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Found 8 papers, 4 papers with code
Assessment of Vacancy Formation and Surface Energies of Materials using Classical Force-Fields
Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles.
Materials Science
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems.
Materials Science
High-throughput discovery of topological materials using spin-orbit spillage
After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials.
Materials Science
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand.
Materials Science
High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited.
Materials Science
Uncertainty Prediction for Machine Learning Models of Material Properties
While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i. e., the evaluation of the uncertainty on each prediction, are seldomly available.
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances.
