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Bingqing Cheng

@chengbingqing.bsky.social
210 followers 87 following 4 posts

Computational Materials Science. Assistant Professor at UC Berkeley.

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Bingqing Cheng @chengbingqing.bsky.social · 08/04/2025
Guess what? By learning from energies and forces, machine learning interatomic potentials can now infer electrical responses like polarization and BECs! This means we can perform MLIP MD simulations under electric fields! arxiv.org/pdf/2504.05169
arxiv.org
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Bingqing Cheng @chengbingqing.bsky.social · 26/03/2025
Method paper finally published:https://www.nature.com/articles/s41524-025-01577-7
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Bingqing Cheng @chengbingqing.bsky.social · 23/12/2024
The original method paper: arxiv.org/abs/2408.15165
arxiv.org
Latent Ewald summation for machine learning of long-range interactions
Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to...
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Bingqing Cheng @chengbingqing.bsky.social · 23/12/2024
Long-range machine learning potentials strike again! 🚀 We benchmarked the Latent Ewald Summation method on diverse systems—molecules, solutions, interfaces. Learning just from energy & forces, it delivers the most accurate potential energy surfaces, physical charges, dipoles, and quadrupoles!
arxiv.org
Learning charges and long-range interactions from energies and forces
Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine lear...
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