MRS Meetings and Events


DS03.10.10 2023 MRS Fall Meeting

Inverse Design of Low-Carbon Concrete Materials by Machine Learning

When and Where

Dec 1, 2023
11:45am - 12:00pm

Hynes, Level 2, Room 206



Kai Yang1,Mathieu Bauchy1

University of California, Los Angeles1


Kai Yang1,Mathieu Bauchy1

University of California, Los Angeles1
Concrete—which is by far the most manufactured material in the world—is responsible for 5-to-10% of human CO<sub>2</sub> emissions. Here, we present an uncertainty-aware, machine-learning-enabled optimization scheme that aims to accelerate the discovery of new optimized concrete mixes featuring minimum embodied CO<sub>2</sub> while meeting target performance and manufacturing constraints. We curate an unprecedented dataset comprising more than 1 million concrete mixtures with varying mixture proportions, together with their measured properties (compressive strength, slump, shrinkage, setting time, etc.). The dataset is used to train a series of Gaussian Process regression (GPR) forward models that accurately map concretes’ mixture proportions to their properties, and uncertainty thereof. We then introduce a new inverse design approach that simultaneously leverages (i) multi-property predictions from the GPR model, (ii) uncertainty thereof, and (iii) physical knowledge to guide the discovery of new concrete mixtures featuring minimum embodied CO<sub>2</sub> while presenting required performance metrics (e.g., with a strength meeting or exceeding a given target) and obeying manufacturing constraints (e.g., with compliant slump, pumpability, finishability, etc.). This pipeline leads to the discovery of several new concrete mixtures presenting a &gt;50% decrease in embodied CO<sub>2</sub>, with no cost increase.



Symposium Organizers

James Chapman, Boston University
Victor Fung, Georgia Institute of Technology
Prashun Gorai, National Renewable Energy Laboratory
Qian Yang, University of Connecticut

Symposium Support

Elsevier B.V.

Publishing Alliance

MRS publishes with Springer Nature