Density-of-states similarity descriptor for unsupervised learning from materials data DOI Creative Commons
Martin Kubáň, Santiago Rigamonti, Markus Scheidgen

et al.

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Oct. 22, 2022

We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate similarity of it. As an application example, we study Computational 2D Materials Database (C2DB) that hosts thousands two-dimensional with their properties calculated by density-functional theory. Combining our clustering algorithm, identify groups similar structure. introduce additional descriptors to characterize these clusters in terms crystal structures, atomic compositions, configurations members. This allows us rationalize found (dis)similarities perform automated exploratory confirmatory analysis C2DB data. From this analysis, find majority consist isoelectronic sharing symmetry, but also outliers, i.e., whose cannot be explained way.

Language: Английский

Deep learning-assisted methods for accelerating the intelligent screening of novel 2D materials: New perspectives focusing on data collection and description DOI

Yuandong Lin,

Ji Ma, Yong‐Guang Jia

et al.

Coordination Chemistry Reviews, Journal Year: 2025, Volume and Issue: 529, P. 216436 - 216436

Published: Jan. 16, 2025

Language: Английский

Citations

0
Advancing Band Structure Simulations of Complex Systems of C, Si and SiC: A Machine Learning Driven Density Functional Tight-Binding Approach DOI
Guozheng Fan, Yu Jing, Thomas Frauenheim

et al.

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: 27(7), P. 3796 - 3802

Published: Jan. 1, 2025

We present a machine learning (ML) workflow for optimizing electronic band structures using density functional tight binding (DFTB) to replicate the results of costly hybrid calculations. The is trained on carbon, silicon, and silicon carbide systems, encompassing bulk, slab, defect geometries. Our method accurately reproduces by applying DFTB-ML scheme train predict scaling parameters two-center integrals on-site energies, which particularly accurate near Fermi energy. model demonstrates excellent transferability, enabling training smaller systems while maintaining functional-level accuracy when predicting larger systems. high adaptability our highlight its potential precise structure predictions across diverse chemical environments.

Language: Английский

Citations

0
Multimodal foundation models for material property prediction and discovery DOI Creative Commons

Viggo Moro,

Charlotte Loh, Rumen Dangovski

et al.

Newton, Journal Year: 2025, Volume and Issue: unknown, P. 100016 - 100016

Published: Feb. 1, 2025

Language: Английский

Citations

0
Recent Progress in the Design and Application of Machine Learning for the Hydrogen Evolution Reaction in Electrocatalysis and Photocatalysis DOI
Kaifeng Zhang, Xudong Wang, Yanjing Su

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112462 - 112462

Published: April 1, 2025

Language: Английский

Citations

0
Density-of-states similarity descriptor for unsupervised learning from materials data DOI Creative Commons
Martin Kubáň, Santiago Rigamonti, Markus Scheidgen

et al.

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Oct. 22, 2022

We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate similarity of it. As an application example, we study Computational 2D Materials Database (C2DB) that hosts thousands two-dimensional with their properties calculated by density-functional theory. Combining our clustering algorithm, identify groups similar structure. introduce additional descriptors to characterize these clusters in terms crystal structures, atomic compositions, configurations members. This allows us rationalize found (dis)similarities perform automated exploratory confirmatory analysis C2DB data. From this analysis, find majority consist isoelectronic sharing symmetry, but also outliers, i.e., whose cannot be explained way.

Language: Английский

Citations

18