Update student project description #34
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
mfherbst
reviewed
Dec 18, 2024
Changed introduction and requirements
mfherbst
reviewed
Dec 19, 2024
student_projects/index.md
Outdated
|
|
||
| The goal of this project is to develop a framework that overcomes these challenges by integrating adaptive learning with uncertainty-aware models. This involves formulating an active learning approach that adaptively selects both material structures and numerical parameters to optimize computational resources, while employing Gaussian process regression [^RasmussenWilliams06] to effectively propagate and manage uncertainties in heterogeneous datasets. By combining these techniques, the project aims to improve the accuracy, efficiency, and reliability of data-driven materials modeling. |
student_projects/index.md
Outdated
|
|
||
| - Non-uniform computational cost: The cost of DFT calculations varies significantly across materials due to differences in numerical parameters (discretisation basis, k-point sampling, tolerances) required for a given accuracy. The baseline active learning approach is computing with a fixed discretisation (plane-wave cutoff) chosen a priori for the whole dataset (e.g. [^vanderOord] and [^Merchant2023]), which may not optimally balance cost and accuracy across diverse materials. | ||
| - Non-uniform computational cost: The cost of DFT calculations varies significantly across materials due to differences in numerical parameters, such as discretization basis, k-point sampling, tolerances, required for a required accuracy. The baseline active learning approach is computing with a fixed discretization (e.g., plane-wave cutoff) chosen a priori for the entire dataset (e.g. [^vanderOord] and [^Merchant2023]), which may not optimally balance computational cost and accuracy across diverse materials. |
There was a problem hiding this comment.
The first sentence is very nice. I think the active learning I would explain in one sentence after the bullet points. Then you can add in a second sentence that currently one usually employs a fixed discretisation and mention the rest of your last sentence here.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
No description provided.