A new tool that can identify the most efficient building materials in the world is being developed by scientists in the US.
A team led by researchers at Harvard University has developed a way to identify the best materials from a database of around 7,500 materials.
The method is the result of a collaboration between the university’s Centre for Sustainable Materials and the New England Institute of Technology (NEIT).
The researchers used machine learning to identify materials with a high correlation to building performance and a high value for money.
“The process involves extracting data from a data set of 7,700 materials that were selected for a variety of reasons.
We then used machine-learning techniques to extract the most valuable materials from the dataset and create a new tool called the Top 10 List,” the researchers wrote in a statement.
“This tool can be used for a wide variety of projects from building construction to the production of new materials, and we have the opportunity to leverage this research to make the world a more sustainable place.”
The process began when the researchers from the Center for Sustainable and Environmental Materials and NEIT looked at the materials that have been used for many years and tried to predict which materials would be most useful in building.
They found that there were a lot of materials that are extremely high in carbon content, meaning they could be used as building materials.
For example, the material called N-methyl-diethyl-4-propylthiophene (NNMD) is the material used to make polyester.
It is made by making a chemical reaction between nitrogen and water.
But the NMD in question contains just a few carbon atoms.
So the researchers wanted to know if it could be made with a lot more carbon.
They also wanted to find materials that would be best for building.
They chose a metal called tin because it contains a large amount of carbon.
The team found that NMDs made from this material could be the best material for building because of its high carbon content.
However, they found that the best-known building material was steel, which is often used in building products, as well as for cars.
To get the best results, the researchers first needed to determine the best steel materials for building from the data.
To do this, they used a machine learning method that automatically identified the best of the data and used that information to develop a new list of materials.
“We were able to identify and predict the materials most likely to be found in the database, which was the best way to test our predictions,” the team said.
By using the same machine learning techniques, the team was able to predict materials with the highest correlation to performance and the highest value for material.
According to the researchers, this is one of the first time such data has been used to develop an analysis tool for the synthesis of new building materials for the first human.
This research is published in the journal Science Advances.