Informing Empirically Fitted Density Functionals about the Physics of Interelectronic Interactions.

Further progress in constructing highly accurate density functionals by enforcing known laws of interelectron interactions is slow, so fitting techniques are usually employed nowadays. These approaches were shown to lead to overfitting when a functional becomes unreliable for properties on which it was not trained on. An approach to maintain the correct physical behavior of a functional during its training is required to build more complex and accurate functionals, including those based on neural networks.

Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design.

Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields.

Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem".

Kirkpatrick . (Reports, 9 December 2021, p. 1385) trained a neural network-based DFT functional, DM21, on fractional-charge (FC) and fractional-spin (FS) systems, and they claim that it has outstanding accuracy for chemical systems exhibiting strong correlation.