Year: 2026
Type: article
Abstract: Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behavior of CaCO3 in water has been widely studied to understand its mineralization in seawater, with potential implications for carbon-capture strategies. However, making accurate computational predictions has been a long-standing challenge, requiring both highly accurate electronic structure methods and extensive statistical sampling. Here, we combine advances in machine learning and electronic structure theory to fully resolve the ion pairing free energy of CaCO3 with explicit solvation. We show that achieving quantitative agreement with experiment requires going beyond density functional theory up to the “gold-standard” coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We generate a set of systematically improvable models, enabling reliable insights into the initial association mechanism of Ca and CO3 ions prior to nucleation while fully quantifying enthalpic and entropic effects. These quantitatively accurate models further resolve a long-standing lack of consensus surrounding the hydration mechanism of the calcium ion in solution. Our results demonstrate that CCSD(T)-level thermodynamic predictions of complex aqueous systems can now be routinely achieved.
DOI: 10.1021/jacs.6c20276
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funders: National Computational Infrastructure, Dell EMC, Flatiron Health, Royal Society, European Commission, Cambridge Trust, Isaac Newton Trust, Gates Cambridge Trust, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council, European Research Council, Australian Research Council
Awards: G1226538766, G1584869537, Theoretical Condensed Matter Cambridge - Critical Mass Grant, Support for the UKCP consortium, Predictive simulation of crystallisation, G4028632349, G5275711260, G581313191, Cambridge Service for Data Driven Discovery (CSD3) - A National Data Intensive Science Cloud for Converged Simulation, AI & Analytics, G8116984139
OpenAlex ID: W7134840893
