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
