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Silicate weathering and organic carbon (OC) burial in soil regulate atmospheric CO2, but their influence on each other remains unclear. Generally, OC oxidation can generate acids that drive silicate weathering, yet clay minerals that form during weathering can protect OC and limit oxidation. This poses a conundrum where clay formation and OC preservation either compete or cooperate. Debate remains about their relative contributions because quantitative tools to simultaneously probe these processes are lacking while those that exist are often not measured in concert. Here we demonstrate that Li isotope ratios of sediment, commonly used to trace clay formation, can help constrain OC cycling. Measurements of river suspended sediment from two watersheds of varying physiography and analysis of published data from Hawaii soil profiles show negative correlations between solid-phase d7Li values and OC content, indicating the association of clay mineral formation with OC accumulation. Yet, the localities differ in their ranges of d7Li values and OC contents, which we interpret with a model of soil formation. We find that temporal trends of Li isotopes and OC are most sensitive to mineral dissolution/clay formation rates, where higher rates yield greater OC stocks and lower d7Li values. Whereas OC-enhanced dissolution primarily dictates turnover times of OC and silicate minerals, clay protection distinctly modifies soil formation pathways and is likely required to explain the range of observations. These findings underscore clay mineral formation, driven primarily by bedrock chemistry and secondarily by climate, as a principal modulator of weathering fluxes and OC accumulation in soil. 

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.