Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

Analysis of CH4 Uptake over Metal–Organic Frameworks Using Data-Mining Tools

(2019) Zeynep Gülsoy et al.   ACS Combinatorial Science

Machine Learning-Aided Structure Determination for TiCl4–Capped MgCl2 Nanoplate of Heterogeneous Ziegler–Natta Catalyst

(2019) Gentoku Takasao et al.   ACS Catalysis

Beyond Scaling Relations for the Description of Catalytic Materials

(2019) Mie Andersen et al.   ACS Catalysis

A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction

(2019) Roman Schmack et al.   Nature Communications

Machine Learning Prediction of H Adsorption Energies on Ag Alloys

(2019) Robert A. Hoyt et al.   Journal of Chemical Information and Modeling

Machine Learning for Computational Heterogeneous Catalysis

(2019) Philomena Schlexer Lamoureux et al.   ChemCatChem

Application of support vector machine on controlling the silanol groups of silica xerogel with the aid of segmented continuous flow reactor

(2019) Chuan Wang et al.   CHEMICAL ENGINEERING SCIENCE

A catalyst selection method for hydrogen production through Water-Gas Shift Reaction using artificial neural networks

(2019) Fábio Machado Cavalcanti et al.   JOURNAL OF ENVIRONMENTAL MANAGEMENT

Deep learning for vibrational spectral analysis: Recent progress and a practical guide

(2019) Jie Yang et al.   ANALYTICA CHIMICA ACTA

Selection rules for estimating the solubility of C4-hydrocarbons in imidazolium ionic liquids determined by machine-learning tools

(2019) Ahsan Jalal et al.   JOURNAL OF MOLECULAR LIQUIDS

Atomistic structure learning

(2019) Mathias S. Jørgensen et al.   JOURNAL OF CHEMICAL PHYSICS

Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning

(2019) Yang Liu et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

(2019) Takashi Toyao et al.   ACS Catalysis

Machine learning for heterogeneous catalyst design and discovery

(2018) Bryan R. Goldsmith et al.   AICHE JOURNAL

Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis

(2018) Ayşe Neslihan Şener et al.   CATALYSIS TODAY

Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data

(2018) Keisuke Takahashi et al.   ChemCatChem

Text Mining Metal–Organic Framework Papers

(2018) Sanghoon Park et al.   Journal of Chemical Information and Modeling

Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys

(2018) Takashi Toyao et al.   Journal of Physical Chemistry C

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

(2018) Juhwan Noh et al.   Chemical Science

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

(2018) Benjamin Meyer et al.   Chemical Science

Computer-aided discovery of a metal–organic framework with superior oxygen uptake

(2018) Peyman Z. Moghadam et al.   Nature Communications

Extracting Knowledge from Data through Catalysis Informatics

(2018) Andrew J. Medford et al.   ACS Catalysis

Artificial neural networks with response surface methodology for optimization of selective CO 2 hydrogenation using K-promoted iron catalyst in a microchannel reactor

(2018) Yong Sun et al.   Journal of CO2 Utilization

An open experimental database for exploring inorganic materials

(2018) Andriy Zakutayev et al.   Scientific Data

Towards operando computational modeling in heterogeneous catalysis

(2018) Lukáš Grajciar et al.   CHEMICAL SOCIETY REVIEWS

Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations

(2018) Yuping He et al.   Journal of Physical Chemistry Letters

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Decision tree analysis for efficient CO2 utilization in electrochemical systems

(2018) M. Erdem Günay et al.   Journal of CO2 Utilization

The Rise of Catalyst Informatics: Towards Catalyst Genomics

(2018) Keisuke Takahashi et al.   ChemCatChem

Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning

(2018) Keisuke Takahashi et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

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

(2018) Asif J. Chowdhury et al.   Journal of Physical Chemistry C

From Process to Properties: Correlating Synthesis Conditions and Structural Disorder of Platinum Nanocatalysts

(2018) Baichuan Sun et al.   Journal of Physical Chemistry C

Metal–Organic Frameworks in Solid–Gas Phase Catalysis

(2018) Cheng Wang et al.   ACS Catalysis

Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools

(2018) Çağla Odabaşı et al.   Nano Energy

Synthesis, characterization and machine learning based performance prediction of straw activated carbon

(2018) Wen Jiang et al.   JOURNAL OF CLEANER PRODUCTION

Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production

(2018) Elif Can et al.   APPLIED CATALYSIS B-ENVIRONMENTAL

Kinetics of photocatalytic, self-cleaning surfaces: A decision tree approach for determination of reaction order

(2018) David Ollis   APPLIED CATALYSIS B-ENVIRONMENTAL

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical review

(2017) Meltem Baysal et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Deep learning for computational chemistry

(2017) Garrett B. Goh et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme

(2017) Ryosuke Jinnouchi et al.   Journal of Physical Chemistry C

Uncovering structure-property relationships of materials by subgroup discovery

(2017) Bryan R Goldsmith et al.   NEW JOURNAL OF PHYSICS

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

(2017) Zachary W. Ulissi et al.   ACS Catalysis

Using data mining technology in screening potential additives to Ni/Al2O3 catalysts for methanation

(2017) Xiaoxia Han et al.   Catalysis Science & Technology

High-throughput screening of bimetallic catalysts enabled by machine learning

(2017) Zheng Li et al.   Journal of Materials Chemistry A

Application of Artificial Neural Networks for Catalysis: A Review

(2017) Hao Li et al.   Catalysts

Machine-learned and codified synthesis parameters of oxide materials

(2017) Edward Kim et al.   Scientific Data

Data science: Accelerating innovation and discovery in chemical engineering

(2016) David A. C. Beck et al.   AICHE JOURNAL

Analysis of past experimental data in literature to determine conditions for high performance in biodiesel production

