A structure-aware graph neural network is trained to predict cross-functional energy residuals and align inconsistent DFT energy scales, which enables reliable predictions of phase stability, battery voltage profiles, and reaction thermodynamics, while allowing the integration of multi-source DFT data to advance the development of high-performance materials foundation models.
Abstract
Heterogeneous density functional theory (DFT) calculations, particularly plane-wave implementations, introduce systematic formation energy errors ranging from tens to hundreds of meV/atom, depending on the selection of exchange-correlation functionals, kinetic energy cutoffs, pseudopotentials, and dispersion corrections. As demonstrated by the MatPES dataset, identical structures can exhibit an average energy discrepancy of 107 meV/atom between PBE and r2SCAN calculations. Such method-dependent discrepancies hinder the integration of multi-source DFT data, greatly limiting the scale and quality of datasets for training robust materials AI models. Here, we resolve this fundamental data silo barrier via graph-based transfer learning. Leveraging 380,190 structurally paired PBE-r2SCAN entries from the MatPES database, we train a structure-aware graph neural network to predict cross-functional energy residuals and align inconsistent DFT energy scales. By adopting GPTFF model architecture, the model converts conventional PBE energies to r2SCAN-level accuracy with a mean absolute error of 14.3 meV/atom, compared with 18.2 meV/atom achieved by CHGNet. This versatile approach effectively upgrades massive legacy PBE datasets to high-precision r2SCAN standards. It enables reliable predictions of phase stability, battery voltage profiles, and reaction thermodynamics, while allowing the integration of multi-source DFT data to advance the development of high-performance materials foundation models.
Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency. In this work, we release OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms. All calculations are carried out at the {\omega}B97X-D/Def2-SVP and Def2-TZVP levels with dispersion corrections, and complete structural optimization trajectories and abundant non-equilibrium structures are recorded. Statistical analyses confirm that this dataset covers a broader conformational space than QM9 in terms of energy, bond lengths and bond angles. A graph neural network-based potential GPTFF-mol is trained using the new dataset, achieving an energy mean absolute error of 16 meV/molecule, which is equivalent to 0.82meV/atom, and superior force prediction performance compared with ANI-2x. Validated by butane rotation and keto-enol tautomerization tests, the model accurately describes molecular dynamical behaviors and reaction barriers at distorted geometries. This work provides a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.
Yifan Huang, Fankai Xie, Jiangnan Zheng et al.· 0 citations
This work demonstrates how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation.
Timo Reents, Marnik Bercx, Giovanni Pizzi· 0 citations
High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction. Graph neural networks (GNNs) enable rapid exploration of materials space but are often limited by the availability of representative training data. Here, we investigate ordered-to-disordered transfer learning using GNNs for formation-energy and HOMO-LUMO gap prediction in HEPOs by transferring knowledge learned from chemically ordered perovskites. Four representative GNN models, including CGCNN, GATGNN, ALIGNN and M3GNet are evaluated to understand the role of structural representations, spanning pairwise two-body and angular three-body interactions in transfer performance. We find strong property-dependent transfer behavior: formation-energy prediction transfers effectively to disordered HEPOs, whereas HOMO-LUMO gap prediction shows limited transferability due to its sensitivity to local chemical environments. Incorporating a small HEPO-specific training dataset substantially improves HOMO-LUMO gap prediction. Representation-level analysis using UMAP further highlights the importance of encoding three-body geometric information such as in ALIGNN for capturing complex structure-property relationships and improving transferability.
Panupol Untarabut, Narjes Jomaa, Sylvian Cadars et al.· 0 citations
Machine learning (ML) techniques have become pivotal in material design, yet accurately capturing the subtle energy shifts associated with local chemical order (LCO) in multi-principal element alloys remains a significant challenge. This study establishes a graph convolutional neural network (GCNN) framework specifically engineered to map the potential energy landscape of NiCoCr medium-entropy alloys with high sensitivity to LCO transitions. Utilizing hybrid Monte-Carlo molecular dynamics simulations as a systematic evaluation benchmark, we evaluate the GCNN’s capacity to learn the non-linear relationship between atomic arrangements and structural stability across varying thermal regimes. The atomic configurations are transformed into graph representations, where nodes incorporate atom types and absolute velocities, and edges are established with the 12 nearest neighbours to encapsulate the local chemical environment. The model’s fitting and predictive fidelity was assessed through three distinct case studies: individual thermal datasets, combined temperature ranges, and unseen configurations. The GCNN demonstrates exceptional performance, achieving coefficient of determination R2 values up to 0.98 and a mean absolute error as low as 1.04 meV atom−1 on entirely withheld thermal trajectories (550 K), effectively tracking the energy variations correlated with the system’s LCO evolution. This research offers a comprehensive graph-based modelling approach for understanding the configuration-to-energy mapping relationships in complex multi-principal element alloys, providing a baseline structural architecture that can be extended toward ML interatomic potential workflows.
Mashaekh Tausif Ehsan, Saifuddin Zafar, Apurba Sarker et al.· Modelling and Simulation in...· 0 citations
Density-functional theory (DFT) has been the workhorse of first-principles calculations for decades, and DFT-derived energies and forces are now widely used to train machine learning models of inter-atomic potentials. However, DFT’s single-particle treatment of exchange-correlation functionals severely limits accuracy for materials with open d- and f-shell elements, and ML models trained on such data inherit this limitation. Dynamical mean-field theory (DMFT) addresses this limitation by explicitly incorporating local electronic correlations, albeit at a significantly higher computational cost. In this work, we develop deep-learning models trained on ab-initio DFT+DMFT calculations to predict electronic self-energies from non-interacting Green’s functions. Using the correlated metal SrVO
3
as a prototype, we show that accurate self-energy predictions can be achieved from small datasets. Through transfer-learning, models pre-trained on SrVO
3
successfully predict the self-energies of CaVO
3
, BaVO
3
and SrNbO
3
, despite differences in composition and electronic structure. Moreover, models pretrained on SrVO
3
and SrNbO
3
can predict self-energy of BaNbO
3
without training on its self-energy. This approach captures temperature variation, extends beyond d
1
perovskites and drastically reduces computational time. These results establish deep-learning as an efficient surrogate for computationally demanding DMFT calculations, enabling rapid prediction of correlation-driven properties, paving the way for a transformative shift in materials theory.
DeGAT enables efficient and accurate prediction of partial atomic charges in MOFs while maintaining charge neutrality and physical consistency, providing a scalable parametrization scheme for high-throughput screening and molecular simulations of porous materials.
Yanhui Sun, Ze-Heng Yu, Yu-Hua Dong et al.· Journal of Chemical Theory a...· 0 citations