Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview-Reference-Cited by-同舟云学术

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Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Published:2024-01-29 Issue:3 Volume:64 Page:597-620
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Nicolle Andre¹^ORCID,Deng Sili²^ORCID,Ihme Matthias³,Kuzhagaliyeva Nursulu⁴,Ibrahim Emad Al⁴^ORCID,Farooq Aamir⁴^ORCID

Affiliation:

1. Aramco Fuel Research Center, Rueil-Malmaison 92852, France

2. Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States

3. Stanford University, Stanford 94305, California, United States

4. King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia

Funder

Clean Combustion Research Center, King Abdullah University of Science and Technology

Saudi Aramco

Basic Energy Sciences

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c01633

Reference425 articles.

1. Inverse design of porous materials using artificial neural networks

2. Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

3. Mixing and the Formation of Homoeomers in on Generation and Corruption 2. 7

4. Developing the Mereology of Chemistry

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1. Bridging odorants and olfactory perception through machine learning: A review;Trends in Food Science & Technology;2024-11

2. VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing;ACS Omega;2024-09-04

3. Deep Learning for Gas Sensing via Infrared Spectroscopy;Sensors;2024-03-14

4. Artificial intelligence as a catalyst for combustion science and engineering;Proceedings of the Combustion Institute;2024

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