Probabilistic materials screening holds significant potential for rational design of innovative energy storage devices provided that a screening methodology relies on efficient simulation methods augmented by advanced machine learning techniques [1]. Here, we focus on the computational design of solid electrolytes with particular attention paid to the development of novel approaches for reinforcing efficiency of atomistic simulation techniques as well as samplers in Bayesian inference through new adaptive multi-stage numerical integrators and effective hyperparameter tuning to be incorporated into a unified framework for large-scale and high-throughput screening of solid electrolyte material systems. We provide performance comparison of the proposed methodologies with the state-of-the-art methods when applied to popular deterministic and stochastic samplers, such as molecular dynamics, Hybrid Monte Carlo (HMC) [2], generalized HMC [3], modified HMC [4], and demonstrate substantial gains in accuracy prediction and sampling efficiency with the novel approaches. Our recent post-processing methods employing data mining and clustering techniques to elucidate ion’s behavior and conductivity mechanisms [5] will also be discussed. REFERENCES [1] M.R. Bonilla, F. García Daza, M. Fernández-Pendás, J. Carrasco, and E. Akhmatskaya, Multiscale modelling and simulation of advanced battery materials, Springer, 69 – 113 (2021). [2] S. Duane, A.D. Kennedy, B.J. Pendleton, and D. Roweth, “Hybrid Monte Carlo”, Phys. Lett. B, 195, 216–222 (1987). [3] A. Kennedy and B. Pendleton, “Cost of the generalised hybrid Monte Carlo algorithm for free field theory”, Nuclear Physics B, 607, 456–510 (2001). [4] E. Akhmatskaya, M. Fernández-Pendás, T. Radivojevic, and J.M. Sanz-Serna, “Adaptive splitting integrators for enhancing sampling efficiency of modified Hamiltonian Monte Carlo methods in molecular simulation”, Langmuir, 33, 11530–11542 (2017). [5] H.A. Cortés, M.R. Bonilla, H. Früchtl, T. van Mourik, J. Carrasco, and E. Akhmatskaya, “A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials: the case of dual-doped Ga0.15/Scy Li7La3Zr2O12”, Journal of Materials Chemistry A, 12, 5181-5193 (2024).