000135310 001__ 135310
000135310 005__ 20241125101153.0
000135310 0247_ $$2doi$$a10.1021/acs.jpcc.3c04308
000135310 0248_ $$2sideral$$a136445
000135310 037__ $$aART-2023-136445
000135310 041__ $$aeng
000135310 100__ $$0(orcid)0000-0002-4931-1358$$aUrbiztondo Castro, Miguel A.
000135310 245__ $$aDeep Learning-Based Energy Mapping of Chlorine Effects in an Epoxidation Reaction Catalyzed by a Silver–Copper Oxide Nanocatalyst
000135310 260__ $$c2023
000135310 5203_ $$aDeep learning is poised to revolutionize the field of heterogeneous catalysis. In this study, we harness its potential to predict energy values across a catalyst surface, a task traditionally relegated to computationally intensive density functional theory (DFT). We propose a novel deep learning approach to construct an exhaustive energy map, pinpointing the optimal locations for adsorbed chlorine in the ethylene epoxidation reaction. Leveraging the power of trained neural networks, we achieved a staggering reduction in computational time, cutting down the duration of energy calculations by over 50 million times compared with traditional methods. This groundbreaking integration of artificial intelligence not only accelerates this process but also effectively surpasses the limitations of conventional methods. By highlighting the transformative potential of deep learning in catalysis, this research paves the way for future studies and stands to revolutionize efficiency in the chemical industry, fostering an urgent need to delve deeper into the implications and applications of this technology.
000135310 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T57-17R$$9info:eu-repo/grantAgreement/ES/DGA/Q-MAD$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2020-115221GB-C41$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-110430GB-C22
000135310 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000135310 590__ $$a3.3$$b2023
000135310 592__ $$a0.957$$b2023
000135310 591__ $$aCHEMISTRY, PHYSICAL$$b82 / 178 = 0.461$$c2023$$dQ2$$eT2
000135310 593__ $$aPhysical and Theoretical Chemistry$$c2023$$dQ1
000135310 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b196 / 439 = 0.446$$c2023$$dQ2$$eT2
000135310 593__ $$aElectronic, Optical and Magnetic Materials$$c2023$$dQ1
000135310 591__ $$aNANOSCIENCE & NANOTECHNOLOGY$$b79 / 141 = 0.56$$c2023$$dQ3$$eT2
000135310 593__ $$aSurfaces, Coatings and Films$$c2023$$dQ1
000135310 593__ $$aEnergy (miscellaneous)$$c2023$$dQ2
000135310 593__ $$aNanoscience and Nanotechnology$$c2023$$dQ2
000135310 594__ $$a6.5$$b2023
000135310 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135310 700__ $$0(orcid)0000-0001-6575-168X$$aGutíerrez Rodrigo, Sergio$$uUniversidad de Zaragoza
000135310 700__ $$aHamad, Said
000135310 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000135310 773__ $$g127, 44 (2023), 21534-21543$$pJ. phys. chem., C$$tJournal of physical chemistry. C.$$x1932-7447
000135310 8564_ $$s6477472$$uhttps://zaguan.unizar.es/record/135310/files/texto_completo.pdf$$yVersión publicada
000135310 8564_ $$s2960844$$uhttps://zaguan.unizar.es/record/135310/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135310 909CO $$ooai:zaguan.unizar.es:135310$$particulos$$pdriver
000135310 951__ $$a2024-11-22-12:08:03
000135310 980__ $$aARTICLE