia', Molecular Simulation', 44(8), p. 623-630, (2018-05-24), doi:10.1080/08927022.2017.1420185, .
  61. Thirumalai H., Kitchin J.R., Investigating the Reactivity of Single Atom Alloys Using Density Functional Theory', Topics in Catalysis', 61(5-6), p. 462-474, (2018-05-01), doi:10.1007/s11244-018-0899-0, .
  62. Kitchin J.R., Machine learning in catalysis', Nature Catalysis', 1(4), p. 230-232, (2018-04-01), doi:10.1038/s41929-018-0056-y, .
  63. Saravanan K., Kitchin J.R., Von Lilienfeld O.A., Keith J.A., Alchemical Predictions for Computational Catalysis: Potential and Limitations', Journal of Physical Chemistry Letters', 8(20), p. 5002-5007, (2017-10-19), doi:10.1021/acs.jpclett.7b01974, .
  64. Wittkamper J., Xu Z., Kombaiah B., Ram F., De Graef M., Kitchin J.R., Rohrer G.S., Salvador P.A., Competitive Growth of Scrutinyite (\xce\xb1-PbO2) and Rutile Polymorphs of SnO2 on All Orientations of Columbite CoNb2O6 Substrates', Crystal Growth and Design', 17(7), p. 3929-3939, (2017-07-05), doi:10.1021/acs.cgd.7b00569, .
  65. Hjorth Larsen A., JOrgen Mortensen J., Blomqvist J., Castelli I.E., Christensen R., Dulak M., Friis J., Groves M.N., Hammer B., Hargus C., Hermes E.D., Jennings P.C., Bjerre Jensen P., Kermode J., Kitchin J.R., Leonhard Kolsbjerg E., Kubal J., Kaasbjerg K., Lysgaard S., Bergmann Maronsson J., Maxson T., Olsen T., Pastewka L., Peterson A., Rostgaard C., SchiOtz J., Schutt O., Strange M., Thygesen K.S., Vegge T., Vilhelmsen L., Walter M., Zeng Z., Jacobsen K.W., The atomic simulation environment - A Python library for working with atoms', Journal of Physics Condensed Matter', 29(27), 273002, (2017-06-07), doi:10.1088/1361-648X/aa680e, .
  66. Kitchin J.R., Van Gulick A.E., Zilinski L.D., Automating data sharing through authoring tools', International Journal on Digital Libraries', 18(2), p. 93-98, (2017-06-01), doi:10.1007/s00799-016-0173-7, .
  67. Boes J.R., Kitchin J.R., Neural network predictions of oxygen interactions on a dynamic Pd surface', Molecular Simulation', 43(5-6), p. 346-354, (2017-04-13), doi:10.1080/08927022.2016.1274984, .
  68. Geng F., Boes J.R., Kitchin J.R., First-principles study of the Cu-Pd phase diagram', Calphad: Computer Coupling of Phase Diagrams and Thermochemistry', 56, p. 224-229, (2017-03-01), doi:10.1016/j.calphad.2017.01.009, .
  69. Boes J.R., Kitchin J.R., Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations', Journal of Physical Chemistry C', 121(6), p. 3479-3487, (2017-02-16), doi:10.1021/acs.jpcc.6b12752, .
  70. Xu Z., Salvador P., Kitchin J.R., First-principles investigation of the epitaxial stabilization of oxide polymorphs: TiO2 on (Sr,Ba)TiO3', ACS Applied Materials and Interfaces', 9(4), p. 4106-4118, (2017-02-01), doi:10.1021/acsami.6b11791, .
  71. Kitchin J.R., Gellman A.J., High-throughput methods using composition and structure spread libraries', AIChE Journal', 62(11), p. 3826-3835, (2016-11-01), doi:10.1002/aic.15294, .
  72. Deshpande S., Kitchin J.R., Viswanathan V., Quantifying Uncertainty in Activity Volcano Relationships for Oxygen Reduction Reaction', ACS Catalysis', 6(8), p. 5251-5259, (2016-08-05), doi:10.1021/acscatal.6b00509, .
  73. Calfa B.A., Kitchin J.R., Property prediction of crystalline solids from composition and crystal structure', AIChE Journal', 62(8), p. 2605-2613, (2016-08-01), doi:10.1002/aic.15251, .
  74. Thirumalai H., Kitchin J.R., The role of vdW interactions in coverage dependent adsorption energies of atomic adsorbates on Pt(111) and Pd(111)', Surface Science', 650, p. 196-202, (2016-08-01), doi:10.1016/j.susc.2015.10.001, .
  75. Boes J.R., Groenenboom M.C., Keith J.A., Kitchin J.R., Neural network and ReaxFF comparison for Au properties', International Journal of Quantum Chemistry', 116(13), p. 979-987, (2016-07-05), doi:10.1002/qua.25115, .
  76. Kitchin J.R., Data sharing in Surface Science', Surface Science', 647, p. 103-107, (2016-05-01), doi:10.1016/j.susc.2015.05.007, .
  77. Bligaard T., Bullock R.M., Campbell C.T., Chen J.G., Gates B.C., Gorte R.J., Jones C.W., Jones W.D., Kitchin J.R., Scott S.L., Toward Benchmarking in Catalysis Science: Best Practices, Challenges, and Opportunities', ACS Catalysis', 6(4), p. 2590-2602, (2016-04-01), doi:10.1021/acscatal.6b00183, .
