Large Language Models for Chemists: Applications and Insights
暫譯: 化學家的大型語言模型：應用與洞見

Zheng, Zhiling

Large Language Models for Chemists: Applications and Insights
  • 出版商: CRC
  • 出版日期: 2026-02-18
  • 售價: $10,450
  • 貴賓價: 9.5 折 $9,928
  • 語言: 英文
  • 頁數: 124
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041132794
  • ISBN-13: 9781041132790
  • 相關分類: Large language model、Python

    • 海外代購書籍(需單獨結帳)

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商品描述

In recent years, LLMs (such as ChatGPT, 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 a 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. It spans 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.

商品描述(中文翻譯)

在近幾年，LLMs（如 ChatGPT、Claude、DeepSeek、LLaMA 及其他基於變壓器的模型）已成為化學領域中強大的工具，促進了科學發現的新方法。雖然許多化學家，從本科生到研究人員，都對這些 AI 模型感到興趣，但他們可能缺乏某些知識基礎，以便更好地將這些工具整合到日常研究中。

《化學家的大型語言模型》打破了這一障礙，以易於理解的方式揭示 LLM 的運作原理，並逐步展示如何應用它們來解決實際的化學問題。這本書以友好的教學風格撰寫，僅假設讀者具備基本的化學背景和最少的程式設計經驗。它首先以通俗的語言輕鬆介紹人工智慧和機器學習的概念，逐步深入 LLM 的內部運作，而不涉及繁重的數學。讀者將學習這些模型如何「思考」和生成文本，並通過類比和簡單的圖示獲得對神經網絡、變壓器和訓練數據等概念的直觀理解。至關重要的是，每個概念都以專注於化學的例子進行強化。內容涵蓋了將化學命名法和反應理解為一種「語言」，到探索 LLM 如何建議合成路徑或解釋光譜數據。

除了理論之外，本書強調實際應用。每一章都包含實作教程和案例研究，邀請讀者使用真實工具進行實驗。利用開源庫（如 RDKit 用於化學資訊學和標準的 Python 機器學習框架），讀者將逐步完成如利用 LLM 預測分子性質、生成新化合物想法、分析研究論文，甚至使用 LLM 作為對話式化學助手的專案。例如，一個案例研究指導讀者使用 LLM 挖掘化學文獻數據庫，然後編寫 Python 代碼來分析反應趨勢，這反映了 LLM 在化學發現中協助代碼生成和數據挖掘的前沿研究。

作者簡介

Zhiling "Zach" Zheng is an Assistant Professor of Chemistry at Washington University in St. Louis, where he directs the Deep Synthesis Lab. His group combines artificial intelligence and automation to accelerate the discovery of porous materials for sustainability and human health. In addition to investigating fundamental aspects of metal-organic framework (MOF) synthesis and new structures, he explores how large language models can aid data mining, reaction and material design, and synthesis planning.

Before joining WashU, Dr. Zheng was a BIDMaP Fellow at UC Berkeley's Department of Electrical Engineering and Computer Sciences from 2024 to 2025 and a postdoctoral researcher in the MIT Department of Chemical Engineering from 2023 to 2024 under the supervision of Professor Klavs Jensen. He earned his Ph.D. in Chemistry at the University of California, Berkeley, in 2023, working in Professor Omar Yaghi's laboratory on MOFs for atmospheric water harvesting. He holds a B.A. in Chemistry, summa cum laude, from Cornell University (2019), where he worked with Professor Kyle Lancaster.

Dr. Zheng's contributions have been recognized with the 2025 Carbon Future Young Investigator Award and the Inflection Award for AI-driven climate solutions. He was also a finalist for the Dream Chemistry Award.

作者簡介(中文翻譯)

Zhiling 'Zach' Zheng 是聖路易斯華盛頓大學化學系的助理教授，並且負責深度合成實驗室。他的研究團隊結合人工智慧和自動化技術，以加速可持續性和人類健康的多孔材料的發現。除了研究金屬有機框架（MOF）合成的基本方面和新結構外，他還探索大型語言模型如何協助數據挖掘、反應和材料設計以及合成規劃。

在加入華盛頓大學之前，鄭博士於2024年至2025年擔任加州大學伯克利分校電機工程與計算機科學系的BIDMaP研究員，並於2023年至2024年在麻省理工學院化學工程系擔任博士後研究員，指導教授為Klavs Jensen教授。他於2023年在加州大學伯克利分校獲得化學博士學位，並在Omar Yaghi教授的實驗室研究用於大氣水收集的MOF。他於2019年以優異成績（summa cum laude）獲得康奈爾大學化學學士學位，並與Kyle Lancaster教授合作。

鄭博士的貢獻獲得了2025年碳未來青年研究者獎和AI驅動氣候解決方案的Inflection獎。他還是夢想化學獎的決賽入圍者。

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