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Chemistry

D-Index
89
Citations
46826
World Ranking
2123
National Ranking
768

Overview

Mark E. Tuckerman is a researcher affiliated with New York University in the United States. Their work spans primarily the fields of Engineering and Materials Science, with a substantial focus on subfields such as Materials Chemistry, Electrical and Electronic Engineering, Atomic and Molecular Physics and Optics, Biomedical Engineering, and Catalysis.

The scientist's main research topics include Machine Learning in Materials Science, Fuel Cells and Related Materials, Computational Drug Discovery Methods, Protein Structure and Dynamics, Membrane-based Ion Separation Techniques, Ionic Liquids Properties and Applications, and Spectroscopy and Quantum Chemical Studies.

Mark E. Tuckerman has contributed to numerous publications. Notable recent papers include:

  • Deep Eutectic Solvents: A Review of Fundamentals and Applications, 2020, Chemical Reviews
  • Quantum chemical accuracy from density functional approximations via machine learning, 2020, Nature Communications
  • Liquid Structure and Transport Properties of the Deep Eutectic Solvent Ethaline, 2020, The Journal of Physical Chemistry B
  • Imidacloprid Crystal Polymorphs for Disease Vector Control and Pollinator Protection, 2021, Journal of the American Chemical Society
  • Molecular simulations: past, present, and future (a Topical Issue in EPJB), 2022, The European Physical Journal B

Frequent collaborators in their research include Tamar Zelovich, Leslie Vogt-Maranto, Brian Doherty, Burcu Gurkan, and Jutta Rogal. These co-authors have appeared in multiple joint publications, reflecting ongoing research partnerships.

Mark E. Tuckerman's work appears regularly in well-known venues such as:

  • arXiv (Cornell University)
  • The Journal of Physical Chemistry Letters
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Communications
  • Journal of Chemical Theory and Computation

The research focus extends across theoretical and applied aspects dealing with molecular simulations, ionic liquids, and machine learning methodologies aimed at improving materials characterization and computational drug discovery techniques. This diverse portfolio demonstrates a broad engagement with contemporary scientific challenges in materials and biological systems.

Best Publications

  • Nosé-Hoover chains : the canonical ensemble via continuous dynamics

    Glenn J. Martyna;Michael L. Klein;Mark Tuckerman

  • Reversible multiple time scale molecular dynamics

    M. Tuckerman;B. J. Berne;G. J. Martyna

  • Deep Eutectic Solvents: A Review of Fundamentals and Applications.

    Benworth B. Hansen;Stephanie Spittle;Brian Chen;Derrick Poe

  • Explicit reversible integrators for extended systems dynamics

    Glenn J. Martyna;Mark E. Tuckerman;Douglas J. Tobias;Michael L. Klein

  • The nature of the hydrated excess proton in water

    Dominik Marx;Mark E. Tuckerman;Jürg Hutter;Michele Parrinello

  • Statistical Mechanics: Theory and Molecular Simulation

    Mark E. Tuckerman

  • The nature and transport mechanism of hydrated hydroxide ions in aqueous solution

    Mark E. Tuckerman;Dominik Marx;Michele Parrinello

  • Ab initio molecular dynamics simulation of the solvation and transport of hydronium and hydroxyl ions in water

    M. Tuckerman;K. Laasonen;K. Laasonen;M. Sprik;M. Parrinello

  • On the quantum nature of the shared proton in hydrogen bonds

    Mark E. Tuckerman;Dominik Marx;Michael L. Klein;Michele Parrinello

  • A reciprocal space based method for treating long range interactions in ab initio and force-field-based calculations in clusters

    Glenn J. Martyna;Mark E. Tuckerman

  • By-passing the Kohn-Sham equations with machine learning

    Felix Brockherde;Leslie Vogt;Li Li;Mark E. Tuckerman

  • Ab initio molecular dynamics simulation of the solvation and transport of H3O+ and OH- ions in water

    Mark Tuckerman;Kari Laasonen;Michiel Sprik;Michele Parrinello;Michele Parrinello

  • A Liouville-operator derived measure-preserving integrator for molecular dynamics simulations in the isothermal?isobaric ensemble

    Mark E Tuckerman;Mark E Tuckerman;José Alejandre;Roberto López-Rendón;Andrea L Jochim

  • Bypassing the Kohn-Sham equations with machine learning

    Felix Brockherde;Felix Brockherde;Leslie Vogt;Li Li;Mark E. Tuckerman;Mark E. Tuckerman

  • Report on the sixth blind test of organic crystal-structure prediction methods

    Anthony M. Reilly;Richard I. Cooper;Claire S. Adjiman;Saswata Bhattacharya

  • Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals

    Mark E. Tuckerman;Bruce J. Berne;Glenn J. Martyna;Michael L. Klein

  • The mechanism of proton conduction in phosphoric acid

    Linas Vilčiauskas;Mark E. Tuckerman;Gabriel Bester;Stephen J. Paddison

  • Understanding Modern Molecular Dynamics: Techniques and Applications

    Mark E. Tuckerman;Glenn J. Martyna

  • Aqueous Basic Solutions: Hydroxide Solvation, Structural Diffusion, and Comparison to the Hydrated Proton

    Dominik Marx;Amalendu Chandra;Mark E. Tuckerman

  • Ab initio molecular dynamics: Concepts, recent developments, and future trends

    Radu Iftimie;Peter Minary;Mark E. Tuckerman

  • Efficient and general algorithms for path integral Car–Parrinello molecular dynamics

    Mark E. Tuckerman;Dominik Marx;Michael L. Klein;Michele Parrinello

Frequent Co-Authors

Michael L. Klein
Michael L. Klein Temple University
Bruce J. Berne
Bruce J. Berne Columbia University
Stephen J. Paddison
Stephen J. Paddison University of Tennessee at Knoxville
Zlatko Bačić
Zlatko Bačić New York University
Dominik Marx
Dominik Marx Ruhr University Bochum
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Christopher J. Mundy
Christopher J. Mundy Pacific Northwest National Laboratory
Chulsung Bae
Chulsung Bae Rensselaer Polytechnic Institute
Bart Kahr
Bart Kahr New York University

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