Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Rafael Gómez-Bombarelli"'
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-10 (2024)
Abstract Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving comp
Externí odkaz:
https://doaj.org/article/8256ee2f7ea24ffb8ae80b39ab72729a
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations
Externí odkaz:
https://doaj.org/article/209b5859bf5748e3a70be35b67a6f5ab
Autor:
Jaclyn R. Lunger, Jessica Karaguesian, Hoje Chun, Jiayu Peng, Yitong Tseo, Chung Hsuan Shan, Byungchan Han, Yang Shao-Horn, Rafael Gómez-Bombarelli
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-11 (2024)
Abstract Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of
Externí odkaz:
https://doaj.org/article/bbb80697d9ad4e3c9f9500aaf1f16eaa
Autor:
Nicholas L. Truex, Somesh Mohapatra, Mariane Melo, Jacob Rodriguez, Na Li, Wuhbet Abraham, Deborah Sementa, Faycal Touti, Derin B. Keskin, Catherine J. Wu, Darrell J. Irvine, Rafael Gómez-Bombarelli, Bradley L. Pentelute
Publikováno v:
ACS Central Science, Vol 10, Iss 4, Pp 793-802 (2024)
Externí odkaz:
https://doaj.org/article/99dad73fc208488c99c976dab55c812a
Autor:
Elton Pan, Soonhyoung Kwon, Zach Jensen, Mingrou Xie, Rafael Gómez-Bombarelli, Manuel Moliner, Yuriy Román-Leshkov, Elsa Olivetti
Publikováno v:
ACS Central Science, Vol 10, Iss 3, Pp 729-743 (2024)
Externí odkaz:
https://doaj.org/article/9b394d87c2224412bcbe1dd5bb6a7fd3
Autor:
Aik Rui Tan, Shingo Urata, Samuel Goldman, Johannes C. B. Dietschreit, Rafael Gómez-Bombarelli
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-11 (2023)
Abstract Neural networks (NNs) often assign high confidence to their predictions, even for points far out of distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems
Externí odkaz:
https://doaj.org/article/677a1abb88db403294eb8bb981b914cb
Autor:
Pau Ferri, Chengeng Li, Daniel Schwalbe-Koda, Mingrou Xie, Manuel Moliner, Rafael Gómez-Bombarelli, Mercedes Boronat, Avelino Corma
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-13 (2023)
Abstract Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl
Externí odkaz:
https://doaj.org/article/2088028ace0041b8983d43e4dcc7b949
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
The authors introduce a diabatic neural network to accelerate excitedstate, non-adiabatic simulations of azobenzene derivatives. The model predicts quantum yields for unseen species that are correlated with experiment.
Externí odkaz:
https://doaj.org/article/945d7acb1c6f4f87834bd6f01e69d0fe
Autor:
Simon Axelrod, Rafael Gómez-Bombarelli
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-14 (2022)
Measurement(s) Conformer geometries and properties Technology Type(s) Computational Chemistry
Externí odkaz:
https://doaj.org/article/fbe9f162fb3a46a8b101637d2c0a8a1b
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-10 (2022)
Abstract Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often
Externí odkaz:
https://doaj.org/article/aad8204d3665426fb622efd1a727c0ef