An Updated Evaluation of the Molybdenum Equation of State

Equation of State production has traditionally been heavily reliant on individual expertise and judgement when fitting parametric models to reference data. Whilst highly successful this approach has been limited in its ability to provide a quantitative measure of uncertainty to the end-user. Furthermore, the actual procedure followed is often opaque to those not directly involved. Molybdenum serves as relatively simple yet widely appliable material to explore how EoS production can be better quantified, automated, and ultimately elevated to an ‘uncertainty-aware’ status. We apply Markov-Chain Monte-Carlo sampling alongside computationally efficient EoS modelling to obtain posterior distributions over both end-result EoS and their respective predictions in thermodynamic regimes of interest. Additionally, we compare the results to very recent SNL Z experiments and new density functional theory calculations to 100 eV temperatures.