arXiv:2609.00059v1 Announce Type: cross Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled...
arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
The paper introduces a benchmark to evaluate compositional generalisation in machine learning interatomic potentials. It comprises four tasks that test models on unseen molecules, designed so that learning underlying chemical principles should enable generalisation. Experiments show that current state‑of‑the‑art models struggle, with out‑of‑distribution errors often an order of magnitude higher than in‑distribution errors, even for foundation models pre‑trained on millions of molecules.
arXiv:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.
arXiv:2604. 14562v2 Announce Type: replace Abstract: Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship.
arXiv:2606. 08148v1 Announce Type: cross Abstract: Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness.
The study shows that electronic structure datasets used for machine learning contain significant redundancy due to low intrinsic dimensionality. Random pruning and a coverage-based strategy can reduce dataset size by up to two orders of magnitude while preserving chemical accuracy and generalizability, cutting training time by a factor of three or more. The essential information lies on a low‑dimensional, non‑linear manifold, suggesting that large datasets may be largely overlapping and that minimal, representative datasets could suffice for accurate predictions.
arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to ge...
arXiv:2610.08165v1 Announce Type: new Abstract: The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their...
SCALE (Simulation‑Calibrated Amortized Learning for Energy Materials) is a physics‑grounded learning architecture that fuses deterministic scientific operators, experimental calibration, and transformer‑scale inference to accelerate the discovery of energy materials. It converts expensive mechanistic computations and measured data into reusable models for rapid screening, ranking, inverse design, and active learning. In a case study on solid‑state metal‑hydride hydrogen‑storage, SCALE calibrated a phase‑equilibrium capacity operator against 381 experimental anchors, generated 5,000 teacher labels, and used a 2.90‑million‑parameter edge‑biased graph transformer to achieve high surrogate fidelity (MAE 0.0582 wt% H₂, RMSE 0.0833 wt% H₂, R² 0.9927, Pearson r 0.9963) while reducing per‑candidate screening cost by 10⁷–10⁸ times.
The paper presents a multitask learning approach that incorporates orbitally resolved semiempirical charges into machine learning interatomic potentials (MLIPs). By training with inexpensive GFN1‑xTB orbital charges, the model reduces energy mean absolute error by 58% compared to energy‑only baselines and needs five times less data to achieve similar performance. The method also outperforms or matches models using costly DFT charges and preserves inference efficiency, offering a scalable way to build accurate, data‑efficient foundation models for complex chemical systems.