• Active learning streamlines development of high performance catalysts for higher alcohol synthesis
    M. Suvarna, T. Zou, S.H. Chong, Y. Ge, A.J. Martín, J. Pérez-Ramírez
    Nat. Commun. 2024, 15, 5844 (doi:10.1038/s41467-024-50215-1, featured in Editors' Highlights)
  • Embracing data science in catalysis research
    M. Suvarna, J. Pérez-Ramírez
    Nat. Catal. 2024, 7, 624-635 (doi:10.1038/s41929-024-01150-3, front cover)
  • Active learning based guided synthesis of engineered biochar for CO2 capture
    X. Yuan, M. Suvarna, J.Y. Lim, J. Pérez-Ramírez, X. Wang, Y.S. Ok
    Environ. Sci. Technol. 2024, 58, 6628−6636 (doi:10.1021/acs.est.3c10922, front cover)
  • Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
    M. Suvarna, A.C. Vaucher, S. Mitchell, T. Laino, J. Pérez-Ramírez
    Nat. Commun. 2023, 14, 7964 (doi:10.1038/s41467-023-43836-5)
  • Identifying descriptors for promoted rhodium-based catalysts for higher alcohol synthesis via machine learning
    M. Suvarna, P. Preikschas, J. Pérez-Ramírez
    ACS Catal. 2022, 12, 15373-15385 (doi:10.1021/acscatal.2c04349)
  • A review of computational modeling techniques for wet waste valorization: research trends and future perspectives
    J. Li, M. Suvarna, L. Li, L. Pan, J. Pérez-Ramírez, Y.S. Ok, X. Wang
    J. Clean. Prod. 2022, 367, 133025 (doi:10.1016/j.jclepro.2022.133025)
  • A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
    M. Suvarna, T. Pinheiro Araújo, J. Pérez-Ramírez
    Appl. Catal. B 2022, 315, 121530 (doi:10.1016/j.apcatb.2022.121530)
  • A three-step machine learning framework for energy profiling and production estimation in dynamic manufacturing environments
    D. Tan, M. Suvarna, Y.S. Tan, J. Li, X. Wang
    Appl. Energy 2021, 291, 1-14 (doi:10.1016/j.apenergy.2021.116808)
  • Cyber physical production system for data-driven, decentralized and secured manufacturing
    M. Suvarna, Y.K. Shaun, N.Y. Ting, X. Wang
    Engineering 2021, 7, 1212-1223 (doi:10.1016/j.eng.2021.04.021)
  • Online prediction of mechanical properties of hot rolled steel plate using machine learning
    X. Qian, M. Suvarna, L. Jiali, X. Zhu, J. Cai, X. Wang
    Mater. Des. 2021, 197, 1-13 (doi:10.1016/j.matdes.2020.109201)
  • Allied machine learning to predict CO2 adsorption on biomass waste-derived porous carbons
    X. Yan, M. Suvarna, S. Liang, P. Dasanaveke, K.B. Lee, L. Jie, X. Wang, Y.S. Ok
    Environ. Sci. Technol. 2021, 55, 11925-11936 (doi:10.1021/acs.est.1c01849)
  • Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
    L. Jie, L. Pan, M. Suvarna, Y.W. Tong, X. Wang
    Appl. Energy 2020, 269, 1-10 (doi:10.1016/j.apenergy.2020.115166)
  • Smart manufacturing for smart cities - Overview, insights and future directions 
    M. Suvarna, L. Buth, J. Hejny, M. Mennenga, L. Jie, N.Y. Ting, C. Herrmann, X. Wang
    Advanced Intelligent Systems 2020, 2, 1-19 (doi:10.1002/aisy.202000043)
  • Process modelling and simulation of bitumen mining and recovery from oilsands
    M. Suvarna, M. Divakaran, E. Nduagu
    Miner. Eng. 2019, 134, 65-76 (doi:10.1016/j.mineng.2018.12.024)
  • Optimization, kinetics and radioprotective potential of a heteropolysaccharide from L. rhamnosus RVPI                                  
    A. Umesh, Pradeepa, A. Manjunath, C. Salian, Manasadeepika, M. Suvarna, V.S. Muddappa
    Biotechnol. Appl. Biochem. 2019, 67, 442-455 (doi:10.1002/bab.1855)




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