Bingqing Cheng
@chengbingqing.bsky.social
Computational Materials Science. Assistant Professor at UC Berkeley.
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/pdf/2504.05169
arxiv.org
April 8, 2025 at 2:34 AM
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/pdf/2504.05169
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!
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...
arxiv.org
December 23, 2024 at 5:08 PM
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!