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Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
Matthew Horton, Jimmy-Xuan Shen, Jordan Burns +21
Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way.…
Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities
Kevin Cruse, Amalie Trewartha, Sanghoon Lee +6
Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particl…
Machine-learning rationalization and prediction of solid-state synthesis conditions
Haoyan Huo, Christopher J. Bartel, Tanjin He +5
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materia…
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
Comparison of the Tetrahedron Method to Smearing Methods for the Electronic Density of States
M. Y. Toriyama, A. M. Ganose, M. Dylla +7
The electronic density of states (DOS) highlights fundamental properties of materials that oftentimes dictate their properties, such as the band gap and Van Hove singularities. In…
When Band Convergence is Not Beneficial for Thermoelectrics
Junsoo Park, Max Dylla, Yi Xia +3
Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances…
Optimal Band Structure for Thermoelectrics with Realistic Scattering and Bands
Junsoo Park, Yi Xia, Vidvuds Ozoliņš +1
Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified ban…
Efficient calculation of carrier scattering rates from first principles
Alex M. Ganose, Junsoo Park, Alireza Faghaninia +3
The electronic transport behaviour of materials determines their suitability for technological applications. We develop an efficient method for calculating carrier scattering rates…
Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
Alexander Dunn, Qi Wang, Alex Ganose +2
We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk mat…
High Thermoelectric Performance and Defect Energetics of Multi-pocketed Full-Heusler Compounds
Junsoo Park, Yi Xia, Alex Ganose +2
We report first-principles density-functional study of electron-phonon interactions and thermoelectric transport properties of full-Heusler compounds SrBiAu and SrSbAu.…
A critical examination of compound stability predictions from machine-learned formation energies
Christopher J. Bartel, Amalie Trewartha, Qi Wang +3
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of…
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
Qi Wang, Anubhav Jain
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures p…
Predicting the Volumes of Crystals
Iek-Heng Chu, Sayan Roychowdhury, Daehui Han +2
New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis…