Understanding Where AI Adds Value in R&D: Accelerating Materials Discovery
Analyst
AI in Materials R&D: Where It Delivers Value Today
AI has become one of the most dominant themes globally, but the gap between aspirations and practical value remains wide. For innovation leaders, the challenge is not just tracking new tools but prioritizing where AI can genuinely improve performance and recognizing where expectations remain ahead of reality. Lux’s AI Value Framework identifies where AI adds knowledge and automation value and where it remains more promise than proof, helping companies make informed decisions about where to focus time and resources.
This webinar examines how AI is being applied across materials discovery and development for polymers, batteries, CCU, hydrogen, and ammonia, with a focus on how AI can overcome bottlenecks, support automation and deeper insight, and where limitations still matter and why claims of fully autonomous “AI scientists” remain highly speculative.
Watch This Webinar Now Or At A Later Time By Registering Above!
Eine Kopie der Präsentationsfolien und der Aufzeichnung des Webinars wird allen Teilnehmern nach dem Webinar zugesandt.
Wenn Sie Fragen haben, senden Sie bitte eine E-Mail an webinars@luxresearchinc.com.
Understanding Where AI Adds Value in R&D: Accelerating Materials Discovery
Analyst
Sehen Sie sich das On-Demand-Webinar an
AI has become one of the most dominant themes globally, but the gap between aspirations and practical value remains wide. For innovation leaders, the challenge is not just tracking new tools but prioritizing where AI can genuinely improve performance and recognizing where expectations remain ahead of reality. Lux’s AI Value Framework identifies where AI adds knowledge and automation value and where it remains more promise than proof, helping companies make informed decisions about where to focus time and resources.
This webinar examines how AI is being applied across materials discovery and development for polymers, batteries, CCU, hydrogen, and ammonia, with a focus on how AI can overcome bottlenecks, support automation and deeper insight, and where limitations still matter and why claims of fully autonomous “AI scientists” remain highly speculative.