(2016) N. Alper Tapan et al.   Biofuels Bioproducts & Biorefining-Biofpr

Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells

(2016) N. Alper Tapan et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Artificial neural network modeling of hydrogen-rich syngas production from methane dry reforming over novel Ni/CaFe2O4 catalysts

(2016) M. Anwar Hossain et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

(2016) Sumyea Helal   JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Machine learning bandgaps of double perovskites

(2016) G. Pilania et al.   Scientific Reports

Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

(2015) Wesley T. Hong et al.   Journal of Physical Chemistry C

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data

(2015) Evgenii V. Kondratenko et al.   Catalysis Science & Technology

Towards computational design of zeolite catalysts for CO2 reduction

(2015) A. W. Thornton et al.   RSC Advances

Toward Practical Application Of H2Generation From Ammonia Decomposition Guided by Rational Catalyst Design

(2014) Enrique García-Bordejé et al.   CATALYSIS REVIEWS-SCIENCE AND ENGINEERING

High-throughput computational screening of metal–organic frameworks

(2014) Yamil J. Colón et al.   CHEMICAL SOCIETY REVIEWS

Review on Biodiesel Production from Various Feedstocks Using 12-Tungstophosphoric Acid (TPA) as a Solid Acid Catalyst Precursor

(2014) Chinmoy Baroi et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012

(2014) Çağla Odabaşı et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

(2014) Michael Fernandez et al.   Journal of Physical Chemistry Letters

Designing rules and probabilistic weighting for fast materials discovery in the Perovskite structure

(2014) I E Castelli et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Data mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells

(2014) Jacqueline M. Cole et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks

(2013) M. Erdem Günay et al.   APPLIED CATALYSIS A-GENERAL

Knowledge Extraction from Catalysis of the Past: A Case of Selective CO Oxidation over Noble Metal Catalysts between 2000 and 2012

(2013) M. Erdem Günay et al.   ChemCatChem

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

(2013) Colin L. Bird et al.   CHEMICAL SOCIETY REVIEWS

Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks

(2013) Michael Fernandez et al.   Journal of Physical Chemistry C

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Modeling preferential CO oxidation over promoted Au/Al2O3 catalysts using decision trees and modular neural networks

(2012) M. Erdem Günay et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

A new weighted optimal combination of ANNs for catalyst design and reactor operation: Methane steam reforming studies

(2011) Viswanathan Arcotumapathy et al.   AICHE JOURNAL

CatApp: A Web Application for Surface Chemistry and Heterogeneous Catalysis

(2011) Jens S. Hummelshøj et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High-Performance Catalysts

(2011) Ulyana Zavyalova et al.   ChemCatChem

Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

(2011) M. Erdem Günay et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Investigation of water gas-shift activity of Pt–MOx–CeO2/Al2O3 (M = K, Ni, Co) using modular artificial neural networks

(2011) M. Erdem Günay et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Analysis of O2 Adsorption Stability and Strength Over Gold Clusters Using DFT and Logistic Regression

(2011) M. Erdem Günay et al.   JOURNAL OF CLUSTER SCIENCE

Merging traditional and high-throughput approaches results in efficient design, synthesis and screening of catalysts for an industrial process

(2010) Laurent A. Baumes et al.   APPLIED CATALYSIS A-GENERAL

Analysis of selective CO oxidation over promoted Pt/Al2O3 catalysts using modular neural networks: Combining preparation and operational variables

(2010) M. Erdem Günay et al.   APPLIED CATALYSIS A-GENERAL

Quaternary mixture designs applied to the development of multi-element oxygen electrocatalysts based on the Ln0.58Sr0.4Fe0.8Co0.2O3−δ system (Ln=La1−x−y−zPrxSmyBaz): Predictive modeling approaches

(2010) José M. Serra et al.   CATALYSIS TODAY

Predictive modeling in homogeneous catalysis: a tutorial

(2010) Ana G. Maldonado et al.   CHEMICAL SOCIETY REVIEWS

Screening of Additives to a Co/SrCO3Catalyst by Artificial Neural Network for Preferential Oxidation of CO in Excess H2

(2010) Yasukazu Kobayashi et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Evidence-Based Design and Optimisation of Titania Photocatalysts via Artificial Neural Network Analysis

(2010) Dean Chesterfield et al.   JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

Structure and activity relationship for CO and O2 adsorption over gold nanoparticles using density functional theory and artificial neural networks

(2010) Tuğba Davran-Candan et al.   JOURNAL OF CHEMICAL PHYSICS

An overview on subgroup discovery: foundations and applications

(2010) Franciso Herrera et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Machine Learning in Virtual Screening

(2009) James Melville et al.   COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING

Visualization of Large Experimental Space Using Holographic Mapping and Artificial Neural Networks. Benchmark Analysis of Multicomponent Catalysts for the Water Gas Shift Reaction

(2009) András Tompos et al.   TOPICS IN CATALYSIS

Development of a high performance Cu-based ternary oxide catalyst for oxidative steam reforming of methanol using an artificial neural network

(2008) Tetsuo Umegaki et al.   APPLIED CATALYSIS A-GENERAL

Data mining in catalysis: Separating knowledge from garbage

(2008) G ROTHENBERG   CATALYSIS TODAY

New catalytic materials for the high-temperature synthesis of hydrocyanic acid from methane and ammonia by high-throughput approach

(2007) S. Moehmel et al.   APPLIED CATALYSIS A-GENERAL

Neural network aided design of Pt-Co-Ce/Al2O3 catalyst for selective CO oxidation in hydrogen-rich streams

(2007) M. Erdem Günay et al.   CHEMICAL ENGINEERING JOURNAL