19 citations · 19 across the 2 of their papers we have counts for
6 papers
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Ting Liang, Ke Xu, Eric Lindgren +16
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…
Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks
Penghua Ying, Ting Liang, Yun Chen +5
While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport w…
Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures
Hekai Bu, Wenwu Jiang, Penghua Ying +3
Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conve…
Lattice distortion leads to glassy thermal transport in crystalline CsBiICl
Zezhu Zeng, Zheyong Fan, Michele Simoncelli +5
The glassy thermal conductivities observed in crystalline inorganic perovskites such as CsBiICl is perplexing and lacking theoretical explanations. Here, we first e…
NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
Ke Xu, Ting Liang, Nan Xu +5
Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across va…
Phonon coherence and minimum thermal conductivity in disordered superlattice
Xin Wu, Zhang Wu, Ting Liang +4
Phonon coherence elucidates the propagation and interaction of phonon quantum states within superlattice, unveiling the wave-like nature and collective behaviors of phonons. Taking…