John Kitchin

Professor

Carnegie Mellon University

United States

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John Kitchin is a Professor at Carnegie Mellon University in the United States. His research areas encompass electrolyte pH impacts on oxygen reduction reactions, uncertainty quantification in graph neural networks, and the advancement of self-driving laboratories for scientific discovery. Additionally, he focuses on developing resilient hydrogen infrastructure solutions and methodologies for estimating surfactant critical micelle concentrations.

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UNITED STATES

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Joshua Schrier

Fordham University

UNITED STATES

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John Kitchin

Research Interests

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About

Articles (30)

A classification-based methodology for the estimation of binary surfactant critical micelle concentrations

How Electrolyte pH Affects the Oxygen Reduction Reaction

Uncertainty quantification in graph neural networks with shallow ensembles

Towards agentic science for advancing scientific discovery

The evolving role of programming and LLMs in the development of self-driving laboratories

Science acceleration and accessibility with self-driving labs

Integrated Systems-to-Atoms (S2A) Framework for Designing Resilient and Efficient Hydrogen Infrastructure Solutions

CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks

Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis

A classification-based methodology for the estimation of binary surfactant critical micelle concentrations

The Potential of Zero Total Charge Predicts Cation Effects for the Oxygen Reduction Reaction

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Cyclic Steady-State Simulation and Waveform Design for Dynamic/Programmable Catalysis

Generalization of graph-based active learning relaxation strategies across materials

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

Controlling Bond Scission Pathways of Isopropanol on Fe- and Pt-Modified Mo2N Model Surfaces and Powder Catalysts

Exploratory mapping of tumor associated macrophage nanoparticle article abstracts using an eLDA topic modeling machine learning approach

Investigating the error imbalance of large-scale machine learning potentials in catalysis

Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data

Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications

An inverse mapping approach for process systems engineering using automatic differentiation and the implicit function theorem

Beyond independent error assumptions in large GNN atomistic models

WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions

High throughput discovery of ternary Cu–Fe–Ru alloy catalysts for photo-driven hydrogen production

Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery

Model-Specific to Model-General Uncertainty for Physical Properties

Evaluation of the degree of rate control via automatic differentiation

Uncertainty quantification in machine learning and nonlinear least squares regression models

Origin of the Stokes–Einstein deviation in liquid Al–Si

Collaborators (8)

Stefan Bernhard

Elizabeth Wayne

Heather Kulik

Robert Tilton

Sergei Kalinin

Rachel Kurchin

Jill Millstone

Joshua Schrier

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