Analytical Science Advances / 2021

Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data

Rola Houhou, Thomas Bocklitz

Computer VisionFoundation ModelsReinforcement Learning

Abstract Artificial intelligence‐based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI‐based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

125 citations3 influential

Full paper

Read the original paper

Source page

A direct open-access PDF is not available in the database yet. Use the source page or learning resources below to open the complete paper from the publisher or index.

Learning resources

Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code Semantic Scholar paper pageSemantic Scholar YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data
Authors: Rola Houhou, Thomas Bocklitz
Year: 2021
Venue: Analytical Science Advances
Categories: Computer Vision, Foundation Models, Reinforcement Learning
Citations: 125
Paper URL: https://www.semanticscholar.org/paper/537ef5127be41fd484d9e077e6788f3f6b0f3ede
Open PDF: Not available

Abstract:
Abstract Artificial intelligence‐based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI‐based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Trends%20in%20artificial%20intelligence%2C%20machine%20learning%2C%20and%20chemometrics%20applied%20to%20chemical%20data)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Trends%20in%20artificial%20intelligence%2C%20machine%20learning%2C%20and%20chemometrics%20applied%20to%20chemical%20data)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/537ef5127be41fd484d9e077e6788f3f6b0f3ede)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Trends%20in%20artificial%20intelligence%2C%20machine%20learning%2C%20and%20chemometrics%20applied%20to%20chemical%20data+paper+explained)