The limitations of classical modeling in complex materials will be the focus of a webinar series beginning in May at 14:00 CET, as QunaSys addresses the point at which simulation capabilities cease to scale effectively. Recognizing the increasing demands placed on industrial research and development, the company will host three standalone sessions examining the boundaries of current computational methods and the potential role of quantum computing. James Pegg of QunaSys will lead a webinar on June 4th at 14:00 CET, exploring where machine learning approaches fall short specifically in chemistry and materials science. A joint presentation by QunaSys’s Ronin Wu and Ricardo Enriquez of Repsol in June at 14:00 CET will then showcase early industry projects actively utilizing quantum computing technologies.
Simulation Scaling Challenges in Materials Modeling
Classical materials modeling techniques are reaching inherent limitations when applied to increasingly complex systems, prompting researchers to investigate alternative computational approaches. This scaling challenge arises because the computational demands of accurately representing material behavior grow exponentially with system size and complexity, hindering the ability to predict properties and design novel materials. While data-driven discovery offers significant advantages, its reliance on existing data and inability to extrapolate beyond known parameters limits truly innovative material design. Industry collaboration is now focused on quantum computing as a potential solution, with Repsol partnering with QunaSys to showcase early projects.
Each webinar is designed as a standalone event, requiring no prior knowledge of quantum computing, reflecting a broader effort to assess the practical implications of emerging technologies.
ML is powerful – but where does it fall short in chemistry and materials?
James Pegg (QunaSys)
