AI model learns long-range electrostatics with polarizable atomic multipoles

Researchers have developed a new framework for machine learning interatomic potentials (MLIPs) that addresses the challenge of long-range electrostatics and polarization. This approach uses polarizable atomic multipoles to predict environment-dependent latent monopoles, dipoles, and quadrupoles, while also capturing non-local charge transfer and polarization through linear response. The framework demonstrates improved accuracy across various benchmarks and enables MLIPs to predict polarization-sensitive observables. AI

影响 Introduces a novel method for improving MLIPs, potentially enhancing their accuracy in predicting properties of ionic, polar, and interfacial systems.

排序理由 This is a research paper detailing a new scientific framework for machine learning interatomic potentials. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• arXiv
• MAPbI$_3
• MLIPs

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →