FERRARIO, MAURO

	Nome completo
	
					FERRARIO, MAURO
				
	Afferenza
	
					DIFA - DIPARTIMENTO DI FISICA E ASTRONOMIA "AUGUSTO RIGHI"
				
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Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling

2025 Pacini, A.; Ferrario, M.; Righi, M. C.

Ab initio informed machine learning potential for tribochemistry and mechanochemistry: Application for eco–friendly gallate lubricant additive

2024 Ta, Huong T. T.; Ferrario, Mauro; Loehlé, Sophie; Righi, Maria Clelia

Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics

2024 Pacini, A.; Ferrario, M.; Loehle, S.; Righi, M. C.

In Operando Formation of Layered Materials for Friction Reduction

2024 Ferrario, Mauro; Righi, Maria Clelia

Titolo	Autore(i)	Anno	Periodico	Editore	Tipo	File
Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling	Pacini, A.; Ferrario, M.; Righi, M. C.	2025-01-01	JOURNAL OF CHEMICAL THEORY AND COMPUTATION	-	1.01 Articolo in rivista	25_JCTC_SCS.pdf
Ab initio informed machine learning potential for tribochemistry and mechanochemistry: Application for eco–friendly gallate lubricant additive	Ta, Huong T. T.; Ferrario, Mauro; Loehlé, Sophie; Righi, Maria Clelia	2024-01-01	COMPUTATIONAL MATERIALS TODAY	-	1.01 Articolo in rivista	1-s2.0-S295046352400005X-main-2.pdf; 1-s2.0-S295046352400005X-mmc1.docx
Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics	Pacini, A.; Ferrario, M.; Loehle, S.; Righi, M. C.	2024-01-01	THE EUROPEAN PHYSICAL JOURNAL PLUS	-	1.01 Articolo in rivista	s13360-024-05348-z.pdf
In Operando Formation of Layered Materials for Friction Reduction	Ferrario, Mauro; Righi, Maria Clelia	2024-01-01	-	Springer Cham	2.01 Capitolo / saggio in libro	-

CRIS Current Research Information System

FERRARIO, MAURO

Accelerating Data Set Population for Training Machine Learning Potentials with Automated System Generation and Strategic Sampling

Ab initio informed machine learning potential for tribochemistry and mechanochemistry: Application for eco–friendly gallate lubricant additive

Advancing tribological simulations of carbon-based lubricants with active learning and machine learning molecular dynamics

In Operando Formation of Layered Materials for Friction Reduction