Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
space for targeted properties as approaches that rely on trial-and-error are impractical. We …
Deep learning spectroscopy: Neural networks for molecular excitation spectra
K Ghosh, A Stuke, M Todorović… - Advanced …, 2019 - Wiley Online Library
Deep learning methods for the prediction of molecular excitation spectra are presented. For
the example of the electronic density of states of 132k organic molecules, three different …
the example of the electronic density of states of 132k organic molecules, three different …
Black-box optimization for automated discovery
Conspectus In chemistry and materials science, researchers and engineers discover,
design, and optimize chemical compounds or materials with their professional knowledge …
design, and optimize chemical compounds or materials with their professional knowledge …
[图书][B] Bayesian optimization and data science
F Archetti, A Candelieri - 2019 - Springer
Bayesian Optimization and Data Science Page 1 123 SPRINGER BRIEFS IN
OPTIMIZATION Francesco Archetti Antonio Candelieri Bayesian Optimization and Data …
OPTIMIZATION Francesco Archetti Antonio Candelieri Bayesian Optimization and Data …
Designing nanostructures for phonon transport via Bayesian optimization
We demonstrate optimization of thermal conductance across nanostructures by developing
a method combining atomistic Green's function and Bayesian optimization. With an aim to …
a method combining atomistic Green's function and Bayesian optimization. With an aim to …
COMBO: An efficient Bayesian optimization library for materials science
In many subfields of chemistry and physics, numerous attempts have been made to
accelerate scientific discovery using data-driven experimental design algorithms. Among …
accelerate scientific discovery using data-driven experimental design algorithms. Among …
Machine learning in materials design and discovery: Examples from the present and suggestions for the future
JE Gubernatis, T Lookman - Physical Review Materials, 2018 - APS
We provide a brief discussion of “What is machine learning?” and then give a number of
examples of how these methods have recently aided the design and discovery of new …
examples of how these methods have recently aided the design and discovery of new …
Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization
We computationally designed an ultranarrow-band wavelength-selective thermal radiator
via a materials informatics method alternating between Bayesian optimization and thermal …
via a materials informatics method alternating between Bayesian optimization and thermal …
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
physics, chemistry, and materials science, but limited by the cost of accurate and precise …