Prediction of mefenamic acid crystal shape by random forest classification

Nakapraves, Siya and Warzecha, Monika and Mustoe, Chantal and Florence, Alastair J. (2022) Prediction of mefenamic acid crystal shape by random forest classification. In: CMAC Annual Open Day 2022, 2022-05-16 - 2022-05-18.

Preview

Text. Filename: Nakapraves_etal_CMAC_2022_Prediction_of_mefenamic_acid_crystal_shape.pdf Final Published Version License: Strathprints license 1.0 Download (1MB)| Preview

Abstract

Research problem: Crystal shape is one of the key attributes affecting the bulk particle properties of a crystalline material as well as its downstream manufacturability1. However, the prediction of experimental crystal shapes remains very challenging. This research aims to explore the potential application of machine learning algorithms to solve this problem.

Persistent Identifier

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Citations and altmetrics
• Item metadata

  Item type:	Conference or Workshop Item(Poster)
  ID code:	81754
  Dates:	Date Event 16 May 2022 Published
  Subjects:	Medicine > Therapeutics. Pharmacology
  Department:	Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences Strategic Research Themes > Advanced Manufacturing and Materials Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)
  Depositing user:	Pure Administrator
  Date deposited:	08 Aug 2022 10:41
  Last modified:	09 Apr 2024 06:58
  URI:	https://strathprints.strath.ac.uk/id/eprint/81754