A new computational method for predicting the properties of a wide range of materials has been created by researchers in the US. The algorithm is based on the principles of statistical physics, which the team says allows it to simulate a much wider range of materials than existing techniques. Others, however, point out that the new method is not ideal when the material constituents are too large to be described by statistical physics, or so small that a quantum-mechanical treatment is required.
Computational models allow scientists to predict the properties of new materials before they are actually made – saving time and money in the research and development process. However, these models are often specific to the development project at hand and the algorithms used to fine-tune designs can take a long time to run. As a result, materials scientists are keen to develop fast-running algorithms that can simulate a wide range of candidate materials.
"Gazillion interacting particles"
Now, scientists at the University of Chicago and Cornell University have created an algorithm that could be used to design any system that can be described by statistical physics. Chicago's Heinrich Jaeger describes statistical physics as trying to "describe a gazillion interacting particles without describing every one". Instead, statistical physics describes the likelihood of certain particle configurations at a given set of parameters, such as temperature.
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