Alexei Nikitin Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Biography and Research Information
OverviewAI-generated summary
Alexei Nikitin's research utilizes computational and theoretical approaches to investigate molecular systems. He has published work on predicting protein structure, including developing novel methods for protein backbone conformation prediction. His research also encompasses the simulation of chemical systems, such as alkanes, using perturbation theory and adaptive force matching. In parallel, Nikitin has explored advancements in wireless communication technologies, specifically focusing on M-ary Aggregate Spread Pulse Modulation for low-power, wide-area networks (LPWANs) suitable for Internet of Things (IoT) applications. His work in this area includes frameworks for robust detection, synchronization, and decoding of these communication signals. Nikitin has a demonstrated h-index of 10 and has authored 38 publications with 445 citations. He has collaborated with Feng Wang at the University of Arkansas at Fayetteville on shared publications.
Metrics
- h-index: 10
- Publications: 33
- Citations: 446
Selected Publications
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Simulation of Linear and Cyclic Alkanes with Second-Order Møller–Plesset Perturbation Theory through Adaptive Force Matching (2024)
Collaboration Network
Top Collaborators
- M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications
- M-ary Aggregate Spread Pulse Modulation in LPWANs for IoT applications
- M-ary Aggregate Spread Pulse Modulation for robust and scalable low-power wireless networks
- M-ary Aggregate Spread Pulse Modulation with pulse-shaping power control for highly scalable LPWANs
- M-ary Aggregate Spread Pulse Modulation for robust and scalable low-power wireless networks
Showing 5 of 9 shared publications
- Structural coordinates: A novel approach to predict protein backbone conformation
- Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information
- To the fast calculation of the solvation free energy. Combining expanded ensembles with <scp>L2MC</scp>
- Structural coordinates: A novel approach to predict protein backbone conformation
- Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information
- Structural coordinates: A novel approach to predict protein backbone conformation
- Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information
- To the fast calculation of the solvation free energy. Combining expanded ensembles with <scp>L2MC</scp>
- Partial charges for molecular-mechanical models of heterocyclic compounds: pyridine nitrogen
- Structural coordinates: A novel approach to predict protein backbone conformation
- Structural coordinates: A novel approach to predict protein backbone conformation
- Structural coordinates: A novel approach to predict protein backbone conformation
- Structural coordinates: A novel approach to predict protein backbone conformation
- Simulation of Linear and Cyclic Alkanes with Second-Order Møller–Plesset Perturbation Theory through Adaptive Force Matching
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