Authors: Milan Pejić | Jelena Zagorac | Tamara Škundrić | Matej Fonović | Dejan Zagorac
Source: VinaR (Institute of Nuclear Sciences "Vinča") (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning in Energy Landscape Exploration: ANN-Assisted High- Pressure Polymorph Discovery in LaFS Machine learning (ML) is increasingly used in computational physics, chemistry, and materials science, where it provides fast predictions with accuracy that approaches ab initio calculations. Trained on a relatively modest density functional theory (DFT) dataset, ML models can predict energies, forces, and stresses at a fraction of the cost of DFT calculations, enabling large-scale structure searches and more extensive sampling of energy landscapes. We developed an AI model for energy landscape exploration (AI-ELX) to complement DFT calculations and structure-selection criteria. The model is an artificial neural network (ANN) that, once trained, rapidly filters candidate structures so that computationally expensive DFT calculations can be focused only on the regions of the energy landscape we are interested in. Within the domain spanned by its training data, the model approaches DFT-level accuracy while achieving near empirical potential speed, forming a practical bridge between a large pool of candidates (tens of thousands or more) and targeted DFT validation (which is realistically feasible only for hundreds of structures), including rapid E(V) estimation required for high-pressure searches. Global searches that feed the model, are performed on the LaFS energy landscape using simulated annealing with empirical potentials, across variable settings for periodic cell sizes, pressures, and atomic radii. This stage generates millions of trial structures spanning a large number of space groups. Structure candidates are then symmetry-analyzed and clustered by space group and structure type, after which several hundred representatives undergo ab initio local optimization (DFT in VASP using the PBE exchange-correlation functional). Energy-vs.-volume, E(V), curves are then computed for the most promising modifications to explore their thermodynamic behavior. While this workflow consistently uncovers multiple low-energy modifications, the high-pressure regime, especially at extreme pressures, often remains under-sampled: many plausible structures lie far above the ambient-pressure ground state, and calculating E(V) curves for all of them using DFT is impossible in practice. To close this gap, the AI-ELX model is trained on DFT data for several hundred structures, consisting of tens of thousands of self-consistent single-point calculations. The trained model is then used to predict energies, perform geometry relaxations, and generate E(V) plots for the unscreened pool of structures. These predictions enable enthalpy ranking at target pressures, after which the top candidates are validated by DFT. Model accuracy is highest for volumes, energies, and local configurations similar to the training set, with increasing errors when going outside that domain. This is addressed through cyclic retraining: new DFT results for candidates suggested by the model are added to the training set and the neural network is retrained, iteratively improving precision and expanding the coverage of local environments. Applied to the LaFS crystal system, AI-ELX identified several previously unexplored structures that were subsequently confirmed by DFT calculations to be new ultrahigh-pressure phases, demonstrating that ML-guided exploration can efficiently focus sampling in the high-pressure region and, more broadly, in any targeted region of the energy landscape. Energy landscape ; Simulated annealing ; Ab initio ; Artificial neural network ; Energy (signal processing) ; Complement (music) ; Energy minimization ; Energy consumption Machine Learning in Materials Science ; Crystallography and molecular interactions ; High-pressure geophysics and materials