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

A Dual-Mode Crystal Structure Prediction Framework for Fixed Element Sets and Fixed Structural Prototypes

Crystal structure prediction (CSP) is a cornerstone technology for the efficient discovery and rational design of functional materials. Here, we propose a deep-learning-enabled dual-mode CSP framework that simultaneously supports two complementary tasks: predicting stable crystal structures for given elemental compositions and identifying chemically viable elemental substitutions for a predefined crystal topology. The framework employs an improved normalized structural fingerprint descriptor that integrates local coordination topology without global spatial-scale information. A cascaded site-probability model, composed of an autoencoder and sigmoid-based classifiers, is developed to predict the occupancy probabilities of 84 chemical elements at crystallographically distinct sites and to efficiently rank candidate structures accordingly. Remarkably, even when trained solely on topological information, the model autonomously captures elemental chemical similarity and intrinsic periodic trends, yielding chemically meaningful multielement probability distributions. Application of the proposed framework to high-throughput screening of superhard materials in the B–N system successfully identifies a thermodynamically, mechanically, and dynamically stable hexagonal BN phase, together with a metastable monoclinic B2N3 structure. Furthermore, elemental substitution screening on the zinc-blende prototype reveals two metastable compounds, In4Sb4 and Ga4Sb4. The proposed framework provides a low-cost, high-efficiency, data-driven strategy for the rapid discovery of inorganic materials.

The use of machine learning interatomic potentials for the verification of experimental molecular crystal structures.

This study detected anomalies in experimental structures that had already passed all prior validation, as well as limitations in the reliability of the MLIP PES calculations, and similarity descriptors were calculated to quantify the differences between the original and optimized structures.

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

Packora is presented, a flow-based generative model for molecular CSP that jointly predicts atomic coordinates and the lattice from molecular graphs that outperforms the baselines on both structure generation and ranking benchmarks.

Neural network model for prediction of crystal structures

A century of structures: historical fidelity and computational fitness in the Inorganic Crystal Structure Database (ICSD).

This work assesses 464 ICSD structures from 1913 to 1929 using two independent tools: Eir v1.4.2 (bond valence sums, global instability index) and MaplePy (Madelung part of lattice energy).

Machine Learning-Driven Refinement of Reactive Force Fields via Hierarchical “Center-Environment” Features for Energetic Molecular Crystals

This work not only establishes a pioneering paradigm for interpretable ML-driven force field refinement but also provides the first feature engineering solution incorporating chemical, physical, and structural information specifically designed for the machine learning of energetic molecular crystals.