Mark E. Tuckerman - US grants

Mark Tuckerman is supported by a CAREER award from the Theoretical and Computational Chemistry Program to study hydrogen bonding and proton transfer in strong acid/base hydrates and gas hydrate crystals using ab initio molecular dynamics and path integral molecular dynamics. He will also use density functional theoretical methods to study the surface chemistry involved in dehydration and Diels-Alders reactions of hydrocarbons on semiconductor surfaces. Tuckerman's teaching plan involves an integration of his research and interdisciplinary background into the curricula of graduate statistical mechanics and undergraduate freshman chemistry courses. Graphic visualization of computer simulations will be used at the undergraduate level. Graduate students will be taught simulations and apply the techniques to realistic problems.

Although most technologically important chemical processes occur in condensed phases, often with the use of a heterogeneous catalyst, little is known about the detailed molecular nature of these processes. Only recently have modern theoretical methods been applied to the microscopic details of chemical reactions in condensed phases. Tuckerman's research program is designed to use modern ab initio computational approaches, employing massively parallel computers, to study such processes thus deepening our theoretical understanding of the molecular details of such systems.

Roberto Car and Annabella Selloni of Princeton University are supported under the Information Technology Research Program (ITR) by the Division of Chemistry, the Division of Materials Research, and the Division of Advanced Computational Infrastructure and Research to make ab initio molecular dynamics simulations more effective and more accessible on high performance computing platforms. Co-PI's include Josep Torrellas and Laxmikant Kale of University of Illinois, Michael Klein of the University of Pennsylvania, Mark Tuckerman of New York University, Glenn Martyna of Indiana University, and Nicholas Nystrom of Carnegie Mellon University (via collaborative proposals CHE-0121357, CHE-0121302, CHE-0121375, CHE-0121367, and CHE-0121273, respectively). This team of computational chemists and computer scientists will develop new efficient and high accuracy methods, extensible open source software modules with desirable scaling properties, and novel hardware designs that will enable modeling of complex events and environments of interest to chemistry, materials science and engineering, geoscience, and biology.

Information technology (IT) has transformed computational science to the extent that realistic, atom-based simulations of key processes in chemistry, nanoscience and engineering, and biology can now be addressed using highly accurate simulations. This research can potentially impact the design of polymer-generating catalysts, nanoscale electronic devices, and artificial biomimetic catalysts.

Professor Mark Tuckerman, of New York University, is supported by Theoretical and Computational Chemistry to perform quantum-mechanical-based investigations on the structure of polypeptides, proton transport and the dynamics of silicate melts. A significant component of this research deals with general algorithmic issues that currently limit the efficiency of such calculations. The research deals with developing on-the-fly force techniques that allow one to treat the atomic and vibrational degrees of freedom semiclassically or quantum-mechanically using forces that are determined from quantum-mechanical treatment of the electronic degrees of freedom. Particular emphasis is on development of descrete-variational grids for solution of Schrodinger's equation and numerical scaling techniques that facilitate the simulation of uncommon events. The effort also aims at the calculation of localized orthogonal molecular orbitals.

This work deals with development of new computational algorithms that will be used to study the seemingly disparate technical problems confronted in the design of fuel cells, the simulation of biological molecules and the storage of nuclear waste materials. The common problem associated with these three topics is that one is most interested in understanding processes that, in relation to electronic interaction, occur infrequently and in determining how to control the rate of these processes.

With support from the Major Research Instrumentation (MRI) Program, the Department of Chemistry at New York University will acquire large-scale parallel computational resources for biological and materials modeling. This equipment will enhance research in a number of areas including a) the application of novel conformational sampling tools to protein structure prediction; b) modeling of DNA polymerase mechanisms; c) studies of metalloenzyme mechanisms; d) analysis of protein-ligand binding; e) accurate treatment of hydrogen-bond dynamics in supramolecular complexes; f) materials design for proton-exchange membranes; g) computationally aided design of novel RNAs; and h) development of linear scaling electronic structure algorithms.

A cluster of fast, modern computer workstations is vital to serving the computing needs of active research departments. Such a "computer network" also serves as a development environment for new theoretical codes and algorithms, provides state-of-the-art graphics and visualization facilities, and supports research in state-of-the-art applications of parallel processing. These studies will have a significant impact in a wide number of areas, including biochemistry and materials science.

