数据链接:
官方服务:
资源简介:
Gaussian Approximation Potential generated over DFT and AIMD data
应用场景:
创建时间:
2024-01-01
相关数据集
OpenREACT-CHON-EFH — O pen RE action Dataset of A tomic C onfigura T ions comprising C , H , O , N with E nergies, F orces, and H essians
These datasets were used in the training and testing of Machine Learning Interatomic Potentials (MLIPs) as part of the work represented in the article titled Does Hessian Data Improve the Performance
DataCite Commons2025-05-29 更新160
Machine learning interatomic potential to study radiation-induced damage in 3C-SiC - The dataset
This dataset contains atomic structures used to train a machine learning interatomic potential (MLIP) with the Gaussian Approximation Potential (GAP) framework. Designed to study radiation damage at t
DataCite Commons2025-07-31 更新70
LMucko/omol25-4m-nbasis-le-512
--- dataset_info: features: - name: property_id dtype: string - name: property_hash dtype: string - name: last_modified dtype: timestamp[us] - name: dataset_id dtype: string
Hugging Face2026-04-18 更新30
Evidential Deep Learning for Interatomic Potential
Machine Learning Interatomic Potentials (MLIPs) are models that utilize machine learning techniques to fit interatomic potential functions, with training data derived from ab initio methods. Consequen
DataCite Commons2025-05-01 更新110
Exploring charge density waves in two-dimensional NbSe2 with machine learning
Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CD
DataCite Commons2026-04-14 更新30



