Catalyst Design by Machine Learning and Multiobjective Optimization

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

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Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

Physical Review Accelerators and Beams ◽

10.1103/physrevaccelbeams.23.044601 ◽

2020 ◽

Vol 23 (4) ◽

Cited By ~ 2

Author(s):

Auralee Edelen ◽

Nicole Neveu ◽

Matthias Frey ◽

Yannick Huber ◽

Christopher Mayes ◽

...

Keyword(s):

Machine Learning ◽

Multiobjective Optimization ◽

Particle Accelerator

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Feature engineering of machine-learning chemisorption models for catalyst design

Catalysis Today ◽

10.1016/j.cattod.2016.04.013 ◽

2017 ◽

Vol 280 ◽

pp. 232-238 ◽

Cited By ~ 67

Author(s):

Zheng Li ◽

Xianfeng Ma ◽

Hongliang Xin

Keyword(s):

Machine Learning ◽

Catalyst Design ◽

Feature Engineering

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Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.9b04173 ◽

2019 ◽

Vol 58 (44) ◽

pp. 20412-20422 ◽

Cited By ~ 8

Author(s):

Sai Gokul Subraveti ◽

Zukui Li ◽

Vinay Prasad ◽

Arvind Rajendran

Keyword(s):

Machine Learning ◽

Multiobjective Optimization ◽

Pressure Swing Adsorption ◽

Pressure Swing

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Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization

10.26434/chemrxiv.12578552.v1 ◽

2020 ◽

Author(s):

Steven Maley ◽

Doo-Hyun Kwon ◽

Nick Rollins ◽

Johnathan Stanley ◽

Orson Sydora ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Transition State ◽

Data Science ◽

Catalyst Design ◽

Ethylene Oligomerization ◽

State Model ◽

General Strategy ◽

Design Features ◽

Molecular Catalyst

The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene<br>

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Molecular Dynamics and Machine Learning in Catalysts

Catalysts ◽

10.3390/catal11091129 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1129

Author(s):

Wenxiang Liu ◽

Yang Zhu ◽

Yongqiang Wu ◽

Cen Chen ◽

Yang Hong ◽

...

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Large Scale ◽

Reaction Force ◽

Catalyst Design ◽

Computer Hardware ◽

Ab Initio Molecular Dynamics ◽

Oxidation Reactions ◽

Recent Developments ◽

Promising Development

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation

10.26434/chemrxiv.8182025.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aditya Nandy ◽

Jiazhou Zhu ◽

Jon Paul Janet ◽

Chenru Duan ◽

Rachel Getman ◽

...

Keyword(s):

Machine Learning ◽

Spin State ◽

Density Functional ◽

Intermediate Formation ◽

Catalyst Design ◽

Structure Property ◽

Design Rules ◽

Energy Prediction ◽

Formation Energies ◽

Rules And Exceptions

<p>Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing <i>d</i>-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.</p>

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Science ◽

10.1126/science.aau5631 ◽

2019 ◽

Vol 363 (6424) ◽

pp. eaau5631 ◽

Cited By ~ 66

Author(s):

Andrew F. Zahrt ◽

Jeremy J. Henle ◽

Brennan T. Rose ◽

Yang Wang ◽

William T. Darrow ◽

...

Keyword(s):

Machine Learning ◽

Catalyst Design ◽

Machine Learning Algorithms ◽

Support Vector ◽

Asymmetric Reaction ◽

Stage Of Development ◽

Chiral Phosphoric Acid ◽

Vector Machines ◽

Feed Forward Neural Networks ◽

Selection Of

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid–catalyzed thiol addition toN-acylimines.

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