Tootfinder

Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Alireza Ghafarollahi, Markus J. Buehler
https://arxiv.org/abs/2508.02956 https://

Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge baked into their training data. A central challenge lies in creating an intelligent system capable of autonomously executing the full inorganic materials discovery cycle, from ideation and planning to experimentation and iterative refinement. We introduce SparksMatter, a multi-agen…

An Ecosystem for Ontology Interoperability
Zhangcheng Qiang
https://arxiv.org/abs/2507.12311 https://arxiv.org/pdf/2507.12311

An Ecosystem for Ontology Interoperability
Ontology interoperability is one of the complicated issues that restricts the use of ontologies in knowledge graphs (KGs). Different ontologies with conflicting and overlapping concepts make it difficult to design, develop, and deploy an interoperable ontology for downstream tasks. We propose an ecosystem for ontology interoperability. The ecosystem employs three state-of-the-art semantic techniques in different phases of the ontology engineering life cycle: ontology design patterns (ODPs) in t…