arXiv:2606. 02662v1 Announce Type: cross Abstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data.
By Vivin Vinod, Peter Zaspel
arXiv:2602. 16908v2 Announce Type: replace-cross Abstract: Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks.
By G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, A. Melnikov
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.
By Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda, Julian Lorenz, Rainer Lienhart, Fabian Pauly
The paper presents a closed‑loop molecule generation pipeline that iteratively retrains on new quantum‑chemical simulation data, overcoming limitations of static generative models. This approach produces molecules whose properties extend up to 0.44 standard deviations beyond the training set and improves out‑of‑distribution classification accuracy by 79%. By conditioning on thermodynamic stability during the loop, the method yields a 3.5‑fold increase in the proportion of stable, potentially synthesizable molecules.
By Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski
arXiv:2503.24111v4 Announce Type: replace-cross
Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classica...
By Arthur M. Faria, Ignacio F. Gra\~na, Savvas Varsamopoulos
arXiv:2609.16527v1 Announce Type: cross
Abstract: Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with...
By Michael Hanna, Julian Cremer, Zekiye Erarslan, Leonardo Medrano Sandonas