arXiv:2606. 19152v1 Announce Type: cross Abstract: Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive.
By Zongmin Zhang, Yuyang Lou, Bowen Zhang, Junwu Chen, Ryo Kuroki, Xuan Vu Nguyen, Edvin Fako, Lixue Cheng, Philippe Schwaller
El Agente Potente is an agentic system that integrates typed execution graphs and a coding mode to facilitate machine‑learning interatomic potential (MLIP) driven atomistic simulations. Typed execution graphs offer structured, provenance‑aware workflows where large language models handle planning and routing while deterministic Python code performs scientific computation and validation. The coding agent builds customized workflows for tasks needing procedural flexibility, invoking existing Potente functions for supported calculations. The system is demonstrated across materials discovery, energy‑landscape exploration, adsorption, and catalytic reaction workflows, with benchmarks on reproducibility and LLM token cost.
By Tsz Wai Ko, Jiaru Bai, Thomas Swanick, Yeonghun Kang, Changhyeok Choi, Angelina Qihong Jiang, Aiwei Yin, Varinia Bernales, Al\'an Aspuru-Guzik
arXiv:2606. 05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator.
By Zhilong Song, Zongmin Zhang, Lixue Cheng
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
By Penghui Yang, Zhonghan Zhang, Yue Li, Xinrun Wang, Yanchen Deng, Yuhao Lu, Bijun Tang, Zheng Liu, Bo An
arXiv:2606. 29459v1 Announce Type: cross Abstract: Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine-learning models reveal little about why a structure succeeds.
By Kyungmin Nam, Seunghee Han, Jihan Kim
arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.
By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork