Related references
Note: Only part of the references are listed.Rapid, automated determination of reaction models and kinetic parameters
Connor J. Taylor et al.
CHEMICAL ENGINEERING JOURNAL (2021)
TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments
Adam Yonge et al.
CHEMICAL ENGINEERING JOURNAL (2021)
Passive NOx adsorption on Pd/H-ZSM-5: Experiments and modeling
Mugdha Ambast et al.
APPLIED CATALYSIS B-ENVIRONMENTAL (2020)
Leveraging Thermochemistry Data to Build Accurate Microkinetic Models
Huijie Tian et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2020)
Efficient Kinetic Data Acquisition and Model Prediction: Continuous Flow Microreactors, Inline Fourier Transform Infrared Spectroscopy, and Self-Modeling Curve Resolution
Verena Fath et al.
ORGANIC PROCESS RESEARCH & DEVELOPMENT (2020)
Probability theory for inverse diffusion: Extracting the transport/kinetic time-dependence from transient experiments
M. Ross Kunz et al.
CHEMICAL ENGINEERING JOURNAL (2020)
Model-based design of transient flow experiments for the identification of kinetic parameters
Conor Waldron et al.
REACTION CHEMISTRY & ENGINEERING (2020)
Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects
Jonathan Lym et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2019)
Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
Seoin Back et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)
Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis
Sebastian Matera et al.
ACS CATALYSIS (2019)
Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
Seoin Back et al.
ACS CATALYSIS (2019)
Predicting Adsorption Energies Using Multifidelity Data
Huijie Tian et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
Rate/Concentration Kinetic Petals: A Transient Method to Examine the Interplay of Surface Reaction Processes
Yixiao Wang et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2019)
Estimating vibrational and thermodynamic properties of adsorbates with uncertainty using data driven surrogates
Huijie Tian et al.
AICHE JOURNAL (2019)
Methods for determining the intrinsic kinetic characteristics of irreversible adsorption processes
Denis Constales et al.
CHEMICAL ENGINEERING SCIENCE (2019)
Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks
Aini Palizhati et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)
Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration
Bowen Li et al.
MOLECULAR SYSTEMS DESIGN & ENGINEERING (2019)
Modeling palladium surfaces with density functional theory, neural networks and molecular dynamics
Tianyu Gao et al.
CATALYSIS TODAY (2018)
Pulse response analysis using the Y-procedure: A data science approach
M. Ross Kunz et al.
CHEMICAL ENGINEERING SCIENCE (2018)
A density functional theory parameterised neural network model of zirconia
Chen Wang et al.
MOLECULAR SIMULATION (2018)
Extracting Knowledge from Data through Catalysis Informatics
Andrew J. Medford et al.
ACS CATALYSIS (2018)
Efficient kinetic experiments in continuous flow microreactors
Kosi C. Aroh et al.
REACTION CHEMISTRY & ENGINEERING (2018)
Applications of Modulation Excitation Spectroscopy in Heterogeneous Catalysis
Philipp Müller et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2017)
Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems
Srinivas Rangarajan et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2017)
Dense CO Adlayers as Enablers of CO Hydrogenation Turnovers on Ru Surfaces
Jianwei Liu et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2017)
Neural network predictions of oxygen interactions on a dynamic Pd surface
Jacob R. Boes et al.
MOLECULAR SIMULATION (2017)
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
Zachary W. Ulissi et al.
NATURE COMMUNICATIONS (2017)
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction
Zachary W. Ulissi et al.
ACS CATALYSIS (2017)
Forty years of temporal analysis of products
K. Morgan et al.
CATALYSIS SCIENCE & TECHNOLOGY (2017)
Precise non-steady-state characterization of solid active materials with no preliminary mechanistic assumptions
Denis Constales et al.
CATALYSIS TODAY (2017)
Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning
Joshua A. Gomberg et al.
ACTA MATERIALIA (2017)
An overview of the estimation of large covariance and precision matrices
Jianqing Fan et al.
ECONOMETRICS JOURNAL (2016)
Rate-Reactivity Model: A New Theoretical Basis for Systematic Kinetic Characterization of Heterogeneous Catalysts
Gregory S. Yablonsky et al.
INTERNATIONAL JOURNAL OF CHEMICAL KINETICS (2016)
Neural Network and ReaxFF Comparison for Au Properties
Jacob R. Boes et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2016)
Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning
Zachary W. Ulissi et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2016)
CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends
Andrew J. Medford et al.
CATALYSIS LETTERS (2015)
Elucidating complex catalytic mechanisms based on transient pulse-response kinetic data
Evgeniy A. Redekop et al.
CHEMICAL ENGINEERING SCIENCE (2014)
Assessing the reliability of calculated catalytic ammonia synthesis rates
Andrew J. Medford et al.
SCIENCE (2014)
Recent Approaches in Mechanistic and Kinetic Studies of Catalytic Reactions Using SSITKA Technique
Cristian Ledesma et al.
ACS CATALYSIS (2014)
COORDINATE DESCENT ALGORITHMS FOR NONCONVEX PENALIZED REGRESSION, WITH APPLICATIONS TO BIOLOGICAL FEATURE SELECTION
Patrick Breheny et al.
ANNALS OF APPLIED STATISTICS (2011)
The Y-Procedure methodology for the interpretation of transient kinetic data: Analysis of irreversible adsorption
Evgeniy A. Redekop et al.
CHEMICAL ENGINEERING SCIENCE (2011)
Density functional theory in surface chemistry and catalysis
Jens K. Norskov et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2011)
Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME
Karline Soetaert et al.
JOURNAL OF STATISTICAL SOFTWARE (2010)
Solving Differential Equations in R: Package deSolve
Karline Soetaert et al.
JOURNAL OF STATISTICAL SOFTWARE (2010)
Needle in a haystack catalysis
Xiaolin Zheng et al.
APPLIED CATALYSIS A-GENERAL (2008)
Sparse inverse covariance estimation with the graphical lasso
Jerome Friedman et al.
BIOSTATISTICS (2008)
The Y-procedure: How to extract the chemical transformation rate from reaction-diffusion data with no assumption on the kinetic model
G. S. Yablonsky et al.
CHEMICAL ENGINEERING SCIENCE (2007)
Ab initio molecular dynamics of hydrogen dissociation on metal surfaces using neural networks and novelty sampling
Jeffery Ludwig et al.
JOURNAL OF CHEMICAL PHYSICS (2007)
Noise in temporal analysis of products (TAP) pulse responses
Raf Roelant et al.
CATALYSIS TODAY (2007)
Assessment of kinetic modeling procedures of TAP experiments
Y. Schuurman
CATALYSIS TODAY (2007)
High-dimensional graphs and variable selection with the Lasso
Nicolai Meinshausen et al.
ANNALS OF STATISTICS (2006)
Toward computational screening in heterogeneous catalysis: Pareto-optimal methanation catalysts
MP Andersson et al.
JOURNAL OF CATALYSIS (2006)
Regularization and variable selection via the elastic net
H Zou et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)
Ruthenium as oxidation catalyst:: bridging the pressure and material gaps between ideal and real systems in heterogeneous catalysis by applying DRIFT spectroscopy and the TAP reactor
J Assmann et al.
CATALYSIS TODAY (2003)
Universality in heterogeneous catalysis
JK Norskov et al.
JOURNAL OF CATALYSIS (2002)
Variable selection via nonconcave penalized likelihood and its oracle properties
JQ Fan et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2001)