Surface Hopping Nested Instances Training Set for Excited-state Learning
Abstract Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) mod...
Saved in:
| Main Authors: | Robin Curth, Theodor E. Röhrkasten, Carolin Müller, Julia Westermayr |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05443-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports
by: Sophia Wesely, et al.
Published: (2025-04-01) -
Multiple Instance Learning With Instance-Level Positive-Unlabeled Learning in Anomaly Detection
by: Ryosuke Matsuo, et al.
Published: (2025-01-01) -
Data Augmentation With Flickering Backgrounds and Instances for Instance Segmentation
by: Kisu Lee, et al.
Published: (2025-01-01) -
Security analysis of ZKPoK based on MQ problem in the multi-instance setting
by: Kahrobaei Delaram, et al.
Published: (2025-04-01) -
AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation
by: Shaoliang Fang, et al.
Published: (2025-05-01)