Artificial intelligence (AI) and machine learning (ML) are no longer futuristic technologies in chemical R&D.
While their first use was for productivity gains in business units, scientists are making inroads in AI/ML adoption.
Organisations are using AI/ML to accelerate experimentation and analysis across the design-make-test-analyse (DMTA) cycle.
The greatest value is emerging as AI is embedded into digital-physical workflows, where continuously captured and contextualised data enables adaptive modelling that enhances — rather than replaces — scientific judgement.
Emily Letton speaks to ACD/Labs’ Dr Sanji Bhal for further insight.
From experiments to “experimental stories”
The power of AI and machine learning (AI/ML) rests on data quality, accessibility and continuity.
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