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"""Configuration variables for ElementEmbeddings."""
from __future__ import annotations
DEFAULT_ELEMENT_EMBEDDINGS = {
"magpie": "magpie.csv",
"magpie_sc": "magpie_sc.json",
"mat2vec": "mat2vec.csv",
"matscholar": "matscholar-embedding.json",
"megnet16": "megnet16.json",
"mod_petti": "mod_petti.json",
"oliynyk": "oliynyk_preprocessed.csv",
"oliynyk_sc": "oliynyk_sc.json",
"random_200": "random_200_new.csv",
"skipatom": "skipatom_20201009_induced.csv",
"atomic": "atomic.json",
"crystallm": "crystallm_v24c.dim512_atom_vectors.csv",
"xenonpy": "xenonpy_element_features.csv",
"cgnf": "cgnf.json",
"mace_mp0": "mace_mp0.csv",
"sevennet": "sevennet.csv",
"orb_v2": "orb_v2.csv",
"chgnet": "chgnet.csv",
"matscibert": "matscibert.csv",
"chemeleon": "chemeleon.csv",
}
DEFAULT_SPECIES_EMBEDDINGS = {
"skipspecies": "skipspecies_2022_10_28_dim200.csv",
"skipspecies_induced": "skipspecies_2022_10_28_induced_dim200.csv",
}
CITATIONS = {
"magpie": [
"@article{ward2016general,"
"title={A general-purpose machine learning framework for "
"predicting properties of inorganic materials},"
"author={Ward, Logan and Agrawal, Ankit and Choudhary, Alok "
"and Wolverton, Christopher},"
"journal={npj Computational Materials},"
"volume={2},"
"number={1},"
"pages={1--7},"
"year={2016},"
"publisher={Nature Publishing Group}}",
],
"magpie_sc": [
"@article{ward2016general,"
"title={A general-purpose machine learning framework for "
"predicting properties of inorganic materials},"
"author={Ward, Logan and Agrawal, Ankit and Choudhary, Alok "
"and Wolverton, Christopher},"
"journal={npj Computational Materials},"
"volume={2},"
"number={1},"
"pages={1--7},"
"year={2016},"
"publisher={Nature Publishing Group}}",
],
"mat2vec": [
"@article{tshitoyan2019unsupervised,"
"title={Unsupervised word embeddings capture latent knowledge "
"from materials science literature},"
"author={Tshitoyan, Vahe and Dagdelen, John and Weston, Leigh "
"and Dunn, Alexander and Rong, Ziqin and Kononova, Olga "
"and Persson, Kristin A and Ceder, Gerbrand and Jain, Anubhav},"
"journal={Nature},"
"volume={571},"
"number={7763},"
"pages={95--98},"
"year={2019},"
"publisher={Nature Publishing Group} }",
],
"matscholar": [
"@article{weston2019named,"
"title={Named entity recognition and normalization applied to "
"large-scale information extraction from the materials "
"science literature},"
"author={Weston, Leigh and Tshitoyan, Vahe and Dagdelen, John and "
"Kononova, Olga and Trewartha, Amalie and Persson, Kristin A and "
"Ceder, Gerbrand and Jain, Anubhav},"
"journal={Journal of chemical information and modeling},"
"volume={59},"
"number={9},"
"pages={3692--3702},"
"year={2019},"
"publisher={ACS Publications} }",
],
"megnet16": [
"@article{chen2019graph,"
"title={Graph networks as a universal machine learning framework "
"for molecules and crystals},"
"author={Chen, Chi and Ye, Weike and Zuo, Yunxing and "
"Zheng, Chen and Ong, Shyue Ping},"
"journal={Chemistry of Materials},"
"volume={31},"
"number={9},"
"pages={3564--3572},"
"year={2019},"
"publisher={ACS Publications} }",
],
"oliynyk": [
" @article{oliynyk2016high,"
"title={High-throughput machine-learning-driven synthesis "
"of full-Heusler compounds},"
"author={Oliynyk, Anton O and Antono, Erin and Sparks, Taylor D and "
"Ghadbeigi, Leila and Gaultois, Michael W and "
"Meredig, Bryce and Mar, Arthur},"
"journal={Chemistry of Materials},"
"volume={28},"
"number={20},"
"pages={7324--7331},"
"year={2016},"
"publisher={ACS Publications} }",
],
"oliynyk_sc": [
" @article{oliynyk2016high,"
"title={High-throughput machine-learning-driven synthesis "
"of full-Heusler compounds},"
"author={Oliynyk, Anton O and Antono, Erin and Sparks, Taylor D and "
"Ghadbeigi, Leila and Gaultois, Michael W and "
"Meredig, Bryce and Mar, Arthur},"
"journal={Chemistry of Materials},"
"volume={28},"
"number={20},"
"pages={7324--7331},"
"year={2016},"
"publisher={ACS Publications} }",
],
"skipatom": [
"@article{antunes2022distributed,"
"title={Distributed representations of atoms and materials "
"for machine learning},"
"author={Antunes, Luis M and Grau-Crespo, Ricardo and Butler, Keith T},"
"journal={npj Computational Materials},"
"volume={8},"
"number={1},"
"pages={1--9},"
"year={2022},"
"publisher={Nature Publishing Group} }",
],
"mod_petti": [
"@article{glawe2016optimal,"
"title={The optimal one dimensional periodic table: "
"a modified Pettifor chemical scale from data mining},"
"author={Glawe, Henning and Sanna, Antonio and Gross, "
"EKU and Marques, Miguel AL},"
"journal={New Journal of Physics},"
"volume={18},"
"number={9},"
"pages={093011},"
"year={2016},"
"publisher={IOP Publishing} }",
],
"crystallm": [
"@article{antunes2023crystal,"
"title={Crystal structure generation "
"with autoregressive large language modeling},"
"author={Antunes, Luis M and Butler, Keith T and Grau-Crespo, Ricardo},"
"journal={arXiv preprint arXiv:2307.04340},"
"year={2023}}",
],
"xenonpy": [
"@article{liu2021machine,"
"title={Machine learning to predict quasicrystals from chemical compositions},"
"author={Liu, Chang and Fujita, Erina and "
"Katsura, Yukari and Inada, Yuki and Ishikawa, Asuka and "
"Tamura, Ryuji and Kimura, Kaoru and Yoshida, Ryo},"
"journal={Advanced Materials},"
"volume={33},"
"number={36},"
"pages={2102507},"
"year={2021},"
"publisher={Wiley Online Library}"
"}",
"@article{kusaba2022crystal,"
"title={Crystal structure prediction with machine "
"learning-based element substitution},"
"author={Kusaba, Minoru and Liu, Chang and Yoshida, Ryo},"
"journal={Computational Materials Science},"
"volume={211},"
"pages={111496},"
"year={2022},"
"publisher={Elsevier}"
"}",
"@article{kusaba2023representation,"
"title={Representation of materials by kernel mean embedding},"
"author={Kusaba, Minoru and Hayashi, Yoshihiro and "
"Liu, Chang and Wakiuchi, Araki and Yoshida, Ryo},"
"journal={Physical Review B},"
"volume={108},"
"number={13},"
"pages={134107},"
"year={2023},"
"publisher={APS}"
"}",
],
"cgnf": [
"@article{jang2024synthesizability,"
"title={Synthesizability of materials stoichiometry "
"using semi-supervised learning},"
