ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

doi:10.1002/wcms.1458

This content will become publicly available on January 29, 2021

Title: ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Authors:

^[1]; Vishwakarma, Gaurav ^[1]; Altarawy, Doaa ^[2]; Subramanian, Ramachandran ^[3]; Kota, Bhargava U. ^[3]; Sonpal, Aditya ^[1]; Setlur, Srirangaraj ^[4];

^[5]

Department of Chemical and Biological EngineeringUniversity at Buffalo, The State University of New York Buffalo New York
The Molecular Sciences Software Institute, Virginia Tech Blacksburg Virginia, Computer and Systems Engineering DepartmentAlexandria University Alexandria Egypt
Department of Computer Science and EngineeringUniversity at Buffalo, The State University of New York Buffalo New York, Center for Unified Biometrics and SensorsUniversity at Buffalo, The State University of New York Buffalo New York
Department of Computer Science and EngineeringUniversity at Buffalo, The State University of New York Buffalo New York, Center for Unified Biometrics and SensorsUniversity at Buffalo, The State University of New York Buffalo New York, Center of Excellence for Document Analysis and Recognition, University at BuffaloThe State University of New York Buffalo New York
Department of Chemical and Biological EngineeringUniversity at Buffalo, The State University of New York Buffalo New York, Computational and Data‐Enabled Science and Engineering Graduate ProgramUniversity at Buffalo, The State University of New York Buffalo New York, New York State Center of Excellence in Materials Informatics Buffalo New York

Publication Date:: 2020-01-30

Sponsoring Org.:: USDOE

OSTI Identifier:: 1596162

Grant/Contract Number:: DE‐SC0017193

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Wiley Interdisciplinary Reviews: Computational Molecular Science

Additional Journal Information:: Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science; Journal ID: ISSN 1759-0876

Publisher:: Wiley Blackwell (John Wiley & Sons)

Country of Publication:: United States

Language:: English

Citation Formats


                    Haghighatlari, Mojtaba, Vishwakarma, Gaurav, Altarawy, Doaa, Subramanian, Ramachandran, Kota, Bhargava U., Sonpal, Aditya, Setlur, Srirangaraj, and Hachmann, Johannes. ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data.  United States: N. p., 2020. 
        Web.  doi:10.1002/wcms.1458.

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                    Haghighatlari, Mojtaba, Vishwakarma, Gaurav, Altarawy, Doaa, Subramanian, Ramachandran, Kota, Bhargava U., Sonpal, Aditya, Setlur, Srirangaraj, & Hachmann, Johannes. ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data.  United States.  doi:10.1002/wcms.1458.

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                    Haghighatlari, Mojtaba, Vishwakarma, Gaurav, Altarawy, Doaa, Subramanian, Ramachandran, Kota, Bhargava U., Sonpal, Aditya, Setlur, Srirangaraj, and Hachmann, Johannes. Thu .  
        "ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data".  United States.  doi:10.1002/wcms.1458.

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@article{osti_1596162,

  title        = {ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data},

  author       = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava U. and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},

  abstractNote = {},

  doi          = {10.1002/wcms.1458},

  journal      = {Wiley Interdisciplinary Reviews: Computational Molecular Science},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {2020},

  month        = {1}

}

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DOI: 10.1002/wcms.1458

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Similar Records

This content will become publicly available on January 29, 2021

Title: ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Citation Formats

Machine learning methods in chemoinformatics: Machine learning methods in chemoinformatics journal, February 2014

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning journal, January 2012

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining journal, May 2019

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Deep Generative Models for Molecular Science journal, January 2018

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Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers journal, January 2019

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
journal, May 2019

Inverse molecular design using machine learning: Generative models for matter engineering
journal, July 2018

Advances of machine learning in molecular modeling and simulation
journal, March 2019

Open Babel: An open chemical toolbox
journal, October 2011

Learning molecular energies using localized graph kernels
journal, March 2017

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
journal, August 2011

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers
journal, June 2018

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015

Deep Generative Models for Molecular Science
journal, January 2018

Constant size descriptors for accurate machine learning models of molecular properties
journal, June 2018

Advances and challenges in deep generative models for de novo molecule generation
journal, October 2018

Active Learning
journal, June 2012

Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics
journal, January 2011

The Harvard organic photovoltaic dataset
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A Survey on Transfer Learning
journal, October 2010

Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project
journal, January 2014

Quantum-chemical insights from deep tensor neural networks
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Batch Mode Active Learning for Regression With Expected Model Change
journal, July 2017

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space
journal, April 2018

A general-purpose machine learning framework for predicting properties of inorganic materials
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Universal fragment descriptors for predicting properties of inorganic crystals
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