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In this work, we propose a linear machine learning force matching approachthat can directly extract pair atomic interactions from ab initio calculationsin amorphous structures. The local feature representation is specificallychosen to make the linear weights a force field as a force/potential functionof the atom pair distance. Consequently, this set of functions is the closestrepresentation of the ab initio forces given the two-body approximation andfinite scanning in the configurational space. We validate this approach inamorphous silica. Potentials in the new force field (consisting of tabulatedSi-Si, Si-O, and O-O potentials) are significantly softer than existingpotentials that are commonly used for silica, even though all of them producethe tetrahedral network structure and roughly similar glass properties. Thissuggests that those commonly used classical force fields do not offerfundamentally accurate representations of the atomic interaction in silica. Thenew force field furthermore produces a lower glass transition temperature($T_g\sim$1800 K) and a positive liquid thermal expansion coefficient,suggesting the extraordinarily high $T_g$ and negative liquid thermal expansionof simulated silica could be artifacts of previously developed classicalpotentials. Overall, the proposed approach provides a fundamental yet intuitiveway to evaluate two-body potentials against ab initio calculations, therebyoffering an efficient way to guide the development of classical force fields.

How close are the classical two-body potentials to ab initio calculations? Insights from linear machine learning based force matching