BOOK

ORGANIC CHEMISTRY

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Chemical synthesis is critical across life sciences, materials, and energy, yet conventional experimentation remains labor‑intensive. This work introduces Chemma, a fully fine‑tuned large language model trained on 1.28 million Q&A pairs about chemical reactions. Chemma surpasses existing benchmarks in tasks such as single‑step retrosynthesis and yield prediction, demonstrating that general AI can match expert-level understanding without quantum‑chemical data. By integrating Chemma within a Bayesian optimization and active‑learning loop, the authors achieved autonomous reaction planning: a novel Suzuki–Miyaura cross‑coupling was optimized within just 15 experimental runs, yielding 67% of the target product. This study highlights how tailored LLMs can not only predict but also accelerate experimental exploration in organic chemistry.

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Detail Information

Series Title

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Call Number

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Publisher

New York : PEARSON EDUCATIO., 1995

Collation

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Language

English

ISBN/ISSN

0-13-199631-2

Classification

NONE

Content Type

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Media Type

unmediated

Carrier Type

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Edition

FIFTH EDITION

Subject(s)

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Specific Detail Info

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Statement of Responsibility

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