Recent Articles
Tiago F. T. Cerqueira, Yue‐Wen Fang, Ion Errea, Antonio Sanna, Miguel A. L. Marques · Advanced Functional Materials (2024)
ML-assisted search for hydride superconductors exemplifies AI-driven materials discovery, directly addressing the researcher's interest in autonomous computational workflows and high-Tc superconductivity.
Tonghang Han, Zhengguang Lu, Zach Hadjri, Lihan Shi, Zhenghan Wu et al. · Nature (2025)
Chiral superconductivity in rhombohedral graphene combines graphene, superconductivity, and 2D materials—core research areas with latest experimental insights.
Zhihuan Dong, Adarsh S. Patri, T. Senthil · Physical Review Letters (2024)
Theoretical framework for quantum anomalous Hall phases in graphene moiré addresses flat bands and topological properties in 2D heterostructures, matching moiré physics interests.
Yi Liu, Ziyi Liu, Jin‐Ke Bao, Pengtao Yang, Liang-Wen Ji et al. · Nature (2024)
Superconductivity discovery in chromium-based kagome metal contributes to strongly correlated materials and unconventional superconductivity research.
Songhao Guo, Willa Mihalyi‐Koch, Yuhong Mao, Xinyu Li, Kejun Bu et al. · Nature Communications (2024)
Exciton engineering in 2D Ruddlesden–Popper perovskites unites optical response calculations, 2D materials, and excitonic properties—all explicit research strengths.
Classic Foundations
Juraj Krempaský, Libor Šmejkal, S. W. D’Souza, Mahdi Hajlaoui, G. Springholz et al. · Nature (2024)
Altermagnetic materials framework establishes new electronic structure classification in condensed matter, foundational for understanding unconventional magnetic and topological properties.
Adil Rasheed, Omer San, Trond Kvamsdal · IEEE Access (2020)
Digital twin methodology provides computational simulation framework for optimization and control, underpinning automated computational workflows and materials design.
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai et al. · arXiv (Cornell University) (2023)
Comprehensive survey of generative AI evolution provides foundational overview of large language models and autonomous agents relevant to AI-assisted research workflows.
Arseniy I. Kuznetsov, Mark L. Brongersma, Jin Yao, Mu Ku Chen, Uriel Levy et al. · ACS Photonics (2024)
Optical metasurfaces roadmap establishes design principles for engineering optical response in materials, directly relevant to optical property calculations and device engineering.
Min Yan, Can Huang, Peter Bienstman, Peter Tiňo, Wei Lin et al. · Nature Communications (2024)
Reservoir computing framework reviews neural network methods for learning dynamical systems, foundational for ML-assisted computational approaches in materials science.
Exploratory
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, Min Chen, Chenying Liu et al. · The Innovation (2024)
AI-driven transformation of scientific inquiry in geoscience offers methodological insights for adapting autonomous research workflows to condensed matter physics.
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lü et al. · 2024
AGIEval benchmark for evaluating foundation models on human-level scientific tasks bridges AI capability assessment with potential applications in autonomous materials discovery and research.