AI Article Synopsis
- Path integral (PI) simulations are used to investigate how nuclear quantum effects (NQEs) impact the behavior of hydroxide hydrate and its isotopomer as it traverses an H-bond bifurcation pathway.
- A new potential energy surface is developed using machine learning techniques combined with high-level computational data to accurately model these systems.
- The study reveals that while NQEs increase the bifurcation barrier at room temperature, they noticeably decrease and flatten the barrier at low temperatures, affecting the O-O distance significantly during the exploration.
Article Abstract
Path integral (PI) simulations are used to explore nuclear quantum effects (NQEs) in hydroxide hydrate and its perdeuterated isotopomer along the H-bond bifurcation pathway. Toward this, a new potential energy surface using the symmetric gradient domain machine learning method with ab initio data at the CCSD(T)/aug-cc-pVTZ level is built. From PI umbrella sampling (US) simulations, free energy profiles along the bifurcation coordinate are explored as a function of temperature. At ambient temperature, the bifurcation barrier is increased upon inclusion of NQEs. At low temperatures in the deep tunneling regime, the barrier is strongly decreased and flattened. These trends are examined, and the role of the O-O distance is also investigated through two-dimensional US simulations.
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| http://dx.doi.org/10.1021/acs.jpca.3c08027 | DOI Listing |
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