scholarly journals Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

2018 ◽  
Vol 122 (49) ◽  
pp. 28142-28150 ◽  
Author(s):  
Asif J. Chowdhury ◽  
Wenqiang Yang ◽  
Eric Walker ◽  
Osman Mamun ◽  
Andreas Heyden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Species ◽  
Metal Catalyst ◽  
Adsorption Energies ◽  
Catalyst Surfaces

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies

2021 ◽  
Author(s):  
Saientan Bag ◽  
Manuel Konrad ◽  
Tobias Schlöder ◽  
Pascal Friederich ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Adsorption Energies

scholarly journals Understanding Potential-dependent Competition Between Electrocatalytic Dinitrogen and Proton Reduction Reactions

2021 ◽  
Author(s):  
Changhyeok Choi ◽  
Geun Ho Gu ◽  
Juhwan Noh ◽  
Hyun S. Park ◽  
Yousung Jung
Keyword(s):  
Charge Transfer ◽  
Heterogeneous Catalysts ◽  
Early Stage ◽  
Side Reaction ◽  
Potential Method ◽  
Reaction Rates ◽  
Reduction Reaction ◽  
Metal Catalyst ◽  
Proton Reduction ◽  
Catalyst Surfaces

Abstract One of the key challenges to practical electrochemical N2 reduction reaction (NRR) is to lower the overpotential and suppression of the side reaction known as the hydrogen evolution reaction (HER) during the NRR. The experimental NRR activity has been consistently shown to reach a maximum at early stage before reaching the mass-transfer limit and decreases with large overpotentials for many heterogeneous catalysts. Though the volcano-type current-potential relationship shown for NRR is unusual with limited reaction rates at higher overpotentials, the mechanistic origin has not been clearly explained, making the design principles for practical NRR still lacking. Herein, we investigate the potential-dependent reaction activity of NRR and HER using the constant electrode potential method and microkinetic modeling. It manifests the dominating proton adsorption over dinitrogen at small overpotentials leading to the inadequate reaction selectivity towards NRR at many metal catalyst surfaces. A clear potential-dependent competition between the N2 adsorption and *H formation is characterized by the degree of charge transfer in the adsorption process. It is also demonstrated that the larger charge transfer in *H formation is a general phenomenon applied to all heterogeneous catalyst surfaces considered here, that poses a fundamental challenge to realize practical electrochemical NRR. We suggest several strategies to overcome the latter challenges based on the present understandings.

scholarly journals Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins

2021 ◽  
Vol 118 (11) ◽  
pp. e2024666118
Author(s):  
Tao Wang ◽  
Guomin Li ◽  
Xinjiang Cui ◽  
Frank Abild-Pedersen
Keyword(s):  
Machine Learning ◽  
Noble Metal ◽  
Ethylene Production ◽  
Large Scale ◽  
Light Alkanes ◽  
Main Obstacle ◽  
Metal Free ◽  
Ethane Dehydrogenation ◽  
Earth Abundant ◽  
Adsorption Energies

Selective ethane dehydrogenation (EDH) is an attractive on-purpose strategy for industrial ethylene production. Design of an effective, stable, and earth-abundant catalyst to replace noble metal Pt is the main obstacle for its large-scale application. Herein, we report an experimentally validated theoretical framework to discover promising catalysts for EDH, which combines descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments. Our approach efficiently evaluates 1,998 bimetallic alloys by using accurately calculated C and CH3 adsorption energies and identifies a small number of new promising noble-metal–free catalysts for selective EDH. A Ni3Mo alloy predicted to be promising is successfully synthesized, and experimentally proven to outperform Pt in selective ethylene production from EDH, representing an important contribution to the improvement of light alkane dehydrogenation to olefins. These results will provide essential additions in the discovery and application of earth-abundant materials in catalysis.

scholarly journals Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

2021 ◽  
Author(s):  
Sheena Agarwal ◽  
Kavita Joshi
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Density Functional ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Face Centered Cubic ◽  
Functional Theory ◽  
Full Picture ◽  
Adsorption Energies ◽  
Face Centered

Abstract<br>Identifying factors that influence interactions at the surface is still an active area of research. In this study, we present the importance of analyzing bondlength activation, while interpreting Density Functional Theory (DFT) results, as yet another crucial indicator for catalytic activity. We studied the<br>adsorption of small molecules, such as O 2 , N 2 , CO, and CO 2 , on seven face-centered cubic (fcc) transition metal surfaces (M = Ag, Au, Cu, Ir, Rh, Pt, and Pd) and their commonly studied facets (100, 110, and 111). Through our DFT investigations, we highlight the absence of linear correlation between adsorption energies (E ads ) and bondlength activation (BL act ). Our study indicates the importance of evaluating both to develop a better understanding of adsorption at surfaces. We also developed a Machine Learning (ML) model trained on simple periodic table properties to predict both, E ads and BL act . Our ML model gives an accuracy of Mean Absolute Error (MAE) ∼ 0.2 eV for E ads predictions and 0.02 Å for BL act predictions. The systematic study of the ML features<br>that affect E ads and BL act further reinforces the importance of looking beyond adsorption energies to get a full picture of surface interactions with DFT.<br>

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

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