Mark E. Tuckerman of New York University is supported by the Theoretical and Computational Chemistry program to develop and implement methods based on specialized variable transformations that are capable of sampling the conformational equilibria of complex systems. This approach reduces the effects of energy barriers without altering thermodynamic and equilibrium properties. Applications of these methods include small peptides as test cases, and fast-folding proteins. A second theme of the research is to develop pseudopotentials appropriate for use in quantum mechanical/molecular mechanical (QM/MM) methods. An initial application of this method is to study the catalytic mechanism of the enzyme histone deacetylase, which is linked to various types of cancer. This work is having a broad impact in developing sampling and search algorithms for bio-simulations. Codes developed in this research, as well as a database of pseudopotentials, are freely available on the PI''s web site. Research results are incorporated into the classroom at the undergraduate and graduate level. Outreach to the community includes non-technical talks to students and faculty at regional middle schools, high school, colleges and universities.

Mark Tuckerman of New York University is supported by an award from the Theory Models and Computational Method program of the Chemistry Division to carry out research, development, and application of novel methods for enhanced conformational sampling, free energy prediction, and hybrid QM/MM calculations. The aim of this project is to address directly measurable structural and dynamical quantities of complex chemical processes in the condensed phase in novel ways based on modeling and simulation. To that end, a novel approach to conformational sampling based on the introduction of specialized variable transformations in the classical canonical partition function is being developed. This approach reduces the effect of energy barriers without altering thermodynamic and equilibrium properties of the system. Applications include systems with increasing complexity, from peptides to fast-folding proteins.
Hence, there are potential benefits to fields ranging from human health (simulations of protein folding and miss-folding) to the rational design of novel materials. A second component of the project addresses chemical reactivity in large biomolecules such as enzymes via the quantum mechanical/molecular mechanical (QM/MM) method. A rigorous theory of molecular pseudo-potentials is developed for describing the interaction between the QM and MM subsystems. The new pseudo-potentials are to be gathered in a database made available to the community on the awardee's Web site.

Methodology development and its incorporation into user-friendly open-source software that is freely available to the community are components of the project that strongly impact computational chemistry and biology, leading to new approaches for solving complex problems in silico. Tuckerman's undergraduate and graduate students and postdoctoral researchers are a diverse and gender-balanced group. Notes of the graduate Statistical Mechanics course, currently available through the Web, are supplemented with advances in this project. Outreach seminars at the Mathematics Speakers Bureau (MSB) of the New York section of the Mathematical Association of America enrich the background of students and faculty of regional middle schools, high schools, colleges and universities on topics reaching beyond the traditional math curriculum.

Zlatko Bacic (PI) and Mark E. Tuckerman (co-PI) of New York University are supported by an award from the Chemical Structures, Dynamics and Mechanisms program of the Chemistry Division for a computational study aimed at achieving a fundamental understanding and a comprehensive theoretical description of the quantum dynamics, spectroscopy, and diffusion of hydrogen molecules inside the nanoscale cavities of diverse host materials, such as clathrate hydrates, fullerenes and carbon nanotubes, and metal-organic frameworks (MOFs). This will provide a broad theoretical framework, as well as quantitative predictions, indispensable for the planning, analysis, and interpretation of various types of spectroscopic measurements of these systems presently carried out by groups around the world. An array of robust theoretical approaches, ranging from high-dimensional quantum bound state and scattering methods to diffusion Monte Carlo and path integral simulations, will be brought to bear on these objectives. Another goal of this proposal is the accurate determination of multidimensional, anisotropic and anharmonic interaction potentials of the nanoconfined hydrogen, and in certain cases H2O, with the host materials. Accomplishing this will involve the combination of sophisticated quantum dynamics and ab initio electronic structure calculations. The eigenstate-resolved calculations will be complemented by path integral simulations directed at elucidating the temperature and concentration dependence of the energetics, free-energetics, spatial distributions, and diffusion of molecular hydrogen, especially in bulk clathrate hydrates and MOFs.

A major hurdle for the large-scale use of hydrogen as a clean and efficient energy carrier is developing ways to store it safely and economically. One possibility currently under intense investigation worldwide absorption in nanoporous materials. Quantitative understanding of the properties of molecular hydrogen under such conditions, and characterization of the interactions of hydrogen with various host environments is essential in joint experimental and theoretical efforts aimed at rational design of new media for hydrogen storage. If met, the challenge of developing efficient hydrogen-storage materials will have an enormous impact on emerging energy technologies. This research will be carried out by graduate and undergraduate students at NYU, including those from traditionally underrepresented groups. In addition, one of our experimental collaborators, Prof. Stephen FitzGerald, is at an undergraduate institution (Oberlin), and his students will be directly involved in the project.

