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Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials -- the guiding rules describing how atoms interact -- 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.
Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, 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. The process to establish materials' interatomic potential -- called parameterization -- has required significant chemical and physical intuition, leading to less accurate prediction of new materials design.