Educating Future Chemists in the Age of AI: A Digital Chemistry Course

Artificial intelligence (AI) and machine learning (ML) are developing fast and are increasingly adopted in both chemical industry and academic research. With the projected role such tools will play in the future, for every chemist, these developments call for a fundamental and sound education for future generations of scientists in these areas. In this perspective, we describe the development of the course Digital Chemistry at ETH Zurich, which addresses these topics. In particular, we outline our approach to teaching ML and its applications in chemistry. We especially emphasize that the skills of understanding, applying and critically assessing ML models will be fundamental for future chemists. We hope that this article will serve as inspiration for educators in this field and help to enhance the teaching in this area of future significance.

Authors

Institutions

Publication Details

Journal
CHIMIA International Journal for Chemistry
Published
2025-10-29
DOI
https://doi.org/10.2533/chimia.2025.689
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Educating Future Chemists in the Age of AI: A Digital Chemistry Course

Stefan P. Schmid, Kjell Jorner, Lauriane Jacot‐Descombes
CHIMIA International Journal for Chemistry
Machine Learning in Materials Science
article

Educating Future Chemists in the Age of AI: A Digital Chemistry Course

Stefan P. Schmid, Kjell Jorner, Lauriane Jacot‐Descombes
article en

Abstract

Artificial intelligence (AI) and machine learning (ML) are developing fast and are increasingly adopted in both chemical industry and academic research. With the projected role such tools will play in the future, for every chemist, these developments call for a fundamental and sound education for future generations of scientists in these areas. In this perspective, we describe the development of the course Digital Chemistry at ETH Zurich, which addresses these topics. In particular, we outline our approach to teaching ML and its applications in chemistry. We especially emphasize that the skills of understanding, applying and critically assessing ML models will be fundamental for future chemists. We hope that this article will serve as inspiration for educators in this field and help to enhance the teaching in this area of future significance.

CHIMIA International Journal for ChemistryVol. 79(10)
ETH Zurich (CH), NCCR Chemical Biology - Visualisation and Control of Biological Processes Using Chemistry (CH)
Openalex Percentile: Top 75%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.