The paper introduces V-PaSS, a physics‑aware semantic system that predicts perovskite solar cell performance by interpolating crystal graphs of 23 pure $ABX_3$ compounds using Vegard’s law and learned bandgap bowing. It couples device stack information—including layer sequence, polarity, and interfacial band offsets—with a scientific language model to encode chemical descriptions. In an institution‑grouped evaluation, V‑PaSS outperforms fourteen baseline models, achieving the lowest mean absolute error (2.861%) and demonstrating significant gains on unseen absorber alloys, new laboratory samples, and devices with unobserved transport layers.
By Qiwei Han, Chi Zhou
VerifiedLab is a multi‑agent framework that transforms research records into active systems for laboratory experiments. It employs a team of AI scientists—principal investigator, librarian, specialists, gate, and critic—to reason over records field by field, verify claims against measured reliability, and decide whether to release or withhold information. Experiments on perovskite solar cells show that VerifiedLab can recover known physical trends, anticipate future device reports, and provide a trust metric for inherited literature.
By Yu Yao, Jiayi Dong, Yang Yang, Shuiwang Ji, Ju Li
PeroMAS is a multi-agent system designed for discovering perovskite materials, integrating tools encapsulated as Model Context Protocols to handle the full workflow from literature retrieval to synthesis. It enables design under multi-objective constraints and outperforms single LLM or traditional search methods in discovery efficiency. The system’s effectiveness was validated by real synthesis experiments guided by expert evaluation.
By Yishu Wang, Wei Liu, Yifan Li, Shengxiang Xu, Xujie Yuan, Ran Li, Yuyu Luo, Jia Zhu, Shimin Di, Min-Ling Zhang, Guixiang Li
SolarBench is an open global benchmark for image-based solar nowcasting that consolidates over six million sky and satellite images from 11 sites across a decade, paired with irradiance, PV output, and atmospheric data. The benchmark includes a toolbox for reproducible data access, processing, model development, and evaluation. Using SolarBench, the authors benchmark representative models, uncover a gap between average forecasting accuracy and the capture of rapid solar fluctuations, quantify predictability across cloud regimes, and demonstrate data‑efficient adaptation to new PV systems.
By Yuhao Nie, Stephen Campbell, Quentin Paletta, Liwenbo Zhang, Tao Jing, Samer Chaaraoui, Jonathan Giezendanner, Andea Scott, Tao Sun, Cong Feng, Max Aragon, Jacques Camier, Adam Jensen, Florian Kotthoff, Yuexing Yang, Yang Ming, Mengying Li, Stefanie Meilinger, Yupeng Wu, Adam Brandt, Sherrie Wang
arXiv:2606. 19624v1 Announce Type: new Abstract: Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery.
By Hongxuan Liu, Roman Bushuiev, Ivy Lightheart, Mrunali Manjrekar, Anton Bushuiev, Magdalena Lederbauer, Filip Jozefov, Yinkai Wang, Soha Hassoun, Josef Sivic, James Taylor, Runzhong Wang, David Healey, Tom\'a\v{s} Pluskal, Connor W. Coley
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.
By Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni, Steven Schockaert