activity
20242026
collaborators

9 papers

quant-ph2026

Agentic AI for Scientific Reasoning in Autonomous Quantum Sensing Experiments

Takuya Isogawa, Ryotaro Okabe, Nutdech Phadetsuwannukun +2

We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond. NV centers are a widel…

cond-mat.mtrl-sci2026

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha +6

Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to dete…

cond-mat.mtrl-sci2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Aritra Roy, Kevin Shen, Andrew MacBride +350

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…

cond-mat.mtrl-sci2026

Quantum Theory of Functionally Graded Materials

Michael J. Landry, Ryotaro Okabe, Chuliang Fu +1

Functionally graded materials (FGMs) are composites whose composition or microstructure varies continuously in space, producing position-dependent mechanical and functional propert…

cond-mat.mtrl-sci2025

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation

Manasi Mandal, Eunbi Rha, Abhijatmedhi Chotrattanapituk +13

CdAs is a prototypical Dirac semimetal that hosts a chiral anomaly and thereby functions as a platform to test high-energy physics hypotheses and to realize energy efficien…

cond-mat.mtrl-sci2025

AI-Driven Defect Engineering for Advanced Thermoelectric Materials

Chu-Liang Fu, Mouyang Cheng, Nguyen Tuan Hung +7

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-off…