John R. Kitchin Citation name: Kitchin, J. R. Affiliation: Carnegie Mellon University State: PA Country: USA Groups/Topicals chaired or co-chaired:Catalysis and Reaction Engineering Division2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)Catalysis and Reaction Engineering Division2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)Catalysis and Reaction Engineering Division2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Sessions chaired or co-chaired:Applications of DFT+X in Catalysis2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Applications of DFT+X in Catalysis II2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Membrane Reactors II2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Poster Session: Catalysis and Reaction Engineering (CRE) Division2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Areas chaired or co-chaired:Catalysis and Reaction Engineering Division2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)Catalysis and Reaction Engineering Division2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)Catalysis and Reaction Engineering Division2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)Authored:(777g) Property Prediction of Crystalline Solids from Composition and Crystal StructureBruno A. CalfaJohn R. Kitchin2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)(617gk) Neural Network Predictions of Oxygen Interactions on a Dynamic Pd SurfaceJacob R. BoesJohn R. Kitchin2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)(253v) Practical Data Sharing for Molecular SimulationJacob R. BoesJohn R. Kitchin2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)(370f) Neural Network and Reaxff Comparison for Au PropertiesJacob R. BoesMitchell GroenenboomJohn A. KeithJohn R. Kitchin2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)(595b) Expanding Molecular Simulation Use By Data/Code Sharing in Scientific PublishingJohn R. Kitchin2016 AIChE Annual Meeting (ISBN: 978-0-8169-1097-7)(392g) Relationships Between the Surface Electronic and Chemical Properties of Doped 4d and 5d Late Transition Metal DioxidesZhongnan XuJohn R. Kitchin2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)(403a) Tuning Oxide Activity through Modification of the Crystal and Electronic Structure: From Strain to Potential PolymorphsZhongnan XuJohn R. Kitchin2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)(719f) Comparative Microfluidic Screening of Amino Acid Salt Solutions for Post-Combustion CO2 CaptureAlexander HallenbeckAdefemi EgbebiKevin P. ResnikDavid HopkinsonJohn R. KitchinShelley L. Anna2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)(432e) Core Level Shifts in Cu-Pd Alloys As a Function of Bulk Composition and StructureJacob R. BoesJohn R. Kitchin2015 AIChE Annual Meeting Proceedings (ISBN: 978-0-8169-1094-6)(684c) Accelerated Catalyst Screening Using Computational AlchemyJohn A. KeithJohn R. KitchinKarthikeyan SaravananO. Anatole von Lilienfeld2017 Annual Meeting (ISBN: 978-0-8169-1102-8)(377d) Modeling of Segregation on Au-Pd (111) Surfaces with Monte Carlo Simulations and Neural Network Atomic PotentialsJacob R. BoesJohn R. Kitchin2017 Annual Meeting (ISBN: 978-0-8169-1102-8)(345e) Applying DFT+U and the Self-Consistent Linear Response Approach to Establish a Thermodynamic Framework for Comparing Redox Reaction Energies in PerovskitesMatthew CurnanJohn R. Kitchin2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)(49d) Elucidating Adsorption Mechanisms On Oxides By Comparing Adsorption On FCC Metals and Reduced B1 Monoxide SurfacesZhongnan XuJohn R. Kitchin2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)(289e) Towards More Accurate Reaction Enthalpies With DFT+U Linear Response MethodsZhongnan XuJohn R. Kitchin2013 AIChE Annual Meeting (ISBN: 978-0-8169-1110-3)(158f) Leveraging DFT with Machine Learning: Applications in CatalysisJohn R. Kitchin2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)(538a) Single-NN: A Modified Behler-Parinello Neural Network with a Single Neural NetworkJohn R. KitchinMingjie Liu[Pre-conference] 2019 AIChE Annual Meeting (411j) Uncertainty Measurement Method for Machine Learned PotentialsNi ZhanJohn R. Kitchin[Pre-conference] 2019 AIChE Annual Meeting