Correction to "Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity".
- DOI
- 10.1021/acsomega.6c05887
- Published
- 2026 Sep 15
- Container
- ACS omega
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1021/acsomega.6c05887,
title = {Correction to "Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity".},
author = {Tin N and Chauhan M and Agwamba K and Sun Y and Parsons A and Payne P and Osan R},
year = {2026},
journal = {ACS omega},
doi = {10.1021/acsomega.6c05887},
url = {https://doi.org/10.1021/acsomega.6c05887}
}RIS
TY - JOUR TI - Correction to "Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity". AU - Tin N AU - Chauhan M AU - Agwamba K AU - Sun Y AU - Parsons A AU - Payne P AU - Osan R PY - 2026 JO - ACS omega DO - 10.1021/acsomega.6c05887 UR - https://doi.org/10.1021/acsomega.6c05887 ER -
APA
N, T., M, C., K, A., Y, S., A, P., P, P., & R, O. (2026). Correction to "Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity".. ACS omega. https://doi.org/10.1021/acsomega.6c05887
Source records
- pubmed · retrieved 2026-09-26T04:24:28.235Z