Large language models for chemists : applications and insights / Zhiling Zheng.

Format:

Book

Author/Creator:

Zheng, Zhiling, Author.

Language:

English

Subjects (All):

Chemistry--Data processing.

Chemistry.

Natural language processing (Computer science)--Scientific applications.

Natural language processing (Computer science).

Artificial intelligence--Scientific applications.

Artificial intelligence.

artificial intelligence.

Physical Description:

136 pages : illustrations (black and white, and colour) ; 24 cm

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2026.

Summary:

"In recent years, LLMs (such as Claude, DeepSeek, Llama and other transformer-based models) have emerged as powerful tools in chemistry, enabling new approaches to scientific discovery. While many chemists, from undergraduate students to researchers find these AI models interesting, they may lack certain knowledge base to better integrate these tools into their daily research. Large Language Models for Chemists breaks down that barrier by demystifying how LLMs work in an accessible way and showing, step-by-step, how they can be applied to solve real chemistry problems. Written in a friendly, tutorial style, the book assumes only a basic background in chemistry and minimal programming experience. It begins by gently introducing artificial intelligence and machine learning concepts in lay terms, building up to the inner workings of LLMs without heavy math. Readers will learn how these models "think" and generate text, gaining an intuitive understanding of concepts like neural networks, transformers, and training data using analogies and simple diagrams. Crucially, each concept is reinforced with chemistry-focused examples - from understanding chemical nomenclature and reactions as a "language," to exploring how an LLM can suggest synthetic routes or explain spectral data. Beyond theory, this book emphasizes practical application. Each chapter includes hands-on tutorials and case studies that invite readers to experiment with real tools. Using open-source libraries (such as RDKit for cheminformatics and standard Python machine learning frameworks), readers will walk through projects like predicting molecular properties with the aid of an LLM, generating novel compound ideas, analyzing research papers, and even using an LLM as a conversational chemistry assistant. For example, one case study guides the reader in using an LLM to mine a chemistry literature database and then write Python code to analyze reaction trends, mirroring cutting-edge research where LLMs assist in code generation and data mining for chemical discovery"-- Provided by publisher.

Contents:

AI's evolving role in chemistry

How to start with data-driven chemistry? Foundations of AI and tools for chemists

Large language models in chemistry

Literature and knowledge mining with LLMs

Generative models for molecule and materials design

LLMs and automation

Ethical considerations and future perspectives.

Notes:

Includes index.

Other Format:

Online version Zheng, Zhiling Large language models for chemists

ebook version :

ISBN:

9781041132790

1041132794

9781041132813

1041132816

OCLC:

1536998906

Publisher Number:

90104345030