6 papers
Machine Learning the Strong Disorder Renormalization Group Method for Disordered Quantum Spin Chains
A. Ustyuzhanin, J. Vahedi, S. Kettemann
We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisati…
Symbolic regression for defect interactions in 2D materials
Mikhail Lazarev, Andrey Ustyuzhanin
Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models of…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
The SND@LHC neutron shielding
LHC Collaboration
The design and construction of a neutron shielding for the SND@LHC detector, which utilizes a combination of plexiglass and borated polyethylene, is presented. FLUKA simulations we…
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
Wei Nong, Ruiming Zhu, Zekun Ren +7
Machine learning interatomic potentials (MLIAPs) have emerged as powerful tools for accelerating materials simulations with near-density functional theory (DFT) accuracy. However,…
Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models
Abdalaziz Al-Maeeni, Denis Derkach, Andrey Ustyuzhanin
The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generati…