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.
- 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}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 14 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
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
Thresholds of catastrophe in the Earth system
journal, September 2017
- Rothman, Daniel H.
- Science Advances, Vol. 3, Issue 9
Combining theory and experiment in electrocatalysis: Insights into materials design
journal, January 2017
- Seh, Zhi Wei; Kibsgaard, Jakob; Dickens, Colin F.
- Science, Vol. 355, Issue 6321
The path towards sustainable energy
journal, December 2016
- Chu, Steven; Cui, Yi; Liu, Nian
- Nature Materials, Vol. 16, Issue 1
Photovoltaic materials: Present efficiencies and future challenges
journal, April 2016
- Polman, A.; Knight, M.; Garnett, E. C.
- Science, Vol. 352, Issue 6283
Batteries and fuel cells for emerging electric vehicle markets
journal, April 2018
- Cano, Zachary P.; Banham, Dustin; Ye, Siyu
- Nature Energy, Vol. 3, Issue 4
Towards high throughput screening of electrochemical stability of battery electrolytes
journal, August 2015
- Borodin, Oleg; Olguin, Marco; Spear, Carrie E.
- Nanotechnology, Vol. 26, Issue 35
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
Discovery of Pb-Free Perovskite Solar Cells via High-Throughput Simulation on the K Computer
journal, September 2017
- Nakajima, Takahito; Sawada, Keisuke
- The Journal of Physical Chemistry Letters, Vol. 8, Issue 19
Examples of Effective Data Sharing in Scientific Publishing
journal, May 2015
- Kitchin, John R.
- ACS Catalysis, Vol. 5, Issue 6
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
The Computational Materials Repository
journal, November 2012
- Landis, David D.; Hummelshoj, Jens S.; Nestorov, Svetlozar
- Computing in Science & Engineering, Vol. 14, Issue 6
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
journal, February 2013
- Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav
- Computational Materials Science, Vol. 68
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015
- Kirklin, Scott; Saal, James E.; Meredig, Bryce
- npj Computational Materials, Vol. 1, Issue 1
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
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012
- Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong
- Computational Materials Science, Vol. 58
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
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
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
journal, November 2017
- Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza
- Computational Materials Science, Vol. 139
Electronic factors determining the reactivity of metal surfaces
journal, December 1995
- Hammer, B.; Nørskov, J. K.
- Surface Science, Vol. 343, Issue 3
Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces
journal, July 2007
- Abild-Pedersen, F.; Greeley, J.; Studt, F.
- Physical Review Letters, Vol. 99, Issue 1
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
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
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
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
Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
journal, November 2015
- Seko, Atsuto; Togo, Atsushi; Hayashi, Hiroyuki
- Physical Review Letters, Vol. 115, Issue 20
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
Amp: A modular approach to machine learning in atomistic simulations
journal, October 2016
- Khorshidi, Alireza; Peterson, Andrew A.
- Computer Physics Communications, Vol. 207
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
journal, March 2016
- Artrith, Nongnuch; Urban, Alexander
- Computational Materials Science, Vol. 114
Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
journal, April 2017
- Kolb, Brian; Lentz, Levi C.; Kolpak, Alexie M.
- Scientific Reports, Vol. 7, Issue 1
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
journal, April 2018
- Xie, Tian; Grossman, Jeffrey C.
- Physical Review Letters, Vol. 120, Issue 14
A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016
- Ward, Logan; Agrawal, Ankit; Choudhary, Alok
- npj Computational Materials, Vol. 2, Issue 1
AiiDA: automated interactive infrastructure and database for computational science
journal, January 2016
- Pizzi, Giovanni; Cepellotti, Andrea; Sabatini, Riccardo
- Computational Materials Science, Vol. 111
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
journal, September 2018
- Tran, Kevin; Ulissi, Zachary W.
- Nature Catalysis, Vol. 1, Issue 9
A high-throughput framework for determining adsorption energies on solid surfaces
journal, March 2017
- Montoya, Joseph H.; Persson, Kristin A.
- npj Computational Materials, Vol. 3, Issue 1
Active Learning
journal, June 2012
- Settles, Burr
- Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 6, Issue 1
Adaptive sequential sampling for surrogate model generation with artificial neural networks
journal, September 2014
- Eason, John; Cremaschi, Selen
- Computers & Chemical Engineering, Vol. 68
Learning surrogate models for simulation-based optimization
journal, March 2014
- Cozad, Alison; Sahinidis, Nikolaos V.; Miller, David C.
- AIChE Journal, Vol. 60, Issue 6
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
Understanding trends in electrochemical carbon dioxide reduction rates
journal, May 2017
- Liu, Xinyan; Xiao, Jianping; Peng, Hongjie
- Nature Communications, Vol. 8, Issue 1
Best Practices for Scientific Computing
journal, January 2014
- Wilson, Greg; Aruliah, D. A.; Brown, C. Titus
- PLoS Biology, Vol. 12, Issue 1
Works referencing / citing this record:
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management
journal, June 2019
- Pendleton, Ian M.; Cattabriga, Gary; Li, Zhi
- MRS Communications, Vol. 9, Issue 3
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
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
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