nabladft: Large-scale conformational energy and hamiltonian prediction benchmark and dataset

K Khrabrov, I Shenbin, A Ryabov, A Tsypin… - Physical Chemistry …, 2022 - pubs.rsc.org
Electronic wave function calculation is a fundamental task of computational quantum
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

K Khrabrov, A Ber, A Tsypin… - Advances in …, 2025 - proceedings.neurips.cc
Methods of computational quantum chemistry provide accurate approximations of molecular
properties crucial for computer-aided drug discovery and other areas of chemical science …

Do** position estimation for FeRh-based alloys

E Rumiantsev, K Khrabrov, A Tsypin, ND Peresypkin… - Scientific Reports, 2024 - nature.com
FeRh-based alloys have attracted significant attention due to their magnetic phase transition
and significant magnetocaloric effects. These properties position them as promising …

[HTML][HTML] Boosting heterogeneous catalyst discovery by structurally constrained deep learning models

AN Korovin, IS Humonen, AI Samtsevich… - Materials Today …, 2023 - Elsevier
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 …

Graph neural networks for predicting structural stability of Cd-and Zn-doped γ-CsPbI3

RA Eremin, IS Humonen, AA Kazakov… - Computational Materials …, 2024 - Elsevier
Computational modeling of disordered crystal structures is essential for the study of
composition–structure–property relations for many families of functional materials. Efficient …

Prediction of urban population-facilities interactions with graph neural network

M Mishina, S Sobolevsky, E Kovtun, A Khrulkov… - … Science and Its …, 2023 - Springer
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 …

On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants

FS Avilov, RA Eremin, SA Budennyy… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

[PDF][PDF] Materials Today Chemistry

AN Korovin, IS Humonen, AI Samtsevich… - Materials …, 2023 - publications.hse.ru
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 …

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 …