Raman Sharma | Machine Learning | Best Researcher Award

Best Researcher Award

Raman Sharma
Himachal Pradesh University

Raman Sharma
Affiliation Himachal Pradesh University
Country India
Scopus ID 7407244783
Documents 78
Citations 335
h-index 12
Subject Area Machine Learning
Event Technology Scientists Awards

The Best Researcher Award recognizes sustained scholarly achievement, scientific innovation, and measurable research impact. Raman Sharma of Himachal Pradesh University has established an academic profile through contributions to machine learning and computational materials research, supported by peer-reviewed publications, citation performance, and interdisciplinary collaboration. His research activities demonstrate continued engagement with emerging computational methodologies and their practical scientific applications.[1]

Abstract

Raman Sharma is recognized for research that integrates machine learning with computational materials science to investigate electronic structures, nanomaterials, adsorption mechanisms, and predictive simulations. His scholarly output demonstrates interdisciplinary collaboration, consistent publication in peer-reviewed journals, and measurable citation impact. Through advanced computational modeling, density functional theory, and machine learning methodologies, his work contributes to scientific understanding while supporting innovation across materials science, condensed matter physics, and computational engineering. These accomplishments provide strong academic justification for recognition through the Best Researcher Award.[1][2][3]

Keywords

Machine Learning, Computational Materials Science, Density Functional Theory, Tellurene, Nanomaterials, Electronic Properties, Artificial Intelligence, Materials Engineering.

Introduction

Raman Sharma has developed an active academic career emphasizing computational materials science and machine learning applications. His investigations combine theoretical modeling with advanced computational techniques to examine material properties, enabling improved scientific understanding and supporting interdisciplinary research across physics, engineering, and emerging nanotechnology domains.[1]

Research Profile

Affiliated with Himachal Pradesh University, Raman Sharma has produced seventy-eight Scopus-indexed publications with more than three hundred citations. His research profile reflects continuous scholarly productivity, collaborative research practices, and contributions spanning machine learning, electronic materials, nanostructures, and computational simulations within internationally recognized scientific literature.[1]

Research Contributions

His research has advanced understanding of tellurene derivatives, adsorption phenomena, and machine learning potentials for predicting complex material behavior. These investigations integrate density functional theory with computational intelligence, providing scientifically valuable insights that support future developments in electronic materials, nanotechnology, and computational physics.[1][2][3]

Publications

The publication record includes peer-reviewed articles addressing quantum capacitance, Rashba splitting, adsorption mechanisms, optical properties, and machine-learned neural network potential energy surfaces. These studies demonstrate methodological diversity and sustained engagement with high-quality scientific publishing within computational materials research.[1][2][3]

Research Impact

The measurable citation record, interdisciplinary collaborations, and Scopus-indexed publications demonstrate meaningful scholarly influence. His research supports broader scientific progress by improving computational approaches for materials discovery, enhancing predictive modeling accuracy, and contributing knowledge relevant to future technological and engineering innovations.[1][3]

Award Suitability

Based on publication quality, citation metrics, interdisciplinary research, and sustained scientific productivity, Raman Sharma demonstrates qualifications consistent with the objectives of the Best Researcher Award. His contributions reflect academic excellence, innovative computational research, and continued commitment to advancing knowledge through internationally recognized scholarship.[1]

Conclusion

Raman Sharma’s scholarly achievements illustrate a balanced combination of research productivity, computational expertise, and interdisciplinary collaboration. His published contributions, scientific impact, and commitment to advancing machine learning applications in materials science collectively support recognition through the Technology Scientists Awards and the Best Researcher Award.[1][2]

References

  1. Sharma, R., et al. (2023). Giant quantum capacitance and Rashba splitting in Tellurene bilayer derivatives. Materials Chemistry and Physics. https://doi.org/10.1016/j.matchemphys.2023.128185
    https://www.sciencedirect.com/science/article/abs/pii/S1386947723001078
  2. Sharma, R., et al. (2023). Adsorption of Te clusters on tellurene and MoS2 monolayers: Structural, electronic, and optical properties. Journal of Computational Electronics.
    https://www.proquest.com/openview/388bf3eab8f46c2a3969823431cbcd0f/1?pq-origsite=gscholar&cbl=1456352
  3. Sharma, R., et al. (2024). Understanding melting behavior of aluminum clusters using machine learned deep neural network potential energy surfaces. The Journal of Chemical Physics, 161(17). https://doi.org/10.1063/5.0228807
    https://pubs.aip.org/aip/jcp/article-abstract/161/17/174301/3318470

Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Mr. Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Researcher | Kharazmi University | Iran

Mr. Amirhossein Ghasemi Abyaneh is an emerging scholar in the field of artificial intelligence applications in sustainable supply chains, affiliated with Kharazmi University, Tehran, Iran. His academic endeavors focus on integrating advanced data analytics, optimization techniques, and machine learning frameworks to enhance decision-making, efficiency, and sustainability across complex supply chain networks. With 3 published research papers and an h-index of 1, Mr. Abyaneh has begun establishing a scholarly footprint that bridges technology-driven innovation with environmental and operational resilience. His work, including the open-access article “An Analytical Review of Artificial Intelligence Applications in Sustainable Supply Chains” (2025, Supply Chain Analytics), provides critical insights into the evolving intersection of AI and sustainability, emphasizing how digital intelligence can optimize resource utilization, reduce carbon footprints, and strengthen circular economy practices. Having received citations from international scholars, he actively contributes to the global academic dialogue on sustainable logistics, smart manufacturing, and responsible innovation. Mr. Abyaneh’s collaborative research network includes seven co-authors from diverse academic and institutional backgrounds, reflecting a strong interdisciplinary approach that combines engineering, data science, and environmental management. His studies aim to foster both theoretical advancement and practical applicability, offering valuable implications for policymakers, corporations, and researchers seeking to transition toward greener, data-driven supply chains. Beyond academic impact, his contributions align with global sustainability goals, promoting knowledge transfer, digital equity, and responsible AI adoption for societal benefit.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Sharbati, A., Movahed, A. B., Abyaneh, A. G., & Rahmanian, F. (2025). Risk assessment of healthcare systems using the FMEA method: Medication management process. Journal of Future Digital Optimization, 1(1), 71–85.
Cited by: 4

2. Abyaneh, A. G., Movahed, A. B., Abyari, A., Nodehfarahani, A., & Khakbazan, M. (2025). Evaluating the RFID technology in Costco Company: A focus on logistics and supply chain management. Applied Innovations in Industrial Management, 5(2), 34–51.
Cited by: 2

3. Movahed, A. B., Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2025). Smart economy cybersecurity: AI-driven risk management in digital markets. In Dynamic and Safe Economy in the Age of Smart Technologies (pp. 49–72).
Cited by: 2

4. Abyaneh, A. G., Ghanbari, H., Mohammadi, E., Amirsahami, A., & Khakbazan, M. (2025). An analytical review of artificial intelligence applications in sustainable supply chains. Supply Chain Analytics, 100173.
Cited by: 1

5. Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2026). Artificial intelligence in digital marketing: Trends, challenges, and strategic opportunities. In Improving Consumer Engagement in Digital Marketing Through Cognitive AI (pp. 225–260)

Mr. Amirhossein Ghasemi Abyaneh envisions a future where artificial intelligence empowers sustainable industrial transformation, enabling supply chains to become more adaptive, transparent, and environmentally responsible. His research advances the integration of smart analytics and sustainability principles, fostering innovation that supports global climate resilience and ethical technological progress.