The global chemical industry—spanning consumer goods, specialty materials, and industrial chemicals—is navigating a massive transition. Driven by stringent regulatory shifts and a global demand for sustainable, high-transparency ingredient portfolios, companies are under pressure to innovate at unprecedented speeds. However, replacing established synthetic chemicals or ingredients with bio-based or green alternatives often introduces complex challenges regarding stability, performance consistency, and material compatibility.
This webinar explores how a “predict-first” digital chemistry platform can mitigate these risks by shifting the discovery from the laboratory alone to a high-throughput computational environment.
Central to this digital transformation is the synergy between physics-based simulations and machine learning (ML). While traditional ML often struggles with the data sparsity typical of novel chemicals and complex mixtures, physics-based methods generate high-fidelity, molecular-level descriptors that provide the “ground truth” for ingredient interactions. These simulations allow R&D teams to characterize key properties—such as solubility, phase behavior, rheology, and chemical stability—of complex, multi-component systems before a single physical sample is synthesized.
Key Highlights:
Solving the Data Gap: Discover how physics-informed AI overcomes the limitations of small datasets, allowing for the design of innovative chemicals where historical experimental data is non-existent
Real-World Case Studies: Showcase how modern industries utilize physics-based modeling and ML to accelerate product development and solve complex R&D challenges
A Scalable Foundation for Industrial R&D: Learn how to improve R&D velocity and meet evolving global regulatory demands in an increasingly volatile market
Jeffrey Sanders
