Innokentiy S. Humonen

Air University — United States

Materials Science · Physical Sciences

4h-index53citations14works2.62yr avg
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Research Topics

Machine Learning in Materials Science(8), Topic Modeling(2), Electrocatalysts for Energy Conversion(2), Natural Language Processing Techniques(2), Handwritten Text Recognition Techniques(2)

Publications14 total

First-principles data-driven approach for assessment of stability of Tc-C systems
Acta Materialia·2025· 1 cited
MuMMy: Multimodal Dataset supporting VLM-based Egyptology Research Assistant
2025
Automatic Image Translation of Long Ancient Egyptian Texts for Augmented Reality Applications
2025
Automatic Interpretation of Ancient Egyptian Texts for Education and Research
2025· 2 cited
Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach
npj Computational Materials·2025· 3 citedOpen Access
Benchmarking machine learning models for predicting lithium ion migration
npj Computational Materials·2025· 12 citedOpen Access
Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI3
Scientific Reports·2025· 2 citedOpen Access
Unleashing the power of novel conditional generative approaches for new materials discovery
arXiv (Cornell University)·2024· 2 citedOpen Access
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
arXiv (Cornell University)·2024· 1 citedOpen Access
Graph neural networks for predicting structural stability of Cd- and Zn-doped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e465"><mml:mi>γ</mml:mi></mml:math>-CsPbI3
Computational Materials Science·2023· 6 cited
Boosting heterogeneous catalyst discovery by structurally constrained deep learning models
Materials Today Chemistry·2023· 12 citedOpen Access
Co-occurrence Networks for Word Sense Induction
2023· 2 cited
Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models
arXiv (Cornell University)·2022Open Access
Hybrid DFT/Data-Driven Approach for Searching for New Quasicrystal Approximants in Sc-X (X = Rh, Pd, Ir, Pt) Systems
Crystal Growth & Design·2022· 10 cited

Frequent Co-authors

Roman A. Eremin(9), Semen Budennyy(9), Vladimir D. Lazarev(4), Ilya Makarov(4), Aliaksei V. Krautsou(3), Maksim Golyadkin(3), А. Н. Коровин(2), Artem I. Samtsevich(2), Ekaterina Bureeva(2), Ekaterina Alexandrova(2), Danil Kalin(2), Artem D. Dembitskiy(1), Dmitry A. Aksyonov(1), Stanislav S. Fedotov(1), A.I. Vasilev(1), V.D. Lazarev(1), Pavel N. Zolotarev(1), И. В. Медриш(1), Leonid Zhukov(1), A. A. Kazakov(1)