Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, and optical detectors. To design such nanomaterials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. Now, researchers at Northwestern University have developed a new framework using machine learning that improves the accuracy of interatomic potentials in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.
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