8 citations · 14 across the 14 of their papers we have counts for
16 papers
JANUS: A Multi-modal Foundation Neural Sampler for Disordered Materials
Denis Blessing, Mouyang Cheng, Maximilian Schebek +4
Disordered materials are central to modern technologies, from high-performance alloys to quantum devices, yet understanding and designing them requires exploring an enormous space…
ATLAS: A Foundation Neural Sampler for Amorphous Materials
Mouyang Cheng, Denis Blessing, Botao Yu +4
Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition tem…
Can LLMs extract scientific consensus? A case study in high-temperature superconductivity
Mouyang Cheng, Wenhao He, Zhuotao Jin +9
Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. Whi…
Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
Mouyang Cheng, Bowen Yu, Chu-Liang Fu +14
Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium m…
Frustrated Magnetism in FeGeO with a Chiral Trillium Network
Matt Boswell, Mingyu Xu, Haozhe Wang +7
The discovery of new magnetic ground states in geometrically frustrated lattices remains a central challenge in materials science. Here, we report the synthesis, structural charact…
Reinforcement learning-guided optimization of critical current in high-temperature superconductors
Mouyang Cheng, Qiwei Wan, Bowen Yu +3
High-temperature superconductors are essential for next-generation energy and quantum technologies, yet their performance is often limited by the critical current density (),…
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Mouyang Cheng, Weiliang Luo, Hao Tang +6
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as ox…
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…
Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction
Thanh Nguyen, Chuliang Fu, Mouyang Cheng +17
Efficient thermal management is essential for the reliability of modern power electronics, where increasing device density leads to severe heat dissipation challenges. However, in…
A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
Mouyang Cheng, Chu-Liang Fu, Bowen Yu +5
Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, es…
Raman Fingerprints of Phase Transitions and Ferroic Couplings in Van der Waals Material CuCrPS
Jing Tang, Benjamin J Lawrie, Mouyang Cheng +9
CuCrPS (CCPS), a type-II multiferroic material, exhibits unique phase transitions involving ferroelectric, antiferroelectric, and antiferromagnetic ordering. In this study,…
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…
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…
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…