Bingqing Cheng

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

Method paper finally published:https://www.nature.com/articles/s41524-025-01577-7

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...

The original method paper:
arxiv.org/abs/2408.15165

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...

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!