A microscopic description of acid-base equilibrium
arXiv:1905.02080 · doi:10.1073/pnas.1819771116
Abstract
Acid-base reactions are ubiquitous in nature. Understanding their mechanisms is crucial in many fields, from biochemistry to industrial catalysis. Unfortunately, experiments only give limited information without much insight into the molecular behaviour. Atomistic simulations could complement experiments and shed precious light on microscopic mechanisms. The large free energy barriers connected to proton dissociation however make the use of enhanced sampling methods mandatory. Here we perform an ab initio molecular dynamics (MD) simulation and enhance sampling with the help of methadynamics. This has been made possible by the introduction of novel descriptors or collective variables (CVs) that are based on a conceptually new outlook on acid-base equilibria. We test successfully our approach on three different aqueous solutions of acetic acid, ammonia, and bicarbonate. These are representative of acid, basic, and amphoteric behaviour.
References in corpus (3)
Cited by in corpus (12)
- Deep Learning Collective Variables from Transition Path Ensemble
- Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CHSOH and HO in phenol
- Entropy governs the structure and reactivity of water dissociation under electric fields
- How collective phenomena impact CO2 reactivity and speciation in different media
- Mechanistic insights into water autoionization
- Intramolecular and water mediated tautomerism of solvated glycine
- Carbon Dioxide, Bicarbonate and Carbonate Ions in Aqueous Solutions at Deep Earth Conditions
- Tautomeric equilibrium in condensed phases
- Propensity of water self-ions at air(oil)-water interfaces revealed by deep potential molecular dynamics with enhanced sampling
- Dissipative Tunneling Rates through the Incorporation of First-Principles Electronic Friction in Instanton Rate Theory I: Theory
- Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations
- Chemistrees: data driven identification of reaction pathways via machine learning