Researchers into materials and chemicals for construction applications seek to improve properties such as compressive strength or corrosion-resistance. And, with the cement industry alone accounting for over 4% of global carbon emissions, they want to lower energy consumption in support of net zero goals. These researchers need to understand and exploit often subtle, hidden relationships between key properties and the ingredients, structure, and processing of materials and chemicals.
Machine learning is an AI approach that should help by analysing experimental or process data to build models that can capture these relationships and use them to fine-tune performance. The problem is that such data is often sparse and noisy, causing machine learning methods to fail. Alchemite™ is an innovative method that overcomes this challenge. In this webinar, we’ll draw on experience of real-world projects to demonstrate how Alchemite™ can be applied to applications including design of new concrete formulations and development of steels and other alloys for structural applications.
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