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Vaibhav bihani
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EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations
V Bihani, S Mannan, U Pratiush, T Du, Z Chen, S Miret, M Micoulaut, ...
Digital Discovery 3 (4), 759-768, 2024
152024
Natural language processing-guided meta-analysis and structure factor database extraction from glass literature
M Zaki, SR Namireddy, T Pittie, V Bihani, SR Keshri, V Venugopal, ...
Journal of Non-Crystalline Solids: X 15, 100103, 2022
102022
Stridernet: A graph reinforcement learning approach to optimize atomic structures on rough energy landscapes
V Bihani, S Manchanda, S Sastry, S Ranu, NMA Krishnan
International Conference on Machine Learning, 2431-2451, 2023
72023
Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning
S Mannan, V Bihani, NMA Krishnan, JC Mauro
Materials Genome Engineering Advances 2 (1), e25, 2024
52024
Reactive molecular simulation of shockwave propagation in calcium–silicate–hydrate gels
V Bihani, A Yadav, NMA Krishnan
Journal of Non-Crystalline Solids 590, 121677, 2022
32022
Unsupervised Graph Neural Network Reveals the Structure--Dynamics Correlation in Disordered Systems
V Bihani, S Manchanda, S Ranu, NM Krishnan
arXiv preprint arXiv:2206.12575, 2022
12022
Foundational Large Language Models for Materials Research
V Mishra, S Singh, D Ahlawat, M Zaki, V Bihani, HS Grover, B Mishra, ...
arXiv preprint arXiv:2412.09560, 2024
2024
Low-Dimensional Projections for Visualizing Energy Landscapes of Atomic Systems
V Bihani, S Sastry, S Ranu, NMA Krishnan
AI for Accelerated Materials Design-Vienna 2024, 0
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