Prediction of mefenamic acid crystal shape by random forest classification

Research output: Contribution to conferencePoster

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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.
Original languageEnglish
Pages31-31
Number of pages1
Publication statusPublished - 16 May 2022
EventCMAC Annual Open Day 2022 - Glasgow, United Kingdom
Duration: 16 May 202218 May 2022

Conference

ConferenceCMAC Annual Open Day 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period16/05/2218/05/22

Keywords

  • crystal shape prediction
  • machine learning
  • random forest classification

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