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Efficient crystal structure prediction using universal neural network potential with diversity preservation in genetic algorithms

This study integrates a universal NNP with a GA-based CSP method, which integrates a universal NNP designed to expand convex hull volumes while preserving the diversity of crystal structures, and demonstrates the validity of PFP across a wide range of crystal structures and element combinations.

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SALTED: a symmetry-adapted machine-learning program for predicting electron-densities in molecules and materials

SALTED provides an open-source Python package for machine learning the quantum-mechanical electron density in molecular and condensed-phase systems based on input atomic coordinates and species, based on a linear atom-centered decomposition of the electron density.

Zekun Lou, Alan M. Lewis, T. Bernhard et al. · 0 citations
Open access Aug 2026

Resolution-standardized evaluation of ligand atomic coordinates in crystallographic structures using machine learning

The aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery and provide a foundation for further development and refinement of machine learning-based coordinate validation.

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Machine learning prediction of organic compound melting points informed by condensed-phase and electronic descriptors.

Melting point (MP) is an important thermophysical property for the chemical process industry, yet accurate prediction of MP for organic compounds in the absence of experimental data remains challenging due to the complex interplay between molecular packing, intermolecular interactions, and electronic structure. Traditi...

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Improved Learning of Molecular Energetics Through an Electron-Wise Joint Charge Density and Energy Objective

We present a graph neural network-based learning framework trained to jointly predict electron densities and molecular energies. The model is trained to predict electron densities calculated with a Generalized Gradient Approximation (GGA) functional while simultaneously learning to predict molecular energies obtained w...

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OrbGNN: A Wave function-based Machine Learning Interelectronic Representation

OrbGNN is presented, an electronic structure graph architecture analogous to molecular graph and MLIP frameworks, where pair-orbital interactions constitute the graph representation, while orbital entanglement encodes the connectivity between them.

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