arXiv Machine Learning August 26, 2026 By Megan C. Davis, R. Seaton Ullberg, Jeremy N. Schroeder, Andrew H. Salij, Marc J. Cawkwell, Christopher J. Snyder, Ivana Matanovic, Wilton J. M. Kort-Kamp

Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials

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arXiv Machine Learning

Jul 31

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

arXiv:2607. 28079v1 Announce Type: new Abstract: Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development.

By Tianyou Bai, Huan Wang, Mingchen Gao, Fangyue Lin, Pinze Ren, Zhenlin Zhao, Siming Dong

benchmarks

arXiv AI

Jun 3

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

arXiv:2604. 13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science.

By Auguste de Lambilly, Vladimir Baturin, David Portehault, Guillaume Lambard, Nataliya Sokolovska, Florence d'Alch\'e-Buc, Jean-Claude Crivello

diffusion fine-tuning benchmarks

arXiv AI

Sep 21

EnSol: an environment-aware graph neural network for molecular solubility prediction

arXiv:2609.21151v1 Announce Type: cross Abstract: Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficien...

By Thao Nguyen, Saman Shafaei, Zhengyi Zhang, Huimin Zhao, Heng Ji

benchmarks

arXiv AI

Jul 29

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.

By Sanjay Chakraborty

benchmarks

arXiv Machine Learning

Jun 30

Shoot from the HIP: Hessian Interatomic Potentials without derivatives

arXiv:2509. 21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy.

By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik

benchmarks

arXiv Machine Learning

Jul 1

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

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.

By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik

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Related stories

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

Finetuning-Free Diffusion Model with Adaptive Constraint Guidance for Inorganic Crystal Structure Generation

EnSol: an environment-aware graph neural network for molecular solubility prediction

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

Shoot from the HIP: Hessian Interatomic Potentials without derivatives

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table