94,19 €
125,59 €
Kaina su kodu: ENG
AI Revolution in Chemistry
AI Revolution in Chemistry
94,19 €
125,59 €
  • Planuojame turėti už 142 d.
AI Revolution in Chemistry is a practical, vendor-neutral field guide for chemists who want to use AI with confidence. It demystifies core methods--machine learning, deep learning, generative models, and digital twins--and shows how they create value from bench to plant: reaction prediction and synthesis planning, autonomous experimentation, materials design, process control (including PAT) and quality. Readers get durable principles and checklists for data, validation and scale-up; plain-Engli…
  • Kaina galioja įvedus kodą: ENG

AI Revolution in Chemistry (el. knyga) (skaityta knyga) | knygos.lt

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AI Revolution in Chemistry is a practical, vendor-neutral field guide for chemists who want to use AI with confidence. It demystifies core methods--machine learning, deep learning, generative models, and digital twins--and shows how they create value from bench to plant: reaction prediction and synthesis planning, autonomous experimentation, materials design, process control (including PAT) and quality. Readers get durable principles and checklists for data, validation and scale-up; plain-English guidance on ethics, bias and regulation; and case-style examples that make clear where AI helps, where it fails and how to course-correct. Written for practising chemists (not data scientists), it keeps chemical intuition at the centre while providing just enough AI to be dangerous--in the best way. The result is a concise, actionable primer that stays relevant as tools evolve, helping teams design, pilot and scale projects across discovery, development and GMP.

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Kaina galioja įvedus kodą: ENG

94,19 €
125,59 €
Planuojame turėti už 142 d.

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AI Revolution in Chemistry is a practical, vendor-neutral field guide for chemists who want to use AI with confidence. It demystifies core methods--machine learning, deep learning, generative models, and digital twins--and shows how they create value from bench to plant: reaction prediction and synthesis planning, autonomous experimentation, materials design, process control (including PAT) and quality. Readers get durable principles and checklists for data, validation and scale-up; plain-English guidance on ethics, bias and regulation; and case-style examples that make clear where AI helps, where it fails and how to course-correct. Written for practising chemists (not data scientists), it keeps chemical intuition at the centre while providing just enough AI to be dangerous--in the best way. The result is a concise, actionable primer that stays relevant as tools evolve, helping teams design, pilot and scale projects across discovery, development and GMP.

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