activity
20232026
most citedSelf-Supervised Generative Models for Crystal Structures

6 citations · 6 across the 5 of their papers we have counts for

collaborators

7 papers

cond-mat.str-el2026

Observation geometry for uncertainty-aware Hamiltonian inference and experimental design in quantum magnets

Roy Liu, Venugopal Ranganathan, David Dahlbom +12

Determining microscopic interactions from spectroscopic and scattering measurements is central to understanding quantum materials, yet it often remains unclear which interactions c…

cond-mat.str-el2026

AIMS: an AI experimentalist turns uncertainty into quantum matter discovery

Siyuan Qiu, Philip D. Suh, Nhat Huy Tran +13

Most AI agents act only after scientists have defined the task. Discovery is harder under practical uncertainties: the probe may not be where it is expected, the signal may occupy…

cs.LG2026

Toward Personalized Darts Training: A Data-Driven Framework Based on Skeleton-Based Biomechanical Analysis and Motion Modeling

Zhantao Chen, Dongyi He, Jin Fang +4

As sports training becomes more data-driven, traditional dart coaching based mainly on experience and visual observation is increasingly inadequate for high-precision, goal-oriente…

cs.LG2025

Multi-resolution Enhancement for Full Spectrum Neural Representations

Yuan Ni, Zhantao Chen, Shizhou Xu +5

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (IN…

cs.CV2025

Physics-Guided Dual Implicit Neural Representations for Source Separation

Yuan Ni, Zhantao Chen, Alexander N. Petsch +7

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contribution…

cond-mat.mtrl-sci2023★ 6 cited

Self-Supervised Generative Models for Crystal Structures

Fangze Liu, Zhantao Chen, Tianyi Liu +4

Drawing inspiration from the achievements of natural language processing, we adopt self-supervised learning and utilize an equivariant graph neural network to develop a unified pla…

cs.CV2023

Augmenting x-ray single particle imaging reconstruction with self-supervised machine learning

Zhantao Chen, Cong Wang, Mingye Gao +3

The development of X-ray Free Electron Lasers (XFELs) has opened numerous opportunities to probe atomic structure and ultrafast dynamics of various materials. Single Particle Imagi…