Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
Shingo Urata,
Marco Bertani,
Alfonso Pedone
Abstract:The emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT) and classical molecular dynamics (CMD) simulations for computational modeling of materials and estimation of their properties. The MLP offers more efficient computation compared to DFT, while providing higher accuracy compared to CMD. This enables us… Show more
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