Incorporate AI into Your Chemistry Curriculum with the ACS In Focus AI & Machine Learning Collection

Aug 20, 2025
1 min read

Modernize your chemistry curriculum with concise, expert-written primers that introduce chemists at all levels to the latest AI trends in research.

A collection of ACS in Focus titles, including Machine Learning in Chemistry; Machine Learning in Materials Science; and Machine Learning for Drug Discovery

The future of chemistry is increasingly shaped by AI. To modernize your curriculum and ensure your students are ready for the next-generation chemistry research, we're excited to introduce the new ACS In Focus AI & Machine Learning Collection.

The Al & Machine Learning Collection includes 12 ACS In Focus digital primers. These 3- to 6-hour reads, each written in non-specialist language by experts in the field, illuminate topics such as Python, machine learning (ML), neural networks, and quantum computing. These resources offer foundational knowledge and practical applications of trending AI technologies in chemistry, making them the perfect starter kit for students and indispensable references for chemists of all levels and branches.

For seamless institutional access for both students and faculty members, recommend the collection to your library via this form. We'll reach out to your institution’s librarians with one-time purchase options.

In the meantime, browse the list of titles included in this collection below.

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Programming and Modeling Graph Data
Machine Learning
Neural Networks
Quantum Computing

Programming and Modeling Graph Data

Python for Chemists

Python for Chemists is the perfect first read to help your department's budding chemists identify problems in their research where code may automate operations for a large volume of data or calculations. The authors deftly shorten the time from learning to doing by introducing meaningful problem-sets in Chapter One.

Authors: Kiyoto Aramis TanemuraDiego Sierra-CostaKenneth M. Merz Jr.

Graph Data Modeling: Molecules, Proteins, & Chemical Processes

Graph Data Modeling: Molecules, Proteins, & Chemical Processes serves as an introduction to graphs as a mathematical object in the context of chemical data and how we can utilize learning algorithms, specifically graph neural networks, to construct systems that operate on graphs. By mastering the concepts presented here, you'll be prepared to contribute to the next generation of chemical breakthroughs.

Authors: José Manuel Barraza-Chavez, Rana A. Barghout, Ricardo Almada-Monter, Benjamin Sanchez-Lengeling, Adrian Jinich, and Radhakrishnan Mahadevan

Machine Learning

Machine Learning in Chemistry

Get up to speed on machine learning (ML)—a must-know topic for the next generation of chemists—with Machine Learning in Chemistry. This digital primer delivers both depth and clarity by focusing on the concepts that matter most to the chemical sciences. You'll explore the fundamentals and limitations of ML, along with its promising applications in chemistry today and in the future.

Authors: Jon Paul Janet and Heather J. Kulik

Machine Learning in Materials Science

Machine Learning in Materials Science provides the fundamentals and useful insight into where machine learning (ML) will have the greatest impact for the materials science researcher.

Authors: Keith T. Butler, Felipe Oviedo, and Pieremanuele Canepa

Machine Learning for Drug Discovery

Machine Learning for Drug Discovery is designed to suit the needs of graduate students, advanced undergraduates, chemists or biologists otherwise new to this research domain with minimal previous exposure to machine learning (ML) methods, or computational scientists with minimal exposure to medicinal chemistry.

Authors: Marcelo C.R. Melo, Jacqueline R. M. A. Maasch, and Cesar de la Fuente-Nunez

Machine Learning for Polymer Informatics

Machine Learning for Polymer Informatics introduces the reader to the most popular ways of applying machine learning in polymer informatics. It prepares readers to ask the right questions about the application of machine learning in their areas of interest, as well as critically interpret publications leveraging machine learning methods.

Authors: Ying Li and Tianle Yue

Molecular Representations for Machine Learning

This primer helps readers understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, readers will be able to use various methods to generate machine and/or human interpretable representations of molecular systems for inputs to machine learning models or for general chemical data science applications.

Authors: Grier M. Jones, Brittany Story, Vasileios Maroulas, and Konstantinos D. Vogiatzis

Neural Networks

Neural Networks for Chemists

This primer is designed as your first step toward understanding neural networks. The authors guide readers from the fundamentals of neural networks to advanced architectures, starting with core building blocks and fully connected networks. Through real-world case studies and a focus on representation learning, they reveal how these tools drive scientific breakthroughs and transform industries like healthcare, finance, and more.

Authors: Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, and Haixu Tang

Neural Networks in the Physical Sciences

This primer gives readers a strong foundation in artificial intelligence, beginning with essential concepts and terminology. It then builds toward a deeper understanding of neural network architectures and the mathematics behind them. With four real-world examples drawn from the authors’ diverse expertise, readers see how theory becomes practice—and how neural networks are driving innovation across science and technology.

Authors: Ian Bentley and Marwan Gebran

Modeling Polymers with Neural Networks

This primer explains at a fundamental level how machine learning models are created, trained, and evaluated while focusing specifically on applications in polymer informatics. The authors introduce techniques suited to polymer data, providing a foundational understanding that the reader can use as a launchpad for more advanced methods. Additionally, to serve as an interactive aid for learning within this primer, the authors have also provided tutorials at the end of each chapter.

Authors: Eric Inae, Yuhan Liu, Yihan Zhu, Jiaxin Xu, Gang Liu, Renzheng Zhang, Tengfei Luo, and Meng Jiang

Quantum Computing

Quantum Computing for Quantum Chemistry

Quantum Computing for Quantum Chemistry offers a clear and accessible introduction to the emerging intersection of quantum chemistry and quantum computing. It provides a solid foundation in core quantum chemistry concepts while showing how quantum computing can tackle complex chemical challenges. With straightforward language and practical examples, this book is an ideal starting point for beginners exploring this fast-evolving interdisciplinary field.

Authors: Philipp Schleich, Luis Mantilla Calderón, Chong Sun, Mohsen Bagherimehrab, Abdulrahman Aldossary, Jakob S. Kottmann, and Alán Aspuru-Guzik

QM/MM Methods

QM/MM Methods offers researchers a strong starting point in the field by focusing on the core principles of QM/MM methodology and how to interpret application results. Foundational concepts are clearly explained, with guidance on how to extend them to larger model systems. The book also points readers to key literature for deeper exploration. By the end, readers will be equipped to ask insightful questions, choose appropriate algorithms, and troubleshoot effectively in their own research.

Author: Hai Lin

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