State-of-the-art machine {learning|studying|understanding|finding out|mastering} (ML) interatomic potentials use {local|nearby|neighborhood|regional} representations of atomic environments {to ensure|to make sure} linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently {related to|associated with} electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel Charge Equilibration (kQEq). This model is {based on|according to|depending on|determined by} classical charge equilibration models like QEq, expanded with an {environment|atmosphere} dependent electronegativity. In contrast to previously reported neural network models {with a|having a|using a} {similar|comparable|equivalent|related} {concept|idea|notion}, kQEq {takes|requires} {advantage|benefit} {of the|from the|in the|on the|with the|of your} linearity of {both|each} QEq and Kernel Ridge Regression to {obtain|acquire|get|receive} a closed-form linear algebra expression for {training|coaching|instruction|education} the models. {Furthermore|Moreover|In addition|Additionally}, we {avoid|steer clear of|stay away from|keep away from|prevent|stay clear of} the ambiguity of charge partitioning schemes {by using|by utilizing} dipole moments as reference {data|information}. As a {first|initial|very first|1st|initially} application, we show that kQEq {can be|may be|could be|might be|is often|is usually} {used|utilized|employed|utilised|applied|made use of} to {generate|produce|create} {accurate|correct|precise} and {highly|extremely|very|hugely} data-efficient models for molecular dipole moments. BuyNOTA-bis(tBu)ester Price of Boc-L-Pyroglutamic acid methyl ester PMID:25147652
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