Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes. Issue 8 (23rd February 2021)
- Record Type:
- Journal Article
- Title:
- Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes. Issue 8 (23rd February 2021)
- Main Title:
- Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes
- Authors:
- Allers, Joshua P.
Garzon, Fernando H.
Alam, Todd M. - Abstract:
- Abstract : Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. Abstract : Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largestAbstract : Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. Abstract : Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds. … (more)
- Is Part Of:
- Physical chemistry chemical physics. Volume 23:Issue 8(2021)
- Journal:
- Physical chemistry chemical physics
- Issue:
- Volume 23:Issue 8(2021)
- Issue Display:
- Volume 23, Issue 8 (2021)
- Year:
- 2021
- Volume:
- 23
- Issue:
- 8
- Issue Sort Value:
- 2021-0023-0008-0000
- Page Start:
- 4615
- Page End:
- 4623
- Publication Date:
- 2021-02-23
- Subjects:
- Chemistry, Physical and theoretical -- Periodicals
541.3 - Journal URLs:
- http://pubs.rsc.org/en/journals/journalissues/cp#!issueid=cp016040&type=current&issnprint=1463-9076 ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d0cp06693a ↗
- Languages:
- English
- ISSNs:
- 1463-9076
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6475.306000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 18202.xml