Using small database and energy descriptors to predict molecular thermodynamic energies through mediated learning models
Delta machine learning (DML) models have paved a new way to obtaining high fidelity ab initio simulation results of materials by using quantities with lower computational cost as learning materials. However, the low out-of-sample extrapolative ability and the requirement of large training sets have...
Saved in:
Main Authors: | Chen, Chao, Deng, Siyan, Li, Shuzhou |
---|---|
Other Authors: | School of Materials Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/179404 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Effect of inaccuracy of a thermodynamic property model on distillation simulation
by: Wantana Somcharoenwattana
Published: (2008) -
In silico prediction and screening of γ-secretase inhibitors by molecular descriptors and machine learning methods
by: Yang, X.-G., et al.
Published: (2014) -
Thermodynamics and Mechanics of Molecular Motors
by: HOU RUIZHENG
Published: (2013) -
Understanding Non-equilibrium Thermodynamics
by: G. Lebon
Published: (2017) -
Limits to catalysis in quantum thermodynamics
by: Ng, N.H.Y, et al.
Published: (2020)