A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy

The study of out-of-equilibrium systems offers a gateway to transformative technological appli- cations and emerging physical phenomena that are inaccessible via standard adiabatic pathways. However, modeling these states is formidably challenging, as it requires describing non-trivial physical processes across vast temporal and spatial scales. This thesis addresses the fundamen- tal accuracy versus efficiency trade-off inherent in the atomistic modeling of these phenomena by developing and deploying rigorous methodological frameworks based on high-fidelity machine learning interatomic potentials. These tools are utilized to investigate three distinct out-of- equilibrium regimes: • Ultrafast non-thermal melting in silicon: a novel framework based on constrained density functional perturbation theory and machine learning interatomic potentials is developed to accurately model the effects of laser-induced photoexcitation and investigate the role of phonon softenings in the non-thermal transition. • Structural and thermodynamic anomalies in undercooled liquid tellurium: a general-purpose machine learning interatomic potential is optimized and deployed to probe the complex chemistry of liquid tellurium, identifying numerous structural and thermodynamic anoma- lies and exploring the potential existence of a liquid-liquid phase transition analogous to that claimed for water; • Vibrational physics of confined carbyne: an accurate machine learning interatomic po- tential is developed for confined carbyne and employed to reproduce its resonant Raman spectra, accounting for high-order phonon-phonon scattering processes via the stochastic self-consistent harmonic approximation. Collectively, this research demonstrates that properly trained machine learning interatomic po- tentials can effectively bridge the accuracy versus efficiency tradeoff and show enhanced predictive capabilities when compared with experimental observations. By enabling the simulation of com- plex metastable and photoexcited states with quantum-chemical accuracy, this thesis provides a robust protocol for exploring the complex and fascinating physics of out-of-equilibrium systems.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2026

Author dc:creator

• Corradini, Andrea

Contributors dc:contributor

• Calandra Buonaura, Matteo
• Marini, Giovanni

• Machine learning interatomic potentials, liquid-liquid phase transitions, non-thermal melting, molecular dynamics, ultrafast photoexcitation, silicon, tellurium

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Creative commons
• license uri:http://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

Handle dc:identifier

https://hdl.handle.net/11572/483910

OAI identifier oai:identifier

oai:iris.unitn.it:11572/483910