1 citations · 4 across the 5 of their papers we have counts for
5 papers
A Physics-Informed Chemical Rule for Topological Materials Discovery
Xinyu Xu, Arif Ullah, Ming Yang
Topological phases of matter$\unicode{x2013}$comprising both insulators and semimetals$\unicode{x2013}$offer great potential for quantum applications, but identifying new candidate…
Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks
Muhammad Atif, Arif Ullah, Ming Yang
Accurately modeling quantum dissipative dynamics remains challenging due to environmental complexity and non-Markovian memory effects. Although machine learning provides a promisin…
Quantum-inspired Chemical Rule for Discovering Topological Materials
Xinyu Xu, Rajibul Islam, Ghulam Hussain +5
Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constraine…
TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery
Arif Ullah, Rajibul Islam, Yangming Huang +4
Topological materials, including topological insulators (TIs) and topological semimetals (TSMs), offer promising platforms for quantum, spintronic, and low-dissipation electronic t…
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
Arif Ullah, Yu Huang, Ming Yang +1
Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely i…