2024
Ogoshi, Elton; Popolin-Neto, Mário; Acosta, Carlos Mera; Nascimento, Gabriel M.; Rodrigues, João N. B.; Oliveira, Osvaldo N.; Paulovich, Fernando V.; Dalpian, Gustavo M.
Learning from machine learning: the case of band-gap directness in semiconductors Journal Article
Em: Discov Mater, vol. 4, não 1, 2024, ISSN: 2730-7727.
@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},
urldate = {2024-12-00},
journal = {Discov Mater},
volume = {4},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {AbstractHaving 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 d 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.},
keywords = {General Medicine},
pubstate = {published},
tppubtype = {article}
}
<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>
Ogoshi, Elton; Popolin-Neto, Mário; Acosta, Carlos Mera; Nascimento, Gabriel M.; Rodrigues, João N. B.; Oliveira, Osvaldo N.; Paulovich, Fernando V.; Dalpian, Gustavo M.
Learning from machine learning: the case of band-gap directness in semiconductors Journal Article
Em: Discov Mater, vol. 4, não 1, 2024, ISSN: 2730-7727.
@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},
urldate = {2024-12-00},
journal = {Discov Mater},
volume = {4},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {AbstractHaving 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 d 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.},
keywords = {General Medicine},
pubstate = {published},
tppubtype = {article}
}
<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>
2023
Wheeler, William A.; Pathak, Shivesh; Kleiner, Kevin G.; Yuan, Shunyue; Rodrigues, João N. B.; Lorsung, Cooper; Krongchon, Kittithat; Chang, Yueqing; Zhou, Yiqing; Busemeyer, Brian; Williams, Kiel T.; Muñoz, Alexander; Chow, Chun Yu; Wagner, Lucas K.
PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF Journal Article
Em: vol. 158, não 11, 2023, ISSN: 1089-7690.
@article{Wheeler2023,
title = {PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF},
author = {William A. Wheeler and Shivesh Pathak and Kevin G. Kleiner and Shunyue Yuan and João N. B. Rodrigues and Cooper Lorsung and Kittithat Krongchon and Yueqing Chang and Yiqing Zhou and Brian Busemeyer and Kiel T. Williams and Alexander Muñoz and Chun Yu Chow and Lucas K. Wagner},
doi = {10.1063/5.0139024},
issn = {1089-7690},
year = {2023},
date = {2023-03-21},
volume = {158},
number = {11},
publisher = {AIP Publishing},
abstract = {We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.},
keywords = {General Physics and Astronomy, Physical and Theoretical Chemistry},
pubstate = {published},
tppubtype = {article}
}
We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.
Ogoshi, Elton; Ferreira, Henrique; Rodrigues, João N. B.; Dalpian, Gustavo M.
Exploring chemical compound space with a graph-based recommender system Working paper
2023.
@workingpaper{ogoshi2023exploring,
title = {Exploring chemical compound space with a graph-based recommender system},
author = {Elton Ogoshi and Henrique Ferreira and João N. B. Rodrigues and Gustavo M. Dalpian},
url = {https://arxiv.org/abs/2306.16496},
doi = {10.48550/arXiv.2306.16496},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
abstract = {With the availability of extensive databases of inorganic materials, data-driven approaches leveraging machine learning have gained prominence in materials science research. In this study, we propose an innovative adaptation of data-driven concepts to the mapping and exploration of chemical compound space. Recommender systems, widely utilized for suggesting items to users, employ techniques such as collaborative filtering, which rely on bipartite graphs composed of users, items, and their interactions. Building upon the Open Quantum Materials Database (OQMD), we constructed a bipartite graph where elements from the periodic table and sites within crystal structures are treated as separate entities. The relationships between them, defined by the presence of ions at specific sites and weighted according to the thermodynamic stability of the respective compounds, allowed us to generate an embedding space that contains vector representations for each ion and each site. Through the correlation of ion-site occupancy with their respective distances within the embedding space, we explored new ion-site occupancies, facilitating the discovery of novel stable compounds. Moreover, the graph's embedding space enabled a comprehensive examination of chemical similarities among elements, and a detailed analysis of local geometries of sites. To demonstrate the effectiveness and robustness of our method, we conducted a historical evaluation using different versions of the OQMD and recommended new compounds with Kagome lattices, showcasing the applicability of our approach to practical materials design.},
keywords = {Materials Science},
pubstate = {published},
tppubtype = {workingpaper}
}
With the availability of extensive databases of inorganic materials, data-driven approaches leveraging machine learning have gained prominence in materials science research. In this study, we propose an innovative adaptation of data-driven concepts to the mapping and exploration of chemical compound space. Recommender systems, widely utilized for suggesting items to users, employ techniques such as collaborative filtering, which rely on bipartite graphs composed of users, items, and their interactions. Building upon the Open Quantum Materials Database (OQMD), we constructed a bipartite graph where elements from the periodic table and sites within crystal structures are treated as separate entities. The relationships between them, defined by the presence of ions at specific sites and weighted according to the thermodynamic stability of the respective compounds, allowed us to generate an embedding space that contains vector representations for each ion and each site. Through the correlation of ion-site occupancy with their respective distances within the embedding space, we explored new ion-site occupancies, facilitating the discovery of novel stable compounds. Moreover, the graph’s embedding space enabled a comprehensive examination of chemical similarities among elements, and a detailed analysis of local geometries of sites. To demonstrate the effectiveness and robustness of our method, we conducted a historical evaluation using different versions of the OQMD and recommended new compounds with Kagome lattices, showcasing the applicability of our approach to practical materials design.
João Gabriel Nascimento Balbino
Fillyp Ramos Pereira