Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces
出版年份 2020 全文链接
标题
Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces
作者
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出版物
ChemCatChem
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2020-06-04
DOI
10.1002/cctc.202000517
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Beyond Scaling Relations for the Description of Catalytic Materials
- (2019) Mie Andersen et al. ACS Catalysis
- CO methanation on ruthenium flat and stepped surfaces: Key role of H-transfers and entropy revealed by ab initio molecular dynamics
- (2019) Lucas Foppa et al. JOURNAL OF CATALYSIS
- Machine Learning for Computational Heterogeneous Catalysis
- (2019) Philomena Schlexer Lamoureux et al. ChemCatChem
- Data Mining the C−C Cross‐Coupling Genome
- (2019) Boodsarin Sawatlon et al. ChemCatChem
- What Can We Learn from First Principles Multi-Scale Models in Catalysis? The Role of the Ni/Al2O3 Interface in Water-Gas Shift and Dry Reforming as a Case Study
- (2019) Lucas Foppa et al. CHIMIA
- High-throughput calculations of catalytic properties of bimetallic alloy surfaces
- (2019) Osman Mamun et al. Scientific Data
- CO2 Hydrogenation on Cu/Al2O3: Role of Metal/Support Interface in Driving Activity and Selectivity of a Bifunctional Catalyst
- (2019) Christophe Copéret et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Facile Fischer–Tropsch Chain Growth from CH2 Monomers Enabled by the Dynamic CO Adlayer
- (2019) Lucas Foppa et al. ACS Catalysis
- A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions
- (2019) Adrian Jinich et al. ACS Central Science
- Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
- (2019) Volker L. Deringer et al. ADVANCED MATERIALS
- Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
- (2019) Rodrigo García-Muelas et al. Nature Communications
- Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors
- (2019) Zhi-Jian Zhao et al. Nature Reviews Materials
- Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
- (2018) Benjamin Meyer et al. Chemical Science
- Adlayer Dynamics Drives CO Activation in Ru-Catalyzed Fischer–Tropsch Synthesis
- (2018) Lucas Foppa et al. ACS Catalysis
- The Matter Simulation (R)evolution
- (2018) Alán Aspuru-Guzik et al. ACS Central Science
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- Contrasting the Role of Ni/Al2O3 Interfaces in Water–Gas Shift and Dry Reforming of Methane
- (2017) Lucas Foppa et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Addressing uncertainty in atomistic machine learning
- (2017) Andrew A. Peterson et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- To address surface reaction network complexity using scaling relations machine learning and DFT calculations
- (2017) Zachary W. Ulissi et al. Nature Communications
- 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
- Machine learning unifies the modeling of materials and molecules
- (2017) Albert P. Bartók et al. Science Advances
- Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats
- (2017) Luca M. Ghiringhelli et al. npj Computational Materials
- Intrinsic reactivity of Ni, Pd and Pt surfaces in dry reforming and competitive reactions: Insights from first principles calculations and microkinetic modeling simulations
- (2016) Lucas Foppa et al. JOURNAL OF CATALYSIS
- Establishing and Understanding Adsorption–Energy Scaling Relations with Negative Slopes
- (2016) Hai-Yan Su et al. Journal of Physical Chemistry Letters
- Increased Back-Bonding Explains Step-Edge Reactivity and Particle Size Effect for CO Activation on Ru Nanoparticles
- (2016) Lucas Foppa et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers
- (2015) Federico Calle-Vallejo et al. Nature Chemistry
- Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors
- (2015) F. Calle-Vallejo et al. SCIENCE
- Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers
- (2014) Federico Calle-Vallejo et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform
- (2014) M. Álvarez-Moreno et al. Journal of Chemical Information and Modeling
- Mechanism and microkinetics of the Fischer–Tropsch reaction
- (2013) R. A. van Santen et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Physical and Chemical Nature of the Scaling Relations between Adsorption Energies of Atoms on Metal Surfaces
- (2012) F. Calle-Vallejo et al. PHYSICAL REVIEW LETTERS
- Density functional theory in surface chemistry and catalysis
- (2011) J. K. Norskov et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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