Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy. While the number of available U-MLIPs and their fine-tuning applications is rapidly expanding, there remains a lack of systematic guidance on how to effectively fine-tune these models. This tutorial provides a comprehensive, step-by-step guide to fine-tuning U-MLIPs for computational materials modeling. Using the recently released MACE-MP-0 as a representative foundation model, we illustrate the full workflow of dataset preparation, hyperparameter selection, model training, and validation. Beyond methodological guidance, we conduct systematic case studies on solid-state electrolytes, stacking fault defects in metals, semiconductors, solid-liquid interfacial interactions in low-dimensional systems, and more complicated heterointerfaces. These examples demonstrate that fine-tuning substantially improves predictive accuracy while maintaining affordable computational cost, accelerates training convergence, enhances out-of-distribution generalization, and achieves superior data efficiency. Remarkably, fine-tuned foundation models can even capture aspects of long-range physics without explicit corrections. Together, these results highlight that fine-tuning not only provides a practical recipe for applying U-MLIPs, but also offers new insights into their physical fidelity and potential for advancing large-scale atomistic simulations. To support practical applications, we include code examples that enable researchers, particularly those new to the field, to efficiently incorporate fine-tuned U-MLIPs into their workflows.

Mechanical THz vibrations in nanocrystals have recently been harnessed for quantum sensing and thermal management. The free boundaries of nanocrystals introduce new surface wave solutions, analogous to the seismic waves on Earth, yet the implications of these surface waves on nanocrystals have remained largely unexplored. Here, we use atomistic molecular dynamics simulations and experimental neutron spectroscopy to elucidate these THz-scale features in nanodiamond. Our key insight is that thermally induced Rayleigh surface phonons, which have a low group velocity and an amplitude that decays exponentially away from the surface, are responsible for the previously observed but unexplained linear scaling of the low-energy vibrational density of states in nanocrystals. Large thermal atomic displacements, relative to the nanoparticle radius, induce perpetual surface quakes, even at ambient conditions. Normalised to the radius, the surface displacement ratio in diamond nanocrystals exceeds that of the largest recorded earthquakes by a factor of $10^{5\pm1}$. We explicate how these dramatic Rayleigh waves coexist with other distinctive features including confined lattice phonons, soft surface modes, the acoustic gap, Love waves, and Lamb modes, thereby offering a complete framework for the vibrational dynamics of nanocrystals.