{"id":{"repo_id":"trento","oai_identifier":"oai:iris.unitn.it:11572/483910"},"canonical_url":"https://search.dev.ndltd.org/etd/trento/oai:iris.unitn.it:11572/483910","repository":{"repo_id":"trento","name":"Università degli Studi di Trento","base_url":"https://iris.unitn.it/oai/request"},"display":{"title":"A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Corradini, Andrea"],"institution":"Università degli studi di Trento","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Calandra Buonaura, Matteo","Marini, Giovanni"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-04-20","date_published":"2026-04-20","updated_at":"2026-07-24T05:04:22Z","subjects":["Machine learning interatomic potentials, liquid-liquid phase transitions, non-thermal melting, molecular dynamics, ultrafast photoexcitation, silicon, tellurium"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/11572/483910","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Corradini, Andrea","Calandra Buonaura, Matteo","Marini, Giovanni"]},{"key":"dc:creator","label":"Author","values":["Corradini, Andrea"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-04-20"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Trento","place:TRENTO"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:224","numberofpages:224"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning interatomic potentials, liquid-liquid phase transitions, non-thermal melting, molecular dynamics, ultrafast photoexcitation, silicon, tellurium"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11572/483910"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:title","label":"Title","values":["A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy"]}]}],"canonical_facts":{"dc:contributor":["Corradini, Andrea","Calandra Buonaura, Matteo","Marini, Giovanni"],"dc:creator":["Corradini, Andrea"],"dc:date":["2026-04-20"],"dc:description":["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."],"dc:identifier":["https://hdl.handle.net/11572/483910"],"dc:language":["eng"],"dc:publisher":["Università degli studi di Trento","place:TRENTO"],"dc:relation":["firstpage:1","lastpage:224","numberofpages:224"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Machine learning interatomic potentials, liquid-liquid phase transitions, non-thermal melting, molecular dynamics, ultrafast photoexcitation, silicon, tellurium"],"dc:title":["A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T05:04:22Z"}