2.1k citations · 6k across the 29 of their papers we have counts for
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Kolmogorov-Arnold Networks in Thermoelectric Materials Design
Marco Fronzi, Michael J. Ford, Kamal Singh Nayal +2
The discovery of high-performance thermoelectric materials requires models that are both accurate and interpretable. Traditional machine learning approaches, while effective at pro…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for TCNQ on TTF
Saeed Moayedpour, Imaneul Bier, Wen Wen +3
Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar…
The AFLOW Fleet for Materials Discovery
Cormac Toher, Corey Oses, David Hicks +48
The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the d…
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
Eric Gossett, Cormac Toher, Corey Oses +8
Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a p…
Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
Olexandr Isayev, Corey Oses, Cormac Toher +3
Historically, materials discovery has been driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the op…
Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
Olexandr Isayev, Denis Fourches, Eugene N. Muratov +4
As the proliferation of high-throughput approaches in materials science is increasing the wealth of data in the field, the gap between accumulated-information and derived-knowledge…