Crystal Group Prediction for Lithiated Manganese Oxides Using Machine Learning DOI Creative Commons
Pier Paolo Prosini

Batteries, Journal Year: 2023, Volume and Issue: 9(2), P. 112 - 112

Published: Feb. 5, 2023

This work aimed to predict the crystal structure of a compound starting only from knowledge its chemical composition. The method was developed select new materials in field lithium-ion batteries and tested on Li-Fe-O compounds. For each testing compound, correspondence with respect training compounds evaluated simply by calculating Euclidean distance existing between stoichiometric coefficients elements constituting two At under test assigned for which value minimum. results showed that model can crystalline group an accuracy higher than 80% precision 90%, cut-off four. then used manganese-based (Li-Mn-O). analysis conducted twenty randomly selected 70%. Out ten valid predictions, nine were true positives, 90%.

Language: Английский

Machine Learning-Aided Materials Design Platform for Predicting the Mechanical Properties of Na-Ion Solid-State Electrolytes DOI

Junho Jo,

Eunseong Choi,

Minseon Kim

et al.

ACS Applied Energy Materials, Journal Year: 2021, Volume and Issue: 4(8), P. 7862 - 7869

Published: Aug. 5, 2021

Na-ion solid-state electrolytes (Na-SSE) exhibit high potential for electrical energy storage owing to their densities and low manufacturing cost. However, mechanical properties critical maintain structural stability at the interface are still insufficiently understood. In this study, a machine learning based regression model was developed predicting of Na-SSEs. As training set, 12,361 materials were obtained from well-known database (Materials Project) represented with respective chemical descriptors. The surrogate exhibited remarkable accuracy (R2 score) 0.72 0.87, mean absolute error 11.8 GPa 15.3 shear bulk modulus, respectively. This then applied predict 2,432 Na-SSEs, which have been validated first principles calculations. Finally, optimization process performed develop an ideal screening platform by adding new minimized dataset, wherein prediction uncertainty is reduced. We believe that proposed in study can accelerate search Na-SSEs minimum

Language: Английский

Citations

23
Contact map based crystal structure prediction using global optimization DOI
Jianjun Hu, Wenhui Yang, Rongzhi Dong

et al.

CrystEngComm, Journal Year: 2021, Volume and Issue: 23(8), P. 1765 - 1776

Published: Jan. 1, 2021

Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering.

Language: Английский

Citations

21
Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula DOI Creative Commons
Abdulmohsen Alsaui, Saad M. Alqahtani, Faisal Mumtaz

et al.

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Jan. 28, 2022

One of the most challenging problems in condensed matter physics is to predict crystal structure just from chemical formula material. In this work, we present a robust machine learning (ML) predictor for point group ternary materials (A[Formula: see text]B[Formula: text]C[Formula: text]) - as first step with very small set ionic and positional fundamental features. From ML perspective, problem strenuous due multi-labelity, multi-class, data imbalance. The resulted prediction reliable high balanced accuracies are obtained by different methods. Many similarity-based approaches accuracy above 95% indicating that well captured reduced features; namely, stoichiometry, radii, ionization energies, oxidation states each three elements compound. not limited approach; but rather points should expect higher having more data.

Language: Английский

Citations

16
Crystal structure prediction of materials with high symmetry using differential evolution DOI
Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Rongzhi Dong

et al.

Journal of Physics Condensed Matter, Journal Year: 2021, Volume and Issue: 33(45), P. 455902 - 455902

Published: Aug. 13, 2021

Crystal structure determines properties of materials. With the crystal a chemical substance, many physical and can be predicted by first-principles calculations or machine learning models. Since it is relatively easy to generate hypothetical chemically valid formula, prediction becomes an important method for discovering new In our previous work, we proposed contact map-based method, which uses global optimization algorithms such as genetic maximize match between map real search coordinates at Wyckoff Positions(WP). However, when predicting with high symmetry, found that algorithm has difficulty find effective combination WPs satisfies mainly caused inconsistency dimensionality target structure. This makes challenging predict structures high-symmetry crystals. order solve this problem, here propose use PyXtal filter random given symmetry constraints based on information formulas space groups. goal, differential evolution non-special positions realize Our experimental results show CMCrystalHS effectively problem inconsistent dimensions symmetry.

Language: Английский

Citations

18
Crystal Group Prediction for Lithiated Manganese Oxides Using Machine Learning DOI Creative Commons
Pier Paolo Prosini

Batteries, Journal Year: 2023, Volume and Issue: 9(2), P. 112 - 112

Published: Feb. 5, 2023

This work aimed to predict the crystal structure of a compound starting only from knowledge its chemical composition. The method was developed select new materials in field lithium-ion batteries and tested on Li-Fe-O compounds. For each testing compound, correspondence with respect training compounds evaluated simply by calculating Euclidean distance existing between stoichiometric coefficients elements constituting two At under test assigned for which value minimum. results showed that model can crystalline group an accuracy higher than 80% precision 90%, cut-off four. then used manganese-based (Li-Mn-O). analysis conducted twenty randomly selected 70%. Out ten valid predictions, nine were true positives, 90%.

Language: Английский

Citations

7