Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Journal Article · · Journal of Chemical Theory and Computation
 [1];  [1];  [1];  [1];  [1];  [2]
  1. University of South Carolina, Columbia, SC (United States)
  2. University of North Carolina, Charlotte, NC (United States)
+ Show Author Affiliations
Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.
Research Organization:
University of South Carolina, Columbia, SC (United States)
Sponsoring Organization:
National Science Foundation; USDOE Office of Science (SC), Basic Energy Sciences (BES); XSEDE
Grant/Contract Number:
AC02-05CH11231; SC0007167
OSTI ID:
1656919
Alternate ID(s):
OSTI ID: 3001236
Journal Information:
Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 2 Vol. 16; ISSN 1549-9618
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (30)

A comparative investigation of non-linear activation functions in neural controllers for search-based game AI engineering journal December 2011
Molecular graph convolutions: moving beyond fingerprints journal August 2016
A theory of learning from different domains journal October 2009
Machine learning in chemoinformatics and drug discovery journal August 2018
Microkinetic modeling of the decarboxylation and decarbonylation of propanoic acid over Pd(111) model surfaces based on parameters obtained from first principles journal September 2013
Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals journal December 2016
Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning journal November 2018
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space journal June 2015
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules journal July 2015
Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction journal August 2017
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks journal December 2017
Extended-Connectivity Fingerprints journal April 2010
A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections journal April 2013
The role of computational results databases in accelerating the discovery of catalysts journal October 2018
Linear scaling relationships and volcano plots in homogeneous catalysis – revisiting the Suzuki reaction journal January 2015
MoleculeNet: a benchmark for molecular machine learning journal January 2018
Atom-centered symmetry functions for constructing high-dimensional neural network potentials journal February 2011
Perspective: Machine learning potentials for atomistic simulations journal November 2016
Constant size descriptors for accurate machine learning models of molecular properties journal June 2018
Density functional theory in surface chemistry and catalysis journal January 2011
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning journal January 2012
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal April 2007
Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces journal July 2007
Gradient-based learning applied to document recognition journal January 1998
Receptive fields of single neurones in the cat's striate cortex journal October 1959
Mapping receptive fields in primary visual cortex: Mapping receptive fields in primary visual cortex journal July 2004
Inverse molecular design using machine learning: Generative models for matter engineering journal July 2018
ImageNet classification with deep convolutional neural networks journal May 2017
Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design journal June 2016
3D deep convolutional neural networks for amino acid environment similarity analysis journal June 2017

Similar Records

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces
Journal Article · Mon Aug 09 20:00:00 EDT 2021 · Journal of Physical Chemistry. C · OSTI ID:3001235
Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning
Journal Article · Thu Nov 08 19:00:00 EST 2018 · Journal of Physical Chemistry. C · OSTI ID:1484052

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Adsorption
Energy
Molecular modeling
Molecules
Neural networks