Modeling prebiotic chemistries with quantum accuracy at classical costs

• 成果类型: Editorial Material
• 署名作者: Tiwary, Pratyush
• 署名单位: University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9785
• DOI: 10.1073/pnas.2408742121
• 发表日期: 2024-06-04
• 关键词: force-field

摘要

Molecular Dynamics (MD) simulations using classical force fields are commonly employed in numerous scientific investigations. However, many natural processes involve bondbreaking and quantum forces. This complexity is compounded by the presence of multiple competing length and timescales. For example, accurately modeling the thermodynamics and dynamics of a chemical reaction requires accounting for the concerted movements of numerous solvent molecules and ions with their own fast or slow timescales. While widely used static density functional theory (DFT) calculations at 0 temperature can be beneficial for such investigations, they do not account for dynamics and lack precision in describing the molecular environments. They particularly fail at correct, rigorous treatments of finite- temperature fluctuations, and thus generalization to experimentally relevant conditions. In PNAS (1), Benayad et al. develop a scalable, generalizable approach for designing Neural Network Potentials (NNPs) that can handle chemical reactivity in solvated systems with quantum accuracy at classical costs. Specifically, they study phosphoester bond formation and rupture, which is fundamentally relevant to the phosphorus-oxygen bond formation central to life, and especially for the RNA world hypothesis. The framework developed here has the potential to generalize to different chemical reactions of energy and biological relevance.