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Sequential Learning

Iterative machine learning helps you reach your experimental design goals faster

Sequential learning identifies trends in data, identifies good candidates, learns from new data, and allows product developers to maintain decision making authority over the whole process. 

WHAT IT is

BETTER HYPOTHESES, FASTER RESULTS

Too many teams still refer to their own development methodologies as “trial and error.” By integrating data and AI into your teams’ brainstorming, their hypotheses are better informed, leading to fewer expensive experiments and faster time to market.

LEARNING LIKE YOU DO

Every day, your teams learn more about their materials and chemicals. They change their thinking and identify new research directions. But humans, for all of their capabilities, struggle to avoid bias. The Citrine Platform’s sequential learning engine learns as you do, from old data and new, and constantly gets better in the process.

AI IS YOUR PARTNER IN THE LAB

Computers crunch data. Humans find inspiration. Sequential learning takes advantage of both of these, integrating human decision-making with AI-based guidance. AI identifies trends, surfaces patterns, and proposes paths forward. The human scientist provides judgment, makes business decisions, and manages the development process to its ideal result. This partnership leads to faster development times and less frustrated developers.

MORE SEQUENTIAL LEARNING INFORMATION

Julia Ling, Maxwell Hutchinson, Erin Antono, Sean Paradiso, Bryce Meredig . High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates. Integrating Materials and Manufacturing Innovation, 6(3), 207–21 (2017).