About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
An Entropy-maximization Approach for the Generation of Training Sets for Machine-learned Potentials |
Author(s) |
Joshua Brown, Mariia Karabin, Danny Perez |
On-Site Speaker (Planned) |
Danny Perez |
Abstract Scope |
The last few years have seen considerable advances in the development of machine-learned interatomic potentials. A very important aspect of the parameterization of transferable potentials is the generation of training sets that are sufficiently diverse, yet compact enough to be affordably characterized with high-fidelity reference methods. We formulate the generation of a training set as an optimization problem where the figure of merit is the entropy of the distribution of atom-wise descriptors. This can be used to create a fictitious potential to explicitly drive the generation of new configurations that maximally improve the diversity of the training set. I will show how this strategy can provide an automated and scalable solution to generate large training sets without human intervention. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, |