DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Dynamic Workflows for Routine Materials Discovery in Surface Science

Abstract

The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. In this paper, we discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.

Authors:
 [1];  [1];  [1]; ORCiD logo [1]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1543623
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Information and Modeling
Additional Journal Information:
Journal Volume: 58; Journal Issue: 12; Journal ID: ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Pharmacology and Pharmacy; Chemistry; Computer Science

Citation Formats

Tran, Kevin, Palizhati, Aini, Back, Seoin, and Ulissi, Zachary W. Dynamic Workflows for Routine Materials Discovery in Surface Science. United States: N. p., 2018. Web. doi:10.1021/acs.jcim.8b00386.
Tran, Kevin, Palizhati, Aini, Back, Seoin, & Ulissi, Zachary W. Dynamic Workflows for Routine Materials Discovery in Surface Science. United States. https://doi.org/10.1021/acs.jcim.8b00386
Tran, Kevin, Palizhati, Aini, Back, Seoin, and Ulissi, Zachary W. Mon . "Dynamic Workflows for Routine Materials Discovery in Surface Science". United States. https://doi.org/10.1021/acs.jcim.8b00386. https://www.osti.gov/servlets/purl/1543623.
@article{osti_1543623,
title = {Dynamic Workflows for Routine Materials Discovery in Surface Science},
author = {Tran, Kevin and Palizhati, Aini and Back, Seoin and Ulissi, Zachary W.},
abstractNote = {The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. In this paper, we discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.},
doi = {10.1021/acs.jcim.8b00386},
journal = {Journal of Chemical Information and Modeling},
number = 12,
volume = 58,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 14 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

A safe operating space for humanity
journal, September 2009

  • Rockström, Johan; Steffen, Will; Noone, Kevin
  • Nature, Vol. 461, Issue 7263
  • DOI: 10.1038/461472a

Thresholds of catastrophe in the Earth system
journal, September 2017


Combining theory and experiment in electrocatalysis: Insights into materials design
journal, January 2017


The path towards sustainable energy
journal, December 2016

  • Chu, Steven; Cui, Yi; Liu, Nian
  • Nature Materials, Vol. 16, Issue 1
  • DOI: 10.1038/nmat4834

Photovoltaic materials: Present efficiencies and future challenges
journal, April 2016


Batteries and fuel cells for emerging electric vehicle markets
journal, April 2018


Towards high throughput screening of electrochemical stability of battery electrolytes
journal, August 2015


Efficient Computational Screening of Organic Polymer Photovoltaics
journal, April 2013

  • Kanal, Ilana Y.; Owens, Steven G.; Bechtel, Jonathon S.
  • The Journal of Physical Chemistry Letters, Vol. 4, Issue 10
  • DOI: 10.1021/jz400215j

Discovery of Pb-Free Perovskite Solar Cells via High-Throughput Simulation on the K Computer
journal, September 2017


Examples of Effective Data Sharing in Scientific Publishing
journal, May 2015


The atomic simulation environment—a Python library for working with atoms
journal, June 2017

  • Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob
  • Journal of Physics: Condensed Matter, Vol. 29, Issue 27
  • DOI: 10.1088/1361-648X/aa680e

The Computational Materials Repository
journal, November 2012

  • Landis, David D.; Hummelshoj, Jens S.; Nestorov, Svetlozar
  • Computing in Science & Engineering, Vol. 14, Issue 6
  • DOI: 10.1109/MCSE.2012.16

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
journal, February 2013


The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015


Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013

  • Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
  • APL Materials, Vol. 1, Issue 1
  • DOI: 10.1063/1.4812323

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012


Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases
journal, March 2016

  • Jain, Anubhav; Persson, Kristin A.; Ceder, Gerbrand
  • APL Materials, Vol. 4, Issue 5
  • DOI: 10.1063/1.4944683

FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications
journal, May 2015

  • Jain, Anubhav; Ong, Shyue Ping; Chen, Wei
  • Concurrency and Computation: Practice and Experience, Vol. 27, Issue 17
  • DOI: 10.1002/cpe.3505

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
journal, November 2017


Electronic factors determining the reactivity of metal surfaces
journal, December 1995


Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces
journal, July 2007


Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers
journal, June 2014

  • Calle-Vallejo, Federico; Martínez, José I.; García-Lastra, Juan M.
  • Angewandte Chemie International Edition, Vol. 53, Issue 32
  • DOI: 10.1002/anie.201402958

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
journal, January 2011

  • Behler, Jörg
  • Physical Chemistry Chemical Physics, Vol. 13, Issue 40
  • DOI: 10.1039/c1cp21668f

Potential Energy Surfaces Fitted by Artificial Neural Networks
journal, March 2010

  • Handley, Chris M.; Popelier, Paul L. A.
  • The Journal of Physical Chemistry A, Vol. 114, Issue 10
  • DOI: 10.1021/jp9105585

To address surface reaction network complexity using scaling relations machine learning and DFT calculations
journal, March 2017

  • Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms14621

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
journal, November 2015


Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends
journal, March 2014

  • Meredig, Bryce; Wolverton, C.
  • Chemistry of Materials, Vol. 26, Issue 6
  • DOI: 10.1021/cm403727z

Amp: A modular approach to machine learning in atomistic simulations
journal, October 2016


An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
journal, March 2016


Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
journal, April 2018


A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016


AiiDA: automated interactive infrastructure and database for computational science
journal, January 2016


Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
journal, September 2018


A high-throughput framework for determining adsorption energies on solid surfaces
journal, March 2017


Active Learning
journal, June 2012


Adaptive sequential sampling for surrogate model generation with artificial neural networks
journal, September 2014


Learning surrogate models for simulation-based optimization
journal, March 2014

  • Cozad, Alison; Sahinidis, Nikolaos V.; Miller, David C.
  • AIChE Journal, Vol. 60, Issue 6
  • DOI: 10.1002/aic.14418

Trends in the Exchange Current for Hydrogen Evolution
journal, January 2005

  • Nørskov, J. K.; Bligaard, T.; Logadottir, A.
  • Journal of The Electrochemical Society, Vol. 152, Issue 3
  • DOI: 10.1149/1.1856988

Understanding trends in electrochemical carbon dioxide reduction rates
journal, May 2017

  • Liu, Xinyan; Xiao, Jianping; Peng, Hongjie
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms15438

Best Practices for Scientific Computing
journal, January 2014


Works referencing / citing this record:

Identifying promising metal–organic frameworks for heterogeneous catalysis via high‐throughput periodic density functional theory
journal, February 2019

  • Rosen, Andrew S.; Notestein, Justin M.; Snurr, Randall Q.
  • Journal of Computational Chemistry, Vol. 40, Issue 12
  • DOI: 10.1002/jcc.25787

In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling
journal, January 2020

  • Saxena, Shivam; Khan, Tuhin Suvra; Jalid, Fatima
  • Journal of Materials Chemistry A, Vol. 8, Issue 1
  • DOI: 10.1039/c9ta07651d

Triplet state structure–property relationships in a series of platinum acetylides: effect of chromophore length and end cap electronic properties
journal, January 2019

  • Cooper, Thomas M.; Haley, Joy E.; Krein, Douglas M.
  • Physical Chemistry Chemical Physics, Vol. 21, Issue 48
  • DOI: 10.1039/c9cp02892g