Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
A Genetic Algorithm For Convex Hull Optimisation
by
Probert, Matt I J
, Lawrence, Robert A
, Donaldson, Scott
in
Convex hulls
/ Genetic algorithms
/ Optimization
2024
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
A Genetic Algorithm For Convex Hull Optimisation
by
Probert, Matt I J
, Lawrence, Robert A
, Donaldson, Scott
in
Convex hulls
/ Genetic algorithms
/ Optimization
2024
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
A Genetic Algorithm For Convex Hull Optimisation
2024
Request Book From Autostore
and Choose the Collection Method
Overview
Computationally efficient and automated generation of convex hulls is desirable for high throughput materials discovery of thermodynamically stable multi-species crystal structures. A convex hull genetic algorithm is proposed that uses methodology adapted from multi-objective optimisation techniques to optimise the convex hull itself as an object, enabling efficient discovery of convex hulls for N >= 2 species. This method, when tested on a LiSi system utilising pre-trained machine learned potentials, was found to be able to efficiently discover reported structures as well as new potential LiSi candidate structures.
Publisher
Cornell University Library, arXiv.org
Subject
This website uses cookies to ensure you get the best experience on our website.