34 citations · 38 across the 8 of their papers we have counts for
9 papers
Equivariant learning of a transferable three-dimensional classical density functional
Bingqing Cheng
Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate ato…
Distilling latent electrostatics from foundation machine learning interatomic potentials
Xiaoyu Wang, Bingqing Cheng
Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally…
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs +21
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale a…
Ion-modulated structure, proton transfer, and capacitance in the Pt(111)/water electric double layer
Xiaoyu Wang, Junmin Chen, Zezhu Zeng +3
The electric double layer (EDL) governs electrocatalysis, energy conversion, and storage, yet its atomic structure, capacitance, and reactivity remain elusive. Here we introduce a…
Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States
Ping Tuo, Zezhu Zeng, Jiale Chen +1
Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables…
Thermal transport of amorphous hafnia across the glass transition
Zezhu Zeng, Xia Liang, Zheyong Fan +3
Heat transport in glasses across a wide range of temperature is vital for applications in gate dielectrics and heat insulator. However, it remains poorly understood due to the chal…
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…
Cartesian atomic cluster expansion for machine learning interatomic potentials
Bingqing Cheng
Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modelling in material science and chemistry. Many potentials use atomic cluster expansio…
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss +24
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantag…