Professor Alexandre Reily Rocha

@article{doi:10.1021/acs.jpcb.2c09059,

title = {Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water},

author = {Márcio S. Gomes-Filho and Alberto Torres and Alexandre Reily Rocha and Luana S. Pedroza},

url = {https://doi.org/10.1021/acs.jpcb.2c09059},

doi = {10.1021/acs.jpcb.2c09059},

year  = {2023},

date = {2023-01-01},

journal = {The Journal of Physical Chemistry B},

volume = {127},

number = {6},

pages = {1422-1428},

abstract = {Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT) and thus is limited to small systems and a relatively short simulation time. In this scenario, Neural Network Force Fields (NNFFs) have an important role, since they provide a way to circumvent these caveats. In this work, we investigate NNFFs designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data set considered. We show that structural properties are less dependent on the size of the training data set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for the training process) can lead to a small sample with good precision.},

note = {PMID: 36730848},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AMORIM2023112207,

title = {Gate voltage enhances the thermoelectric transport of quantum dots in graphene nanoribbons},

author = {Felippe P. Amorim and Alberto Torres and Cesar E. P. Villegas and Alexandre Reily Rocha},

url = {https://www.sciencedirect.com/science/article/pii/S092702562300201X},

doi = {https://doi.org/10.1016/j.commatsci.2023.112207},

issn = {0927-0256},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Computational Materials Science},

volume = {227},

pages = {112207},

abstract = {Chemically derived graphene nanoribbons and quantum dots are unique nanostructures that offer more possibilities than 2D and 3D systems to tune their electronic properties due to the enhanced quantum confinement effects. This feature make them potential candidates for many technological applications, including thermoelectrics. In this work, we combined density functional theory calculations with the non-equilibrium Green’s function formalism to investigate the electronic and thermoelectric properties of recently synthesized quantum dots in graphene nanoribbons under the presence of an applied gate voltage, and for different temperatures. We find that the electronic states at the band edge are highly localized in the inner region of the quantum dot, and can be lifted to higher energies by applying a gate voltage, which subsequently enhances figure of merit. Moreover, at zero gate voltage and room temperature, we estimate the lower bound for ZT to be approximately 0.25. Interestingly, this lower bound can exceed unity by smoothly increasing the gate voltage for values above 6 V. The overall results regarding the enhancement of ZT suggest that quantum dots in graphene nanoribbons would be promising candidates for thermoelectric applications.},

keywords = {7-14-7 AGNR, Gate voltage enhances, Quantum dots in graphene, Thermoelectric transport},

pubstate = {published},

tppubtype = {article}

}