Niranjan Sitapure

Mitchell Institute — United States

Engineering · Physical Sciences

15h-index874citations36works6.62yr avg
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Research Topics

Fault Detection and Control Systems(9), Quantum Dots Synthesis And Properties(8), Perovskite Materials and Applications(6), Machine Learning in Materials Science(6), Advanced Battery Technologies Research(5)

Publications36 total

Predicting both thermodynamic and kinetic properties of crystallizing molecules via transformer‐based language model
AIChE Journal·2025· 9 citedOpen Access
Machine learning-based multiscale modeling and control of quantum dot manufacturing and their applications
2025
Review of: "Transformers Can Navigate Mazes With Multi-Step Prediction"
2025Open Access
Multiscale modeling of catalyst deactivation in dry methane reforming
Chemical Engineering Journal·2024· 14 cited
Empowering Hybrid Models with Attention-Based Time-series Transformers: A Case Study in Batch Crystallization
2024
Integrating Machine Learning in Process Control with LSTMc: A Case Study in Batch Crystallization
2024
Advancing crystal growth prediction: An adaptive kMC model spanning multiple regimes
Chemical Engineering Science·2024· 9 cited
Machine learning meets process control: Unveiling the potential of LSTMc
AIChE Journal·2024· 21 citedOpen Access
Introducing Hybrid Modeling with Time-Series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization
Industrial & Engineering Chemistry Research·2023· 68 cited
Exploring Different Time-series-Transformer (TST) Architectures: A Case Study in Battery Life Prediction for Electric Vehicles (EVs)
arXiv (Cornell University)·2023· 6 citedOpen Access
Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization
arXiv (Cornell University)·2023Open Access
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers
Computers & Chemical Engineering·2023· 79 cited
Require Process Control? LSTMc is all you need!
arXiv (Cornell University)·2023· 3 citedOpen Access
Investigating High-Performance Non-Precious Transition Metal Oxide Catalysts for Nitrogen Reduction Reaction: A Multifaceted DFT–kMC–LSTM Approach
ACS Catalysis·2023· 56 citedOpen Access
Model Predictive Control of Cadmium Telluride (CdTe) Quantum Dot (QD) Crystallization
2023· 1 cited
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers
arXiv (Cornell University)·2023· 1 citedOpen Access
Exploring the potential of time-series transformers for process modeling and control in chemical systems: An inevitable paradigm shift?
Process Safety and Environmental Protection·2023· 112 cited
A Unified Approach for modeling and control of crystallization of quantum dots (QDs)
Digital Chemical Engineering·2022· 10 citedOpen Access
DFT–kMC Analysis for Identifying Novel Bimetallic Electrocatalysts for Enhanced NRR Performance by Suppressing HER at Ambient Conditions Via Active-Site Separation
ACS Catalysis·2022· 67 cited
Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management
Journal of Cleaner Production·2022· 12 citedOpen Access
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Frequent Co-authors

Joseph Sang‐Il Kwon(29), Chi H. Lee(4), Sungwon Hwang(4), Myeongjae Lee(3), BongSoo Kim(3), Silabrata Pahari(3), Robert W. Epps(3), Milad Abolhasani(3), Joseph Sang‐II Kwon(3), Jeehye Yang(2), Se Young Park(2), Myoungjin Park(2), Jonghoon Kim(2), Donghyo Hahm(2), Seunghyun Rhee(2), Daeyeon Lee(2), Yong Hyun Jo(2), Jaemin Lim(2), Jungwook Kim(2), Tae Joo Shin(2)