Prediction of mefenamic acid crystal shape by random forest classification

Siya Nakapraves; Monika Warzecha; Chantal Mustoe; Alastair J. Florence

Prediction of mefenamic acid crystal shape by random forest classification

Siya Nakapraves, Monika Warzecha, Chantal Mustoe, Alastair J. Florence

Research output: Contribution to conference › Poster

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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 language	English
Pages	31-31
Number of pages	1
Publication status	Published - 16 May 2022
Event	CMAC Annual Open Day 2022 - Glasgow, United Kingdom Duration: 16 May 2022 → 18 May 2022

Conference

Conference	CMAC Annual Open Day 2022
Country/Territory	United Kingdom
City	Glasgow
Period	16/05/22 → 18/05/22

Keywords

crystal shape prediction
machine learning
random forest classification

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Nakapraves-etal-CMAC-2022-Prediction-of-mefenamic-acid-crystal-shapeFinal published version, 1.24 MBLicence: Strath_1

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title = "Prediction of mefenamic acid crystal shape by random forest classification",

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.",

keywords = "crystal shape prediction, machine learning, random forest classification",

author = "Siya Nakapraves and Monika Warzecha and Chantal Mustoe and Florence, {Alastair J.}",

year = "2022",

month = may,

day = "16",

language = "English",

pages = "31--31",

note = "CMAC Annual Open Day 2022 ; Conference date: 16-05-2022 Through 18-05-2022",

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T1 - Prediction of mefenamic acid crystal shape by random forest classification

AU - Nakapraves, Siya

AU - Warzecha, Monika

AU - Mustoe, Chantal

AU - Florence, Alastair J.

PY - 2022/5/16

Y1 - 2022/5/16

N2 - 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.

AB - 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.

KW - crystal shape prediction

KW - machine learning

KW - random forest classification

M3 - Poster

SP - 31

EP - 31

T2 - CMAC Annual Open Day 2022

Y2 - 16 May 2022 through 18 May 2022

ER -

Prediction of mefenamic acid crystal shape by random forest classification

Abstract

Conference

Keywords

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