Recent Articles
Chris Lu, Cong Lu, R. T. Lange, Jakob Foerster, Jeff Clune et al. · arXiv (Cornell University) (2024)
Directly demonstrates fully autonomous AI scientist for open-ended scientific discovery, aligning with HIGH PRIORITY interest in AI agents for computational research
Dylan M. Anstine, R.I. Zubatyuk, Olexandr Isayev · Chemical Science (2025)
Neural network interatomic potential enabling efficient computational chemistry automation and materials property prediction
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Kipton Barros et al. · Chemical Reviews (2024)
Data generation strategies for machine learning interatomic potentials bridging computational accuracy and simulation scale
Tonghang Han, Zhengguang Lu, Yuxuan Yao, Jixiang Yang, Junseok Seo et al. · Science (2024)
Quantum anomalous Hall effect in 2D graphene-WS₂ heterostructure demonstrating moiré physics and topological flat-band engineering
Kapildeb Dolui, Lewis J. Conway, Christoph Heil, Timothy A. Strobel, Rohit P. Prasankumar et al. · Physical Review Letters (2024)
Routes to ambient-pressure hydride superconductivity combining rare-earth hydrides and superconductivity from core research interests
Classic Foundations
Paolo Giannozzi, Stefano Baroni, Nicola Bonini, Matteo Calandra, Roberto Car et al. · Journal of Physics Condensed Matter (2009)
QUANTUM ESPRESSO: foundational DFT code underpinning first-principles methods throughout computational materials work
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang et al. · Nature Reviews Physics (2021)
Seminal Nature Reviews Physics on physics-informed machine learning, establishing integration of physics constraints with AI models
Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh · Nature (2018)
Foundational Nature review of machine learning for molecular and materials science, core methodology for computational materials
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux et al. · arXiv (Cornell University) (2023)
LLaMA foundation model enabling large language models for LLM-assisted DFT automation and autonomous computational workflows
Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu, Hui‐Xiong Deng et al. · InfoMat (2019)
Comprehensive review of machine learning in materials science covering data-driven acceleration of traditional DFT approaches
Exploratory
Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori et al. · Cell (2024)
AI agent methodology for autonomous biomedical discovery, demonstrating cross-domain transfer of autonomous research agent paradigms
Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari et al. · Journal of Artificial Intelligence Research (2024)
Human-in-the-loop reinforcement learning framework for training autonomous agents with human oversight, enabling trustworthy AI scientists