Litcius/Paper detail

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

Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, R. Subramanian, Bhargava Urala Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann

2020Wiley Interdisciplinary Reviews Computational Molecular Science64 citationsDOIOpen Access PDF

Abstract

Abstract ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under: Software &gt; Simulation Methods Computer and Information Science &gt; Chemoinformatics Structure and Mechanism &gt; Computational Materials Science Software &gt; Molecular Modeling

Topics & Concepts

CheminformaticsWorkflowComputer scienceInformaticsData scienceContext (archaeology)SoftwareSuiteSoftware engineeringAutomationDomain (mathematical analysis)Key (lock)ToolchainDatabaseChemistryEngineeringProgramming languageComputer securityComputational chemistryBiologyElectrical engineeringHistoryArchaeologyMathematicsPaleontologyMathematical analysisMechanical engineeringMachine Learning in Materials ScienceComputational Drug Discovery Methods