8 citations · 14 across the 17 of their papers we have counts for
17 papers
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…
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…
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…
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…
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…
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…
AI-driven materials design: a mini-review
Mouyang Cheng, Chu-Liang Fu, Ryotaro Okabe +4
Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techni…
Quantum Theory of X-ray Photon Correlation Spectroscopy
Phum Siriviboon, Chu-Liang Fu, Michael Landry +4
Characterizing quantum materials is essential for understanding their microscopic interactions and advancing quantum technology. X-ray photon correlation spectroscopy (XPCS) with c…
Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
Ryotaro Okabe, Zack West, Abhijatmedhi Chotrattanapituk +5
The synthesis of inorganic crystalline materials is essential for modern technology, especially in quantum materials development. However, designing efficient synthesis workflows r…
Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates
Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk +9
Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searc…
Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure
Nguyen Tuan Hung, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk +1
Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. Howeve…
Incipient nematicity from electron flat bands in a kagome metal
Nathan Drucker, Thanh Nguyen, Manasi Mandal +16
Engineering new quantum phases requires fine tuning of the electronic, orbital, spin, and lattice degrees of freedom. To this end, the kagome lattice with flat bands has garnered g…
Machine learning detecting Majorana Zero Mode from Zero Bias Peak measurements
Mouyang Cheng, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk +1
Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry non-Abelian statistics, hold great promise for achieving fault-tolerant topological quantum computation. A…
Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation
Manasi Mandal, Abhijatmedhi Chotrattanapituk, Kevin Woller +10
The precise controllability of the Fermi level is a critical aspect of quantum materials. For topological Weyl semimetals, there is a pressing need to fine-tune the Fermi level to…
Topological superconductors from a materials perspective
Manasi Mandal, Nathan C. Drucker, Phum Siriviboon +6
Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits t…
Tetris-inspired detector with neural network for radiation mapping
Ryotaro Okabe, Shangjie Xue, Jiankai Yu +6
In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their confi…
Virtual Node Graph Neural Network for Full Phonon Prediction
Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird +10
The structure-property relationship plays a central role in materials science. Understanding the structure-property relationship in solid-state materials is crucial for structure d…