"author={Jang, Jidon and Noh, Juhwan and Zhou, Lan "
"and Gu, Geun Ho and Gregoire, John M and Jung, Yousung},"
"journal={Matter},"
"volume={7},"
"number={6},"
"pages={2294--2312},"
"year={2024}",
],
"mace_mp0": [
"@article{batatia2024foundation,"
"title={A foundation model for atomistic materials chemistry},"
"author={Batatia, Ilyes and Benner, Philipp and Chiang, Yuan "
"and Elena, Alin M. and Kov{\\'a}cs, D{\\'a}vid P. "
"and Riebesell, Janosh and others},"
"journal={arXiv preprint arXiv:2401.00096},"
"year={2024}}",
],
"sevennet": [
"@article{park2024scalable,"
"title={Scalable parallel algorithm for graph neural network "
"interatomic potentials in molecular dynamics simulations},"
"author={Park, Yutack and Kim, Jaesun and Hwang, Seungwoo "
"and Han, Seungwu},"
"journal={arXiv preprint arXiv:2402.03789},"
"year={2024}}",
],
"orb_v2": [
"@article{neumann2024orb,"
"title={ORB: A Fast, Scalable Neural Network Potential},"
"author={Neumann, Mark and Gin, James and Rhodes, Benjamin "
"and Bennett, Steven and Li, Zhiyi and Choubisa, Hitarth "
"and Hussey, Arthur and Godwin, Jonathan},"
"journal={arXiv preprint arXiv:2410.22570},"
"year={2024}}",
],
"chgnet": [
"@article{deng2023chgnet,"
"title={{CHGNet} as a pretrained universal neural network potential "
"for charge-informed atomistic modelling},"
"author={Deng, Bowen and Zhong, Peichen and Jun, KyuJung and "
"Riebesell, Janosh and Han, Kevin and Bartel, Christopher J "
"and Ceder, Gerbrand},"
"journal={Nature Machine Intelligence},"
"volume={5},"
"number={9},"
"pages={1031--1041},"
"year={2023},"
"publisher={Nature Publishing Group}}",
],
"matscibert": [
"@article{gupta2022matscibert,"
"title={{MatSciBERT}: A materials domain language model for text "
"mining and information extraction},"
"author={Gupta, Tanishq and Zaki, Mohd and Krishnan, NM Anoop "
"and Mausam},"
"journal={npj Computational Materials},"
"volume={8},"
"number={1},"
"pages={102},"
"year={2022},"
"publisher={Nature Publishing Group}}",
],
"chemeleon": [
"@article{park2025chemeleon,"
"title={Crystal structure generation and property optimization "
"using a generative graph neural network},"
"author={Park, Hyunsoo and Onwuli, Anthony O. and Walsh, Aron},"
"journal={Nature Communications},"
"volume={16},"
"pages={4869},"
"year={2025},"
"doi={10.1038/s41467-025-59636-y},"
"publisher={Nature Publishing Group}}",
],
"skipspecies": [
"@article{Onwuli_Butler_Walsh_2024, "
"title={Ionic species representations for materials informatics}, "
"DOI={10.26434/chemrxiv-2024-8621l}, "
"journal={ChemRxiv}, "
"author={Onwuli, Anthony and Butler, Keith T. and Walsh, Aron}, year={2024}} "
"This content is a preprint and has not been peer-reviewed.",
"@article{antunes2022distributed,"
"title={Distributed representations of atoms and materials "
"for machine learning},"
"author={Antunes, Luis M and Grau-Crespo, Ricardo and Butler, Keith T},"
"journal={npj Computational Materials},"
"volume={8},"
"number={1},"
"pages={1--9},"
"year={2022},"
"publisher={Nature Publishing Group} }",
],
"skipspecies_induced": [
"@article{Onwuli_Butler_Walsh_2024, "
