Photovoltaic energy represents a keystone for the transition to renewable energy. A crucial point in the design of new technologies is represented by the optimization of solar cell structures. Here we review the main results obtained using multiobjective optimization algorithms, machine learning techniques, and new perspectives given by organic solar cells.
High-Performance Solar Cells by Machine Learning and Pareto Optimality
	
	
	
		
		
		
		
		
	
	
	
	
	
	
	
	
		
		
		
		
		
			
			
			
		
		
		
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
			
			
				
				
					
					
					
					
						
							
						
						
					
				
				
				
				
				
				
				
				
				
				
				
			
			
		
		
		
		
	
Nastasi, Giovanni
;Romano, Vittorio;Nicosia, Giuseppe
			2022-01-01
Abstract
Photovoltaic energy represents a keystone for the transition to renewable energy. A crucial point in the design of new technologies is represented by the optimization of solar cell structures. Here we review the main results obtained using multiobjective optimization algorithms, machine learning techniques, and new perspectives given by organic solar cells.File in questo prodotto:
	
	
	
    
	
	
	
	
	
	
	
	
		
			
				
			
		
		
	
	
	
	
		
		
			| File | Dimensione | Formato | |
|---|---|---|---|
| 
									
										
										
										
										
											
												
												
												    
												
											
										
									
									
										
										
											NaRoNi_Springer.pdf
										
																				
									
										
											 solo gestori archivio 
											Tipologia:
											Versione Editoriale (PDF)
										 
									
									
									
									
										
											Licenza:
											
											
												NON PUBBLICO - Accesso privato/ristretto
												
												
												
											
										 
									
									
										Dimensione
										402.77 kB
									 
									
										Formato
										Adobe PDF
									 
										
										
								 | 
								402.77 kB | Adobe PDF | Visualizza/Apri | 
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


