Towards net zero by data-driven discovery of sustainable cement alternatives

The construction industry faces mounting pressure to reduce its carbon footprint, with cement production alone contributing over 6% of global greenhouse gas emissions. In recent work, machine learning models screened over 14,000 materials from scientific literature and 1 million rock samples, identifying a diverse set of secondary and natural materials that could partially replace global cement production.

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Publication Details

Journal
Communications Chemistry
Published
2025-07-24
DOI
https://doi.org/10.1038/s42004-025-01608-w
Citations
1
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
1.15
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article

Towards net zero by data-driven discovery of sustainable cement alternatives

Jack D. Evans
1 citations
Communications Chemistry
Reservoir Engineering and Simulation Methods
1.15
article

Towards net zero by data-driven discovery of sustainable cement alternatives

Jack D. Evans
article en
1 citations

Abstract

The construction industry faces mounting pressure to reduce its carbon footprint, with cement production alone contributing over 6% of global greenhouse gas emissions. In recent work, machine learning models screened over 14,000 materials from scientific literature and 1 million rock samples, identifying a diverse set of secondary and natural materials that could partially replace global cement production.

Communications ChemistryVol. 8(1)
The University of Adelaide (AU)
Responsible consumption and production
Openalex Percentile: Top 21%
Reservoir Engineering and Simulation Methods
1.15
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Towards net zero by data-driven discovery of sustainable cement alternatives — Jack D. Evans · Communications Chemistry (2025) | TGRS Research Map | TGRS