39 citations · 42 across the 8 of their papers we have counts for
9 papers
All-Electron Single-Atom Reference Correction for Absolute Transition Energies in Fixed-Reference PAW-XCH Calculations
Yinan Wang, Teruyasu Mizoguchi
The transition energy of a core-loss spectrum comprises a transferable atomic core-reference contribution and a material-dependent response that determines chemical shifts. Density…
Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning
Xiaoyang Wang, Yinan Wang, Wenbo Zhao +4
Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES)…
Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES
Yinan Wang, Arpita Varadwaj, Teruyasu Mizoguchi +1
Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine…
From Disorder to Function: Entropy-Engineered Broadband Photonics with Ion-Transport-Stabilized Spectral Fidelity
Yuxiang Xin, Chen-Xin Yu, Jianru Wang +14
The high-entropy halide-perovskite field has expanded rapidly, yet a key gap remains: configurational entropy is not yet a reliable, designable lever to co-deliver expanded photoni…
OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
Yinan Wang, Xiaoyang Wang, Zhenyu Wang +3
High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are tr…
LAMBench: A Benchmark for Large Atomistic Models
Anyang Peng, Chun Cai, Mingyu Guo +9
Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first…
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
Daye Zheng, Xingliang Peng, Yike Huang +8
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic…
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
XiaoYang Wang, YiNan Wang, LinFeng Zhang +2
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the under…
A Generalizable Machine-learning Potential of Ag-Au Nanoalloys and its Application on Surface Reconstruction, Segregation and Diffusion
Yinan Wang, Xiaoyang Wang, Linfeng Zhang +2
Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To…