"title={Ionic species representations for materials informatics}, "
"DOI={10.26434/chemrxiv-2024-8621l}, "
"journal={ChemRxiv}, "
"author={Onwuli, Anthony and Butler, Keith T. and Walsh, Aron}, year={2024}} "
"This content is a preprint and has not been peer-reviewed.",
"@article{antunes2022distributed,"
"title={Distributed representations of atoms and materials "
"for machine learning},"
"author={Antunes, Luis M and Grau-Crespo, Ricardo and Butler, Keith T},"
"journal={npj Computational Materials},"
"volume={8},"
"number={1},"
"pages={1--9},"
"year={2022},"
"publisher={Nature Publishing Group} }",
],
}
ELEMENT_GROUPS_PALETTES = {
"Alkali": "tab:blue",
"Alkaline": "tab:cyan",
"Lanthanoid": "tab:purple",
"TM": "tab:orange",
"Post-TM": "tab:green",
"Metalloid": "tab:pink",
"Halogen": "tab:red",
"Noble gas": "tab:olive",
"Chalcogen": "tab:brown",
"Others": "tab:gray",
"Actinoid": "thistle",
}
X = {
"H": 2.2,
"He": 1.63,
"Li": 0.98,
"Be": 1.57,
"B": 2.04,
"C": 2.55,
"N": 3.04,
"O": 3.44,
"F": 3.98,
"Ne": 1.63,
"Na": 0.93,
"Mg": 1.31,
"Al": 1.61,
"Si": 1.9,
"P": 2.19,
"S": 2.58,
"Cl": 3.16,
"Ar": 1.63,
"K": 0.82,
"Ca": 1.0,
"Sc": 1.36,
"Ti": 1.54,
"V": 1.63,
"Cr": 1.66,
"Mn": 1.55,
"Fe": 1.83,
"Co": 1.88,
"Ni": 1.91,
"Cu": 1.9,
"Zn": 1.65,
"Ga": 1.81,
"Ge": 2.01,
"As": 2.18,
"Se": 2.55,
"Br": 2.96,
"Kr": 3.0,
"Rb": 0.82,
"Sr": 0.95,
"Y": 1.22,
"Zr": 1.33,
"Nb": 1.6,
"Mo": 2.16,
"Tc": 1.9,
"Ru": 2.2,
"Rh": 2.28,
"Pd": 2.2,
"Ag": 1.93,
"Cd": 1.69,
"In": 1.78,
"Sn": 1.96,
"Sb": 2.05,
"Te": 2.1,
"I": 2.66,
"Xe": 2.6,
"Cs": 0.79,
"Ba": 0.89,
"La": 1.1,
"Ce": 1.12,
"Pr": 1.13,
"Nd": 1.14,
"Pm": 1.155,
"Sm": 1.17,
"Eu": 1.185,
"Gd": 1.2,
"Tb": 1.21,
"Dy": 1.22,
"Ho": 1.23,
"Er": 1.24,
"Tm": 1.25,
"Yb": 1.26,
"Lu": 1.27,
"Hf": 1.3,
"Ta": 1.5,
"W": 2.36,
"Re": 1.9,
"Os": 2.2,
"Ir": 2.2,
"Pt": 2.28,
"Au": 2.54,
"Hg": 2.0,
"Tl": 1.62,
"Pb": 2.33,
"Bi": 2.02,
"Po": 2.0,
"At": 2.2,
"Rn": 1.63,
"Fr": 0.7,
"Ra": 0.9,
"Ac": 1.1,
"Th": 1.3,
"Pa": 1.5,
"U": 1.38,
"Np": 1.36,
"Pu": 1.28,
"Am": 1.3,
"Cm": 1.3,
"Bk": 1.3,
}
MENDELEEV_NUMBERS = {
"H": 103,
"He": 1,
"Li": 12,
"Be": 77,
"B": 86,
"C": 95,
"N": 100,
"O": 101,
"F": 102,
"Ne": 2,
"Na": 11,
"Mg": 73,
"Al": 80,
"Si": 85,
"P": 90,
"S": 94,
"Cl": 99,
"Ar": 3,
"K": 10,
"Ca": 16,
"Sc": 19,
"Ti": 51,
"V": 54,
"Cr": 57,
"Mn": 60,
"Fe": 61,
"Co": 64,
"Ni": 67,
"Cu": 72,
"Zn": 76,
"Ga": 81,
"Ge": 84,
"As": 89,
"Se": 93,
"Br": 98,
"Kr": 4,
"Rb": 9,
"Sr": 15,
"Y": 25,
"Zr": 49,
"Nb": 53,
"Mo": 56,
"Tc": 59,
"Ru": 62,
"Rh": 65,
"Pd": 69,
"Ag": 71,
"Cd": 75,
"In": 79,
"Sn": 83,
"Sb": 88,
"Te": 92,
"I": 97,
"Xe": 5,
"Cs": 8,
"Ba": 14,
"La": 33,
"Ce": 32,
"Pr": 31,
"Nd": 30,
"Pm": 29,
"Sm": 28,
"Eu": 18,
"Gd": 27,
"Tb": 26,
"Dy": 24,
"Ho": 23,
"Er": 22,
"Tm": 21,
"Yb": 17,
"Lu": 20,
"Hf": 50,
"Ta": 52,
"W": 55,
"Re": 58,
"Os": 63,
"Ir": 66,
"Pt": 68,
"Au": 70,
"Hg": 74,
"Tl": 78,
"Pb": 82,
"Bi": 87,
"Po": 91,
"At": 96,
"Rn": 6,
"Fr": 7,
"Ra": 13,
"Ac": 48,
"Th": 47,
"Pa": 46,
"U": 45,
"Np": 44,
"Pu": 43,
"Am": 42,
"Cm": 41,
"Bk": 40,
"Cf": 39,
"Es": 38,
"Fm": 37,
"Md": 36,
"No": 35,
"Lr": 34,
}