Zobrazeno 1 - 10
of 482
pro vyhledávání: '"Roitberg, Adrian E."'
Publikováno v:
J. Chem. Phys. 151, 034113 (2019)
We show that the generalized Boltzmann distribution is the only distribution for which the Gibbs-Shannon entropy equals the thermodynamic entropy. This result means that the thermodynamic entropy and the Gibbs-Shannon entropy are not generally equal,
Externí odkaz:
http://arxiv.org/abs/1903.02121
Efficient computational methods that are capable of supporting experimental measures obtained at constant values of pH and redox potential are important tools as they serve to, among other things, provide additional atomic level information that cann
Externí odkaz:
http://arxiv.org/abs/1809.07726
Publikováno v:
The Journal of Chemical Physics 149, 072338 (2018)
Redox processes are important in chemistry, with applications in biomedicine, chemical analysis, among others. As many redox experiments are also performed at a fixed value of pH, having an efficient computational method to support experimental measu
Externí odkaz:
http://arxiv.org/abs/1803.09652
Publikováno v:
J. Chem. Phys. 148, 241733 (2018)
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in de
Externí odkaz:
http://arxiv.org/abs/1801.09319
Publikováno v:
The Australian Journal of Mathematical Analysis and Applications, Volume 16, Issue 2, Article 14, pp. 1-16, 2019
This paper gives upper and lower bounds on the gap in Jensen's inequality, i.e., the difference between the expected value of a function of a random variable and the value of the function at the expected value of the random variable. The bounds depen
Externí odkaz:
http://arxiv.org/abs/1712.05267
Publikováno v:
Scientific Data 4, Article number: 170193 (2017)
One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML), in particular neural networks, are emerging as a powerful approa
Externí odkaz:
http://arxiv.org/abs/1708.04987
Akademický článek
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Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accura
Externí odkaz:
http://arxiv.org/abs/1610.08935
Autor:
Bueno, Paulo Roberto, Cruzeiro, Vinícius Wilian D., Roitberg, Adrian E., Feliciano, Gustavo T.
Publikováno v:
In Electrochimica Acta 10 August 2021 387
Akademický článek
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