Carlos Mera Acosta

@article{Franca2026,

title = {Dimensionality‐Driven Emerging Phenomena in Hybrid Metal Halides},

author = {José R. Franca and Lucas A. R. Santana and Cássio C. S. Soares and Carlos Mera Acosta and Gustavo M. Dalpian and Jose Antonio Souza},

doi = {10.1002/sstr.202600018},

issn = {2688-4062},

year  = {2026},

date = {2026-06-08},

journal = {Small Structures},

volume = {7},

number = {6},

publisher = {Wiley},

abstract = {<jats:p>Hybrid metal halides are unique among semiconductors in that their inorganic octahedral networks can be rationally assembled in 3D, 2D, 1D, and 0D forms, enabling dimensionality to assist as a powerful control knob over structure, electronic properties, excitations, and stability. This perspective unifies these systems under a connectivity‐based framework and examines how reducing dimensionality reshapes quantum confinement, dielectric screening, structural anisotropy, exciton binding and interaction, lattice distortions, electron–phonon coupling, and spin–orbit interactions, giving rise to a set of emergent physical phenomena: self‐trapped excitons and polarons, Rashba–Dresselhaus spin splitting, photon recycling and energy funneling, and spin/polar ferroic orders. Together, these phenomena reveal how dimensionality serves not merely as a structural descriptor but as a design principle that governs collective excitations and couplings among charge, spin, and lattice degrees of freedom. We critically assess, challenge, and highlight the importance of understanding emergent behavior in transitioning from phenomenology to the rational design of next‐generation optoelectronic, spintronic, and quantum devices based on dimensionality‐engineered halide perovskites.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sandoval2025,

title = {Orbital filtering in edge-state transport of PbSe nanoribbons},

author = {M. A. Toloza Sandoval and R. L. H. Freire and A. L. Araújo and Carlos Mera Acosta and F. Crasto Lima and A. Fazzio},

doi = {10.1103/ttfc-9z7d},

issn = {2475-9953},

year  = {2025},

date = {2025-09-02},

journal = {Phys. Rev. Materials},

volume = {9},

number = {9},

publisher = {American Physical Society (APS)},

abstract = {Topological insulators are expected to exhibit robust edge-state transport protected by time-reversal symmetry, yet experimental conductance often falls below the quantized limit due to intrinsic and interfacial effects. Here, we demonstrate that the orbital character of topological edge states, governed by spin-orbit coupling and edge termination, plays a critical role in electron injection and transport. Using first-principles calculations combined with nonequilibrium Green's function methods, we investigate PbSe nanoribbons as a model system. We identify an orbital filtering mechanism arising from spin-orbital-entangled edge states, where the 𝑗=1/2 and 𝑗=3/2 components of the 𝑝 orbitals obey symmetry-imposed selection rules at the interface. This effect limits conductance even in the absence of disorder; when present, disorder further promotes an interplay between topological edge states and resonant edge-localized states originating from dangling bonds. Our findings highlight the importance of orbital engineering and angular momentum symmetry mismatch in the design of topological devices and call for an examination of interface physics in quantum materials regarding the topological protection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sandoval2025b,

title = {Orbital filtering in edge-state transport of PbSe nanoribbons},

author = {M. A. Toloza Sandoval and R. L. H. Freire and A. L. Araújo and Carlos Mera Acosta and F. Crasto Lima and A. Fazzio},

doi = {10.1103/ttfc-9z7d},

issn = {2475-9953},

year  = {2025},

date = {2025-09-01},

journal = {Phys. Rev. Materials},

volume = {9},

number = {9},

publisher = {American Physical Society (APS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ogoshi2024,

title = {Learning from machine learning: the case of band-gap directness in semiconductors},

author = {Elton Ogoshi and Mário Popolin-Neto and Carlos Mera Acosta and Gabriel M. Nascimento and João N. B. Rodrigues and Osvaldo N. Oliveira and Fernando V. Paulovich and Gustavo M. Dalpian},

