Public articles linked to the same research event.
arXiv The authors introduce AI Theorist, a system of AI agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations, and evidence-driven refinement, and apply it to the Kitaev quantum spin liquid candidate α-RuCl₃, producing a new interpretation of optical and photocurrent observations that identifies distinct excitonic states with contrasting optical selection rules and real-space distributions.
The authors introduce AI Theorist, a system of AI agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations, and evidence-driven refinement, and apply it to the Kitaev quantum spin liquid candidate α-RuCl₃, producing a new interpretation of optical and photocurrent observations that identifies distinct excitonic states with contrasting optical selection rules and real-space distributions.
The authors introduce AI Theorist, a system of AI agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations, and evidence-driven refinement, and apply it to the Kitaev quantum spin liquid candidate α-RuCl₃, producing a new interpretation of optical and photocurrent observations that identifies distinct excitonic states with contrasting optical selection rules and real-space distributions.
The authors introduce AI Theorist, a system of AI agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations, and evidence-driven refinement, and apply it to the Kitaev quantum spin liquid candidate α-RuCl₃, producing a new interpretation of optical and photocurrent observations that identifies distinct excitonic states with contrasting optical selection rules and real-space distributions.