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nabladft: Large-scale conformational energy and hamiltonian prediction benchmark and dataset
Electronic wave function calculation is a fundamental task of computational quantum
chemistry. Knowledge of the wave function parameters allows one to compute physical and …
chemistry. Knowledge of the wave function parameters allows one to compute physical and …
DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
Methods of computational quantum chemistry provide accurate approximations of molecular
properties crucial for computer-aided drug discovery and other areas of chemical science …
properties crucial for computer-aided drug discovery and other areas of chemical science …
Do** position estimation for FeRh-based alloys
FeRh-based alloys have attracted significant attention due to their magnetic phase transition
and significant magnetocaloric effects. These properties position them as promising …
and significant magnetocaloric effects. These properties position them as promising …
[HTML][HTML] Boosting heterogeneous catalyst discovery by structurally constrained deep learning models
The discovery of new catalysts is one of the significant topics of computational chemistry as it
has the potential to accelerate the adoption of renewable energy sources. Recently …
has the potential to accelerate the adoption of renewable energy sources. Recently …
Graph neural networks for predicting structural stability of Cd-and Zn-doped γ-CsPbI3
Computational modeling of disordered crystal structures is essential for the study of
composition–structure–property relations for many families of functional materials. Efficient …
composition–structure–property relations for many families of functional materials. Efficient …
Prediction of urban population-facilities interactions with graph neural network
The urban population interacts with service facilities on a daily basis. The information on
population-facilities interactions is considered when analyzing the current city organization …
population-facilities interactions is considered when analyzing the current city organization …
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used
and well-tried method of new materials design, the issues of its robustness, reliability, and …
and well-tried method of new materials design, the issues of its robustness, reliability, and …
[PDF][PDF] Materials Today Chemistry
abstract The discovery of new catalysts is one of the significant topics of computational
chemistry as it has the potential to accelerate the adoption of renewable energy sources …
chemistry as it has the potential to accelerate the adoption of renewable energy sources …
Urban energy planning and renewable energy integration driven by energy data
J Li, H Lin, Y Liu, C **e, Z Bao - Fifth International Conference …, 2024 - spiedigitallibrary.org
Urban energy planning plays an important role in the national sustainable development
strategy. In recent years, urban energy planning has gradually received attention from the …
strategy. In recent years, urban energy planning has gradually received attention from the …