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Theoretical and Computational Chemistry

Working Paper

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Sabrina Jaeger, Simone Fulle, Samo Turk

Version 1 posted 23 October 2017

4,974

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2

Citations

Theoretical and Computational Chemistry

Working Paper

Regio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors

Yanfei Guan, Connor Coley, Haoyang wu, Duminda Ranasinghe, esther heid, Thomas J. Struble, Lagnajit Pattanaik, William H. Green, Klavs F. Jensen

Version 1 posted 03 September 2020

1,230

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1

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Catalysis

Working Paper

Automatic Feature Engineering for Catalyst Design Using Small Data without Prior Knowledge of the Target Catalysis

Toshiaki Taniike, Aya Fujiwara, Sunao Nakanowatari, Fernando Escobar, Keisuke Takahashi

Version 1 posted 07 July 2023

424

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1

Citations

Theoretical and Computational Chemistry

Working Paper

The AABBA Graph Kernel: Atom–Atom, Bond–Bond, and Bond–Atom Autocorrelations for Machine Learning

Lucía Morán-González, Jørn Eirik Betten, Hannes Kneiding, David Balcells

Version 2 posted 02 September 2024

564

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Theoretical and Computational Chemistry

Working Paper

REINDEER: A Protein-Ligand Feature Generator Software for Machine Learning Algorithms

Milad Rayka

Version 1 posted 09 April 2024

204

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Theoretical and Computational Chemistry

Review

Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning

Nihal Dadheech

Version 1 posted 29 September 2023

183

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Materials Science

Working Paper

Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd3

Sangjoon Lee, Clio Chen, Griheydi Garcia, Anton Oliynyk

Version 1 posted 03 January 2023

623

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3

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Energy

Review

Battery lifetime predictions: information leakage from unblinded training

Alexis Geslin, Bruis van Vlijmen, Xiao Cui, Arjun Bhargava, Patrick Asinger, Richard Braatz, William Chueh

Version 1 posted 27 March 2023

783

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Theoretical and Computational Chemistry

Working Paper

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks

Han-Chung Chang, Ming-Hsuan Tsai, Yi-Pei Li

Version 1 posted 21 October 2024

112

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Theoretical and Computational Chemistry

Working Paper

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces

Clara Kirkvold, Brianna Collins, Jason Goodpaster

Version 1 posted 22 May 2024

122

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Refine Search

11 results in feature engineering in Keywords: feature engineering

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

Regio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors

Automatic Feature Engineering for Catalyst Design Using Small Data without Prior Knowledge of the Target Catalysis

The AABBA Graph Kernel: Atom–Atom, Bond–Bond, and Bond–Atom Autocorrelations for Machine Learning

REINDEER: A Protein-Ligand Feature Generator Software for Machine Learning Algorithms

Predicting SARS-CoV-2 Protein Interactions: Insights from Machine Learning

Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd3

Battery lifetime predictions: information leakage from unblinded training

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces

11 results in Keywords: feature engineering