Yi-Fan Hou

Solid State Physics Laboratory — India

Materials Science · Physical Sciences

10h-index318citations43works5.52yr avg
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Research Topics

Machine Learning in Materials Science(20), Computational Drug Discovery Methods(7), Various Chemistry Research Topics(6), Advanced Chemical Physics Studies(5), Spectroscopy and Quantum Chemical Studies(5)

Publications43 total

AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed across Seven Main-Group Elements
Journal of Chemical Theory and Computation·2026
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
The Journal of Physical Chemistry Letters·2025· 3 cited
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
2025Open Access
Combining spectral and texture indexes predicts plant diversity in temperate riverine wetlands of the lower Yellow River, China
Ecological Indicators·2025· 2 citedOpen Access
Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations
Machine Learning Science and Technology·2025· 2 citedOpen Access
Synthesis of quinazoline derivatives and their in vitro inhibition activity against MDA-MB-231 cells and A549 cells
Bioorganic & Medicinal Chemistry Letters·2025
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
2025· 1 citedOpen Access
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
arXiv (Cornell University)·2025Open Access
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
npj Computational Materials·2025· 13 citedOpen Access
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
The Journal of Physical Chemistry A·2025· 7 cited
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
2025Open Access
ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies
The Journal of Physical Chemistry Letters·2025· 12 cited
Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations
2024· 3 citedOpen Access
Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models
2024Open Access
Universal and Updatable Artificial Intelligence-Enhanced Quantum Chemical Foundational Models
2024· 5 citedOpen Access
Towards Accurate and Efficient Anharmonic Vibrational Frequencies with the Universal Interatomic Potential ANI-1ccx-gelu and Its Fine-Tuning
2024Open Access
Surprising Dynamics Phenomena in the Diels–Alder Reaction of C <sub>60</sub> Uncovered with AI
The Journal of Organic Chemistry·2024· 4 cited
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
2024
Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations
Journal of Chemical Theory and Computation·2024· 11 cited
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning
2024· 4 citedOpen Access
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Frequent Co-authors

Pavlo O. Dral(35), Fuchun Ge(19), Lina Zhang(14), Mario Barbatti(12), Yuxinxin Chen(11), Mikołaj Martyka(7), Quanhao Zhang(6), Max Pinheiro(5), Olexandr Isayev(5), Bao-Xin Xue(4), Peikun Zheng(4), Joanna Jankowska(4), Arif Ullah(3), Jakub Martinka(3), Jiřı́ Pittner(3), Jianxing Huang(2), Cheng Wang(2), Yuming Su(2), Yiheng Dai(2), Yangtao Chen(2)