Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav; Hautier, Geoffroy; Kocher, Michael; Cholia, Shreyas; Gunter, Dan; Chevrier, Vincent L.; Persson, Kristin A.; Ceder, Gerbrand

doi:10.1016/j.commatsci.2012.10.028

Title: Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Journal Article · Sat Dec 08 00:00:00 EST 2012 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2012.10.028· OSTI ID:1511345

Ong, Shyue Ping ^[1]; Richards, William Davidson ^[1]; Jain, Anubhav ^[2]; Hautier, Geoffroy ^[3]; Kocher, Michael ^[2]; Cholia, Shreyas ^[2]; Gunter, Dan ^[2]; Chevrier, Vincent L. ^[4]; Persson, Kristin A. ^[2]; Ceder, Gerbrand ^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. catholique de Louvain, Louvain-La-Neuve (Belgium)
3M, St. Paul, MN (United States)

We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials analysis. A key enabler in high-throughput computational materials science efforts is a robust set of software tools to perform initial setup for the calculations (e.g., generation of structures and necessary input files) and post-calculation analysis to derive useful material properties from raw calculated data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodynamic analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calculations and experiments. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project's REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen's interface to the Materials Project's RESTful API and phasediagram package, we demonstrate how the phase and electrochemical stability of a recently synthesized material, Li₄SnS₄, can be analyzed using a minimum of computing resources. Here, we find that Li₄SnS₄ is a stable phase in the Li-Sn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chemical potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1511345

Journal Information:: Computational Materials Science, Vol. 68, Issue C; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Catalyst synthesis under CO2 electroreduction favours faceting and promotes renewable fuels electrosynthesis Wang, Yuhang; Wang, Ziyun; Dinh, Cao-Thang Nature Catalysis, Vol. 3, Issue 2 https://doi.org/10.1038/s41929-019-0397-1	journal	December 2019
Systematic exploration of the mechanical properties of 13 621 inorganic compounds Chibani, Siwar; Coudert, François-Xavier Chemical Science, Vol. 10, Issue 37 https://doi.org/10.1039/c9sc01682a	journal	January 2019
Proposed definition of crystal substructure and substructural similarity Yang, Lusann; Dacek, Stephen; Ceder, Gerbrand Physical Review B, Vol. 90, Issue 5 https://doi.org/10.1103/physrevb.90.054102	journal	August 2014
Method to quantify the delocalization of electronic states in amorphous semiconductors and its application to assessing charge carrier mobility of $p$ -type amorphous oxide semiconductors de Jamblinne de Meux, A.; Pourtois, G.; Genoe, J. Physical Review B, Vol. 97, Issue 4 https://doi.org/10.1103/physrevb.97.045208	journal	January 2018
Reversible High Capacity and Reaction Mechanism of Cr 2 (NCN) 3 Negative Electrodes for Li-Ion Batteries Arayamparambil, Jeethu Jiju; Chen, Kaixuan; Iadecola, Antonella RWTH Aachen University https://doi.org/10.18154/rwth-2020-04806	text	January 2020
Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors Cao, Zhuo; Dan, Yabo; Xiong, Zheng Crystals, Vol. 9, Issue 4 https://doi.org/10.3390/cryst9040191	journal	April 2019
High-Throughput ab-initio Dilute Solute Diffusion Database Wu, Henry; Mayeshiba, Tam; Morgan, Dane arXiv https://doi.org/10.48550/arxiv.1602.01725	text	January 2016
ChemTS: An Efficient Python Library for de novo Molecular Generation Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki arXiv https://doi.org/10.48550/arxiv.1710.00616	text	January 2017
Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra Zheng, Chen; Mathew, Kiran; Chen, Chi arXiv https://doi.org/10.48550/arxiv.1711.02227	preprint	January 2017
Deep Neural Networks for Accurate Predictions of Garnet Stability Ye, Weike; Chen, Chi; Wang, Zhenbin arXiv https://doi.org/10.48550/arxiv.1712.01908	text	January 2017
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Zhang, Linfeng; Lin, De-Ye; Wang, Han arXiv https://doi.org/10.48550/arxiv.1810.11890	text	January 2018
Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High-Efficiency Solar Cells Jacobs, Ryan; Luo, Guangfu; Morgan, Dane arXiv https://doi.org/10.48550/arxiv.1904.05690	text	January 2019
High-throughput computational screening for solid-state Li-ion conductors Kahle, Leonid; Marcolongo, Aris; Marzari, Nicola arXiv https://doi.org/10.48550/arxiv.1909.00623	text	January 2019
Crystal structure representations for machine learning models of formation energies Felix, Faber,; Alexander, Lindmaa,; Anatole, von Lilienfeld, O. Wiley https://doi.org/10.5451/unibas-ep53264	text	January 2015
Computationally Guided Discovery of the Sulfide Li₃AlS₃ in the Li–Al–S Phase Field: Structure and Lithium Conductivity Gamon, Jacinthe; Duff, Benjamin B.; Dyer, Matthew S. Chemistry of Materials, Vol. 31, Issue 23 https://doi.org/10.1021/acs.chemmater.9b03230	journal	October 2019
Identifying the Origins of Vacancies in the Crystal Structures of Rock Salt-type Chalcogenide Superconductors Simons, Jasmin; Steinberg, Simon ACS Omega, Vol. 4, Issue 13 https://doi.org/10.1021/acsomega.9b02368	journal	September 2019
First-Principles-Based Force Field for 2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105) Wang, Xian; Zeng, Qun; Li, Jinshan ACS Omega, Vol. 4, Issue 25 https://doi.org/10.1021/acsomega.9b02410	journal	December 2019
High potential thermoelectric figure of merit in ternary La3Cu3X4 (X = P, As, Sb and Bi) compounds Pandey, Tribhuwan; Parker, David S. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-14658-5	journal	October 2017
Aqueous Stability of Alkali Superionic Conductors from First-Principles Calculations Radhakrishnan, Balachandran; Ong, Shyue Ping Frontiers in Energy Research, Vol. 4 https://doi.org/10.3389/fenrg.2016.00016	journal	April 2016
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry Bartel, Christopher J.; Millican, Samantha L.; Deml, Ann M. arXiv https://doi.org/10.48550/arxiv.1805.08155	text	January 2018
Evaluation of Mg compounds as coating materials in Mg batteries Chen, Tina; Ceder, Gerbrand; Gopalakrishnan, Sai Gautam arXiv https://doi.org/10.48550/arxiv.1902.01925	text	January 2019

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