Computational methods have revolutionised material discovery, driving interest in designing new advanced materials, e.g., superconductors, which exhibit zero resistivity below a critical temperature (Tc). Reaching pressures in the 100 GPa range enables the synthesis of superhydrides, a new class of hydrogen-rich materials that exhibit remarkable properties such as superconductivity, hydrogen diffusion, and hydrogen storage. A striking example is LaH₁₀, which holds the record for the highest superconducting transition temperature at Tc = 250 K [1-3]. However, stabilising such compounds at ambient or low pressure remains a major challenge for practical applications. This has driven the search beyond binary hydrides toward ternary superhydrides, aiming to discover new superconductors with high critical temperatures that can persist near ambient pressure. The discovery of new superconducting hydrides traditionally relies on ab initio prediction and experimental synthesis. However, ab initio calculations become computationally prohibitive, e.g. for assessing the thermodynamic stability of complex systems such as ternary superhydrides. To overcome this limitation, our group has successfully developed tailored machine learning interatomic potentials (EDDP) enabling a great acceleration in structure prediction [4]. This approach was used to build the convex-hull of a new predicted metastable ambient-pressure hydrogen-based superconductor Mg2IrH6 with a Tc of 160 K [5]. The importance of evaluating thermodynamic stability alongside dynamical stability has been explicitly acknowledged by the community during the discussion session of the “Computational Design of Room Temperature Superconductors” workshop (CDRTS 2025). Very recently, a ternary hydride, Y3Fe4H20, synthesized at 80 GPa from a Laves phase hydride was brought metastable at ambient pressure and ambient air for few hours [6,7]. While this material did not show evidence of superconducting transition, this result confirms the possibility to bring back superhydrides from high pressure to ambient pressure, paving the way for promising high-temperature superconducting hydride metastable at ambient pressure. Given that metastable states vastly outnumber thermodynamically stable ones, a large pool of energetically plausible metastable high-Tc hydrides can be expected, though only a small fraction may be experimentally accessible. Incorporating synthetic feasibility into predictive frameworks could therefore significantly improve the experimental realization of theoretically promising candidates. Thus, a key challenge in superconductor prediction is improving the prediction of synthesizability by assessing thermodynamic and kinetic stability, an aspect as critical as correctly predicting Tc. In this seminar, I will introduce our high-throughput workflow to search for new superconductors, including a method to improve the prediction of Tc with coarse parameters [8]. [1] M. Somayazulu et al., Phys. Rev. Lett., vol. 122 (2019) 027001 [2] A.P. Drozdov et al., Nature, vol 569 (2019) 528-531 [3] M. Caussé et al., Phy. Rev. B, vol 107 (2023) L060301 [4] C. J. Pickard, Phys Rev B, vol 106 (2022) 014102 [5] K. Dolui et al., Phys. Rev. Lett., vol 132 (2024) 166001 [6] M. Caussé et al. JALCOM, vol 1010 (2025) 177392 [7] M. Caussé et al. arXiv (2025) arXiv:2505.16799v1 [8] K. Bozier et al. arXiv (2025) arXiv:2508.18371v1