Abstract
Research findings on Machine Learning are discussed in a new report. According to news reporting from Madison, Wisconsin, by NewsRx journalists, research stated, "In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance." The news correspondents obtained a quote from the research from the University of Wisconsin Madison, "Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si-Si, Si-O, and O-O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (T-g similar to 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high T-g and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials."
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