The paper proposes pre-training uncertainty estimators on large, diverse materials science datasets to improve uncertainty quantification (UQ) for machine‑learned interatomic potentials (MLIPs). By evaluating these pre‑trained models on both in‑distribution and out‑of‑domain adsorption‑energy predictions, the authors demonstrate that zero‑shot UQ is viable and that fine‑tuning yields strong correlations between predicted uncertainty and actual prediction error, even on challenging datasets. This approach aims to provide foundational UQ models that enable broader, more reliable deployment of MLIPs across chemical space.
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.
arXiv:2603. 10950v2 Announce Type: replace Abstract: Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates.
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv:2609.00488v1 Announce Type: new Abstract: Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamic...
arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.
The study examines how multi‑fidelity training—using abundant low‑fidelity data and scarce high‑fidelity labels—affects uncertainty decomposition in atomistic models. By training deep ensembles of mean‑variance MACE models on ANI‑1x, the authors compare high‑fidelity training from scratch with low‑fidelity pretraining followed by fine‑tuning, evaluating aleatoric, epistemic, and total uncertainty under unseen molecular systems and higher‑energy configurations. Results show that pretraining lowers prediction error and improves uncertainty‑based ranking, but does not produce well‑calibrated predictive variances, especially under energy extrapolation, and that fine‑tuning yields a more effective uncertainty‑based acquisition function.
arXiv:2608. 11054v1 Announce Type: new Abstract: Deep learning models have emerged as the standard computational tool for a wide range of applications in genomics.
arXiv:2606. 11870v1 Announce Type: cross Abstract: Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces.
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:2609.08620v1 Announce Type: cross Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learni...
BOOM is a new benchmark for evaluating out‑of‑distribution (OOD) molecular property predictions in machine learning. It provides chemically‑informed tests across common property prediction tasks and assesses over 150 model‑task combinations. The study shows that current models, including chemical foundation models, struggle to generalize OOD, with the best model still exhibiting three times higher error than in‑distribution predictions.
arXiv:2508. 05762v2 Announce Type: replace-cross Abstract: Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table.