Webinar 2

Limits of data-driven discovery: Challenges in machine learning for chemistry and materials

Modern R&D increasingly relies on a combination of simulation, data, and machine learning to explore large chemical and materials spaces. These approaches have significantly accelerated discovery, from drug candidates to new materials.

However, as these workflows scale, new challenges emerge. Generating high-quality data can be computationally expensive and difficult to scale, while machine learning models often struggle to capture subtle physical or chemical effects — especially in complex systems.

In practice, this means that even with large datasets and advanced models, important correlations or behaviors may remain difficult to identify, limiting the ability to reliably guide experimental decisions.

This webinar explores how these bottlenecks emerge in real industrial workflows, how teams address them today, and how new computational approaches — including quantum computing — are being explored to complement existing methods.

What this webinar will cover

• Key bottlenecks in data-driven workflows: expensive data generation, limited data quality, and gaps between machine learning models and underlying physics
• How these challenges impact real discovery workflows (e.g. missed correlations, poor generalization, slow iteration cycles)
• How teams are combining simulation, machine learning, and emerging approaches like quantum computing to improve robustness and insight generation

Who is this webinar for

• Chemistry and applied science teams working with simulation, data, and machine learning
• R&D teams scaling data-driven or AI-based discovery workflows
• Technical leaders and decision-makers responsible for computational R&D strategy

Register

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