{"id":1145950,"date":"2025-07-30T07:03:10","date_gmt":"2025-07-30T14:03:10","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=1145950"},"modified":"2025-07-30T07:03:10","modified_gmt":"2025-07-30T14:03:10","slug":"closed-loop-optimization-using-machine-learning-for-the-accelerated-design-of-sustainable-cements-incorporating-algal-biomatter","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/closed-loop-optimization-using-machine-learning-for-the-accelerated-design-of-sustainable-cements-incorporating-algal-biomatter\/","title":{"rendered":"Closed-loop optimization using machine learning for the accelerated design of sustainable cements incorporating algal biomatter"},"content":{"rendered":"<p>The substantial embodied carbon of cement, coupled with the ever-increasing need for construction materials, motivates the need for more sustainable cementitious materials. An emerging strategy to mitigate CO<sub>2<\/sub>\u00a0emissions involves incorporating carbon-negative biomatter; however, this introduces new challenges due to complex hydration-strength relationships and the combinatorial design space. Here, using machine learning, we develop a closed-loop optimization strategy to accelerate green-cement design with minimal CO<sub>2<\/sub>\u00a0emissions while meeting compressive-strength criterion. Green cements incorporating algae are tested in real time to predict strength evolution, with early-stopping criteria applied to accelerate the optimization process. This approach, using only 28 days of experiment time, attains both the strength requirement and 93% of the achievable improvement in global warming potential (GWP), resulting in a cement that has a 21% reduction in GWP. We further validate model-informed relationships via analysis of hydration, demonstrating the potential for developing materials grounded in scientific understanding.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The substantial embodied carbon of cement, coupled with the ever-increasing need for construction materials, motivates the need for more sustainable cementitious materials. An emerging strategy to mitigate CO2\u00a0emissions involves incorporating carbon-negative biomatter; however, this introduces new challenges due to complex hydration-strength relationships and the combinatorial design space. Here, using machine learning, we develop a closed-loop 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