url = {https://link.springer.com/article/10.1007/s43939-024-00073-x},

doi = {10.1007/s43939-024-00073-x},

issn = {2730-7727},

year  = {2024},

date = {2024-02-29},

journal = {Discov Mater},

volume = {4},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>Having a direct or indirect band gap can influence the potential applications of a semiconductor, for indirect band gap materials are usually not suitable for optoelectronic devices. Even though this is a fundamental property of semiconducting materials, discussed in textbooks, no unified theory exists to explain why a material has a direct or indirect band gap. Here we used an interpretable machine learning model, the multiVariate dAta eXplanation (VAX) method, to gather information from a dataset of materials extracted from the Materials Project. The dataset contains more than 10000 entries, and atomic properties such as the number of electrons, electronic affinity and orbital energies were used as features to build random forest models that successfully explain the directness of the band gaps. Our results indicate that symmetry is an important feature that dictates the target property, which is the reason why our analysis is made based on sub-groups with similar structures. These sub-groups include materials with zincblende, rocksalt, wurtzite, and perovskite structures. Besides the symmetry of the materials, the existence or not of <jats:italic>d</jats:italic> bands and the relative energy of atomic orbitals were found to be important in defining whether a material’s band gap is direct or indirect. In conclusion, interpretable machine learning methods such as VAX can be useful in obtaining physical interpretation from materials databases.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ogoshi2024b,

title = {Learning from machine learning: the case of band-gap directness in semiconductors},

author = {Elton Ogoshi and Mário Popolin-Neto and Carlos Mera Acosta and Gabriel M. Nascimento and João N. B. Rodrigues and Osvaldo N. Oliveira and Fernando V. Paulovich and Gustavo M. Dalpian},

url = {https://repositorio.usp.br/directbitstream/1ee44f9f-13de-45ad-bb4b-fb7f0cb95272/3183025%20falta%20rep.pdf},

doi = {10.1007/s43939-024-00073-x},

issn = {2730-7727},

year  = {2024},

date = {2024-02-29},

journal = {Discov Mater},

volume = {4},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>Having a direct or indirect band gap can influence the potential applications of a semiconductor, for indirect band gap materials are usually not suitable for optoelectronic devices. Even though this is a fundamental property of semiconducting materials, discussed in textbooks, no unified theory exists to explain why a material has a direct or indirect band gap. Here we used an interpretable machine learning model, the multiVariate dAta eXplanation (VAX) method, to gather information from a dataset of materials extracted from the Materials Project. The dataset contains more than 10000 entries, and atomic properties such as the number of electrons, electronic affinity and orbital energies were used as features to build random forest models that successfully explain the directness of the band gaps. Our results indicate that symmetry is an important feature that dictates the target property, which is the reason why our analysis is made based on sub-groups with similar structures. These sub-groups include materials with zincblende, rocksalt, wurtzite, and perovskite structures. Besides the symmetry of the materials, the existence or not of <jats:italic>d</jats:italic> bands and the relative energy of atomic orbitals were found to be important in defining whether a material’s band gap is direct or indirect. In conclusion, interpretable machine learning methods such as VAX can be useful in obtaining physical interpretation from materials databases.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{YADAV20232711,

title = {First-principles investigations of 2D materials: Challenges and best practices},

author = {Asha Yadav and Carlos Mera Acosta and Gustavo M. Dalpian and Oleksandr I. Malyi},

url = {https://www.sciencedirect.com/science/article/pii/S2590238523002370},

doi = {https://doi.org/10.1016/j.matt.2023.05.019},

issn = {2590-2385},

year  = {2023},

date = {2023-01-01},

journal = {Matter},

volume = {6},

number = {9},

pages = {2711-2734},

abstract = {Summary 

The successful exfoliation of graphene from graphite has brought significant attention to predicting new two-dimensional (2D) materials that can be realized experimentally. As a consequence, first-principles studies of novel 2D materials become routine, with thousands of papers published every year. What makes these studies interesting is that they predict new materials that have not been realized yet but should be a panacea for topological insulators, next-generation battery electrodes, novel solar cell absorbers, etc. There is no doubt that some of the proposed materials can provide a specific solution, and their properties/performance can be confirmed experimentally. At the same time, there are many false predictions because of the computational errors or the Legoland approach used to study 2D materials. To reduce the gap between theoretical and experimental works, we performed a systematic review of computational and Legoland factors that should be minimized in future theoretical works.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}