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Machine learning for renewable energy materials

Journal of materials chemistry. A, Materials for energy and sustainability, 2019, Vol.7 (29), p.1796-17117

2019

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Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions

Nature communications, 2021-07, Vol.12 (1), p.4353-4353, Article 4353

2021

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Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

Advanced materials (Weinheim), 2020-09, Vol.32 (35), p.e1907865-n/a

2020

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Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Nature communications, 2022-04, Vol.13 (1), p.2087-2087, Article 2087

2022

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Guaiacol Hydrodeoxygenation Mechanism on Pt(111): Insights from Density Functional Theory and Linear Free Energy Relations

ChemSusChem, 2015-01, Vol.8 (2), p.315-322

2015

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Perovskite synthesizability using graph neural networks

npj computational materials, 2022-04, Vol.8 (1), p.1-8, Article 71

2022

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Machine-enabled inverse design of inorganic solid materials: promises and challenges

Chemical science (Cambridge), 2020-04, Vol.11 (19), p.4871-4881

2020

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Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

Journal of the American Chemical Society, 2020-11, Vol.142 (44), p.18836-18843

2020

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Generative Adversarial Networks for Crystal Structure Prediction

ACS central science, 2020-08, Vol.6 (8), p.1412-1420

2020

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Highly stable two-dimensional bismuth metal-organic frameworks for efficient electrochemical reduction of CO2

Applied catalysis. B, Environmental, 2020-11, Vol.277, p.119241, Article 119241

2020

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Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation

Chemical science (Cambridge), 2024-01, Vol.15 (3), p.139-145

2024

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Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments

Chemical communications (Cambridge, England), 2019-11, Vol.55 (89), p.13418-13421

2019

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Integrated electrocatalytic synthesis of ammonium nitrate from dilute NO gas on metal organic frameworks-modified gas diffusion electrodes

Nature communications, 2024-08, Vol.15 (1), p.7243-15

2024

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Dual‐Atom‐Site Sn‐Cu/C3N4 Photocatalyst Selectively Produces Formaldehyde from CO2 Reduction

Advanced functional materials, 2023-05, Vol.33 (19), p.n/a

2023

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Predicting potentially hazardous chemical reactions using an explainable neural network

Chemical science (Cambridge), 2021-08, Vol.12 (33), p.1128-1137

2021

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Microkinetic modeling of aqueous phase biomass conversion: Application to ethylene glycol reforming

Chemical engineering science, 2019-04, Vol.197 (C), p.415-418

2019

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Surface Area‐Enhanced Cerium and Sulfur‐Modified Hierarchical Bismuth Oxide Nanosheets for Electrochemical Carbon Dioxide Reduction to Formate

Small (Weinheim an der Bergstrasse, Germany), 2024-10, Vol.20 (40), p.e2400913-n/a

2024

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Tailoring electrode hydrophobicity to improve anode performance in alkaline media

Journal of power sources, 2013-11, Vol.242, p.581-588

2013

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Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection

Reaction chemistry & engineering, 2018-08, Vol.3 (4), p.454-466

2018

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Correction to “Generative Adversarial Networks for Crystal Structure Prediction”

ACS central science, 2022-03, Vol.8 (3), p.402-402

2022

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Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

Journal of chemical information and modeling, 2020-04, Vol.60 (4), p.1996-2003

2020

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Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

The journal of physical chemistry letters, 2020-05, Vol.11 (9), p.3185-3191

2020

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Activity Trends of Methane Oxidation Catalysts under Emission Conditions

ACS catalysis, 2022-08, Vol.12 (16), p.10255-10263

2022

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Reaction Templates: Bridging Synthesis Knowledge and Artificial Intelligence

Accounts of chemical research, 2024-07, Vol.57 (14), p.1964-1972

2024

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Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects

Journal of physical chemistry. C, 2019-08, Vol.123 (31), p.18951-18959

2019

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Machine learning for renewable energy materials

Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions

Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Guaiacol Hydrodeoxygenation Mechanism on Pt(111): Insights from Density Functional Theory and Linear Free Energy Relations

Perovskite synthesizability using graph neural networks

Machine-enabled inverse design of inorganic solid materials: promises and challenges

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

Generative Adversarial Networks for Crystal Structure Prediction

Highly stable two-dimensional bismuth metal-organic frameworks for efficient electrochemical reduction of CO2

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation

Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments

Integrated electrocatalytic synthesis of ammonium nitrate from dilute NO gas on metal organic frameworks-modified gas diffusion electrodes

Dual‐Atom‐Site Sn‐Cu/C3N4 Photocatalyst Selectively Produces Formaldehyde from CO2 Reduction

Predicting potentially hazardous chemical reactions using an explainable neural network

Microkinetic modeling of aqueous phase biomass conversion: Application to ethylene glycol reforming

Surface Area‐Enhanced Cerium and Sulfur‐Modified Hierarchical Bismuth Oxide Nanosheets for Electrochemical Carbon Dioxide Reduction to Formate

Tailoring electrode hydrophobicity to improve anode performance in alkaline media

Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection

Correction to “Generative Adversarial Networks for Crystal Structure Prediction”

Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

Activity Trends of Methane Oxidation Catalysts under Emission Conditions

Reaction Templates: Bridging Synthesis Knowledge and Artificial Intelligence

Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects

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