  78. Hallenbeck A.P., Egbebi A., Resnik K.P., Hopkinson D., Anna S.L., Kitchin J.R., Comparative microfluidic screening of amino acid salt solutions for post-combustion CO2 capture', International Journal of Greenhouse Gas Control', 43, p. 189-197, (2015-12-01), doi:10.1016/j.ijggc.2015.10.026, .
  79. Watkins J.D., Siefert N.S., Zhou X., Myers C.R., Kitchin J.R., Hopkinson D.P., Nulwala H.B., Redox-Mediated Separation of Carbon Dioxide from Flue Gas', Energy and Fuels', 29(11), p. 7508-7515, (2015-11-19), doi:10.1021/acs.energyfuels.5b01807, .
  80. Xu Z., Kitchin J.R., Tuning oxide activity through modification of the crystal and electronic structure: from strain to potential polymorphs', Physical Chemistry Chemical Physics', 17(43), p. 28943-28949, (2015-09-30), doi:10.1039/c5cp04840k, .
  81. Curnan M.T., Kitchin J.R., Investigating the Energetic Ordering of Stable and Metastable TiO2 Polymorphs Using DFT+U and Hybrid Functionals', Journal of Physical Chemistry C', 119(36), p. 21060-21071, (2015-09-10), doi:10.1021/acs.jpcc.5b05338, .
  82. Kitchin J.R., Examples of effective data sharing in scientific publishing', ACS Catalysis', 5(6), p. 3894-3899, (2015-06-05), doi:10.1021/acscatal.5b00538, .
  83. Michael J.D., Demeter E.L., Illes S.M., Fan Q., Boes J.R., Kitchin J.R., Alkaline electrolyte and fe impurity effects on the performance and active-phase structure of niooh thin films for OER catalysis applications', Journal of Physical Chemistry C', 119(21), p. 11475-11481, (2015-05-28), doi:10.1021/acs.jpcc.5b02458, .
  84. Gumuslu G., Kondratyuk P., Boes J.R., Morreale B., Miller J.B., Kitchin J.R., Gellman A.J., Correlation of electronic structure with catalytic activity: H2-D2 exchange across CuxPd1- x composition space', ACS Catalysis', 5(5), p. 3137-3147, (2015-05-01), doi:10.1021/cs501586t, .
  85. Xu Z., Joshi Y.V., Raman S., Kitchin J.R., Accurate electronic and chemical properties of 3d transition metal oxides using a calculated linear response U and a DFT + U (V) method', Journal of Chemical Physics', 142(14), 144701, (2015-04-14), doi:10.1063/1.4916823, .
  86. Xu Z., Kitchin J.R., Relationships between the surface electronic and chemical properties of doped 4d and 5d late transition metal dioxides', Journal of Chemical Physics', 142(10), 104703, (2015-03-14), doi:10.1063/1.4914093, .
  87. Xu Z., Rossmeisl J., Kitchin J.R., A linear response DFT+U study of trends in the oxygen evolution activity of transition metal rutile dioxides', Journal of Physical Chemistry C', 119(9), p. 4827-4833, (2015-03-05), doi:10.1021/jp511426q, .
  88. Boes J.R., Gumuslu G., Miller J.B., Gellman A.J., Kitchin J.R., Estimating bulk-composition-dependent H2 adsorption energies on CuxPd1- x alloy (111) surfaces', ACS Catalysis', 5(2), p. 1020-1026, (2015-02-06), doi:10.1021/cs501585k, .
  89. Boes J.R., Kondratyuk P., Yin C., Miller J.B., Gellman A.J., Kitchin J.R., Core level shifts in Cu-Pd alloys as a function of bulk composition and structure', Surface Science', 640, p. 127-132, (2015-01-01), doi:10.1016/j.susc.2015.02.011, .
  90. Curnan M.T., Kitchin J.R., Effects of concentration, crystal structure, magnetism, and electronic structure method on first-principles oxygen vacancy formation energy trends in perovskites', Journal of Physical Chemistry C', 118(49), p. 28776-28790, (2014-12-11), doi:10.1021/jp507957n, .
  91. Xu Z., Kitchin J.R., Probing the coverage dependence of site and adsorbate configurational correlations on (111) surfaces of late transition metals', Journal of Physical Chemistry C', 118(44), p. 25597-25602, (2014-11-06), doi:10.1021/jp508805h, .
  92. Xu Z., Kitchin J.R., Relating the electronic structure and reactivity of the 3d transition metal monoxide surfaces', Catalysis Communications', 52, p. 60-64, (2014-07-05), doi:10.1016/j.catcom.2013.10.028, .
  93. Demeter E.L., Hilburg S.L., Washburn N.R., Collins T.J., Kitchin J.R., Electrocatalytic oxygen evolution with an immobilized TAML activator', Journal of the American Chemical Society', 136(15), p. 5603-5606, (2014-04-16), doi:10.1021/ja5015986, .
  94. Thompson R.L., Shi W., Albenze E., Kusuma V.A., Hopkinson D., Damodaran K., Lee A.S., Kitchin J.R., Luebke D.R., Nulwala H., Probing the effect of electron donation on CO2 absorbing 1,2,3-triazolide ionic liquids', RSC Advances', 4(25), p. 12748-12755, (2014-03-17), doi:10.1039/c3ra47097k, .
  95. Mehta P., Salvador P.A., Kitchin J.R., Identifying potential BO2 oxide polymorphs for epitaxial growth candidates', ACS Applied Materials and Interfaces', 6(5), p. 3630-3639, (2014-03-12), doi:10.1021/am4059149, .