What role does AI play in material development? In what ways can it make work more efficient?
Tappan: We see it AI a co-pilot that enables product developers, scientists, and engineers to do their jobs better. Materials development problems are highly complex and multi-dimensional, so it's impossible for any human (or team of really smart humans) to evaluate all of the possible solutions and materials that might meet their goals in a reasonable amount of time. With our platform and an AI-guided product development workflow, our customers have accelerated the rate at which they develop new materials by 90+ percent compared to traditional methods.
On top of that, as scientists start to incorporate their expertise into the modeling process, new scientists and adjacent teams can begin to reuse these digital assets to get a jumpstart on new projects and product development initiatives. This both decreases the "ramp-up time" for new projects, and ensures that senior scientists are sharing their knowledge across the team and preventing knowledge loss as veterans begin to retire and new scientists enter the workforce. This domain knowledge can take a number of forms on our Platform from well-structured graphical datasets, "features" or "descriptors" of physical phenomena and known processing relationships that can be used as inputs to a machine learning model, design spaces that define the set of constraints and objectives for specific applications, and reusable AI models.