The paper introduces PYTHIA, a machine‑learning framework that combines foundational models and transfer learning to optimize defects and adsorption sites in materials. By training a large foundational model on chemically similar molecules and then fine‑tuning with a transformer layer and Gaussian processes, PYTHIA can efficiently predict optimal sites using limited data. The framework is demonstrated on two distinct systems, successfully identifying optimal adsorption sites even for complex materials.
arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.
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.
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:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.
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.
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.
The article discusses how machine learning potentials (MLPs) can surpass the accuracy of the quantum‑chemical methods they are trained on by generalizing across a third space—different quantum‑chemical methods. It proposes the development of method‑transferable MLPs that predict properties at unseen methods, demonstrating that such models can outperform any of their training methods in both density‑functional and wavefunction‑based theories. The authors argue that pursuing accuracy beyond the training methods could enable universal MLPs to exceed single‑method models, influencing dataset construction and model evaluation to push chemical simulation limits.
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.
Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT). However, inference time of MLPs is orders of magnitude slower than that of classical force fields, hindering real-world applications for biomolecular systems that require timescales of microseconds and beyond.
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.
Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.
arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.