2016/08/26 by Teppei Suzuki, Ryo Tamura, Tsuyoshi Miyazaki · 2 citations
Chemistry · Materials Science · Physics and Astronomy · #Advanced Physical and Chemical Molecular Interactions #Construct (python library) #Force Microscopy Techniques and Applications #Kernel (algebra) #Machine Learning in Materials Science #Range (aeronautics) #Ridge #Support vector machine #Transferability #cond-mat.mtrl-sci
paper · pdf · doi:10.1002/qua.25307
published as International Journal of Quantum Chemistry 117, 33 (2017) · 20pages, 5 figures
arxiv created 2016/08/26 · openalex created_date 2016/09/16 · openalex publication_date 2016/10/24 · arxiv updated 2018/03/08 · openalex updated_date 2026/08/05
Abstract Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum‐mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine‐learning model on a crystalline silicon system to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine‐learning model using a quantum‐mechanical dataset taken from canonical‐ensemble simulations at a higher temperature, or an upper bound of the temperature range. With our model, the force prediction errors were about 2% or smaller with respect to the corresponding force ranges, in the temperature region between 300 K and 1650 K. We also verified the applicability to a larger system, ensuring the transferability with respect to system size.