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This webinar explored how physics-based simulations and machine learning are helping chemical companies accelerate sustainable innovation while reducing development risk. Attendees learned how a "predict-first" digital chemistry approach can evaluate properties such as solubility, stability, and material compatibility before physical testing begins. The session also highlighted real-world applications of AI-powered modeling and demonstrated how these tools can help organizations speed product development, address complex R&D challenges, and adapt to evolving regulatory and sustainability demands.
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ตอนอื่นๆ ใน PODCAST นี้
Dr. T. Edwin Freeman, recipient of the 2026 Innovation Research Interchange Innovation Leadership Award, has transformed sustainable packaging through advanced materials science, fiber-based alternatives, and systems thinking. From early experiments on his father’s rubber farm in Liberia to leading…
Hosts Meetal Desai and Besher Al‑Maklouf interview Dr. Sumeru Nayak, Winner of the IRI’s Promising Early Career Innovation Professional Award, about his interdisciplinary career, mentorship at James Hardy, and how he applies AI, computer vision, and agile problem‑solving to real‑world construction …
The IRI podcast episode features Jonathan Welch, VP of AI and Machine Learning at Albert, discussing why many R&D organizations stall in their AI journeys and how a strong data foundation and purpose-built AI can unlock decades of trapped scientific knowledge.
Topics include the difference bet…
A central question confronting many innovation leaders today is how R&D systems that are optimized for today’s demands could also explore tomorrow’s opportunities. These opportunities offer significant potential but carry higher risk and must compete with the immediate demands of the day-to-day…
ข้อสงวนสิทธิ์: พอดแคสต์และอาร์ตเวิร์คที่ฝังอยู่ในหน้านี้มาจาก iriweb ซึ่งเป็นทรัพย์สินของเจ้าของและไม่มีส่วนเกี่ยวข้องหรือรับรองโดย Listen Notes, Inc.