An international team consisting of Teresa Head-Gordon and Martin Head-Gordon (University of California, Berkeley), Paul Nerenberg (Claremont McKenna College), David Case (Rutgers University), Jay Ponder (Washington University), Mark Tuckerman (New York University) with their UK collaborators: Lorna Smith and Neil Chue Hong (University of Edinburgh), Chris-Kriton Skylaris and Jonathan W. Essex (University of Southampton), Ilian Todorov (Daresbury Laboratory), Mario Antonioletti (EPCC) are are supported through the SI2-CHE program to develop and deploy robust and sustainable software for advanced potential energy surfaces. The greater accuracy introduced by improvements in the new generation of potential energy surfaces opens up several challenges in their manifestation as algorithms and software on current or emergent hardware platforms that in turn limits their wide adoption by the computational chemistry community. The research team is overcoming these obstacles via multiple but integrated directions: (1) to optimally implement advanced potential energy surfaces across multi-core and GPU enabled systems, (2) to develop a hierarchy of advanced polarizable models that alter the tradeoff between accuracy and computational speed,(3) to create new multiple time stepping methods; (4) to write a Quantum Mechanics/Molecular Mechanics (QM/MM ) application programing interface (API) that fully supports mutual polarization, (5) to adopt software best practices to ensure growth of a self-sustaining community and (6) to provide exemplar calculations with the new software in the several emerging application areas.

Molecular simulation and quantum chemistry software is an integral part of chemistry and chemical biology, and has been broadly adopted by academic researchers and industry scientists. Next generation scientific breakthroughs that utilize chemical software will be enabled by the deployment of state of the art theoretical models and algorithms that are translated into a sustainable software framework rapidly implemented on emergent high performance computing platforms. Potential energy surfaces describe the interactions between atoms. Advanced and highly accurate potential energy surfaces encounter software-related obstacles that inhibit their application to grand challenge chemistry problems. This UK and US consortium, representing a broad cross section of the computational chemistry software community, is working to directly tackle these obstacles. This US and UK collaboration between universities and High Performance Computing centers works to endure that chemical software investments made in advanced potential energy surface models has a long term payoff in community sustainability and the training of the next generation of scientists. Outreach and training workshops are organized around the emergence of the advanced potential energy software including an introductory molecular simulation software boot camp for undergraduate students.

The US based investigators are supported by the CHE and ACI divisions within NSF; the UK based investigators are supported by the EPSRC.

Mark Tuckerman of New York University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry division to develop theoretical and computational methods to address key challenges in determining accurate free energies. (1) In order to determine free energies, it is necessary to sample extensively the complex and extremely high-dimensional probability distribution of possible spatial configurations of the system. (2) Despite the extremely high dimensionality of a system, conformational free energies can often be characterized in terms of a few key variables; however, identifying such variables a priori is highly nontrivial. (3) Accurate free energies require an accurate yet computationally efficient model of the interactions between the atoms in a system. Tuckerman and his research group address these challenges through the development of novel computational techniques and apply these methods to a number of problems in biomolecular structure prediction and crystal polymorphism determination. This proposal is cofunded by the Condensed Matter and Materials Theory Program in the Division of Materials Research.

The importance of theory and computation in scientific disciplines such as chemistry, materials science, and biology is now well recognized and, in fact, was recently highlighted in the White House's document on the Materials Genome Initiative, which called for a synergy between theoretical and experimental scientists and industrial engineers. The overarching goal is to accelerate the time between concept and a marketable material. The role of theory and computation will be significantly enhanced through the development of new mathematical approaches that address outstanding challenges in these areas. Efficient computational protocols of the type to be developed in this proposal allow rapid and reliable predictions to be made that could accelerate the development of novel pharmaceuticals by pre-screening compounds that form undesirable polymorphs and speed the determination of structure in biomolecules. This information could not only lead to new drug design strategies involving novel compounds, but could provide important clues about how such molecules function in healthy and unhealthy cellular environments, which will increase our understanding of how certain types of diseases, leading to new therapy targets.

In this project funded by the Designing Materials to Revolutionize and Engineer our Future (DMREF) Program of the Chemistry Division, Professor Mark Tuckerman at New York University, Professor Chulsung Bae at Rensselaer Polytechnic Institute, Professor Michael Hickner of the Pennsylvania State University, and Professor Stephen Paddison of the University of Tennessee are designing, synthesizing, and testing new materials for use in alkaline fuel cells and discovering a set of rules for best practices in the development of future materials for fuel cell applications. As the United States seeks to enhance its energy security through identification and development of clean energy sources a range of technologies need to be leveraged in order to secure a sustainable energy supply. Electrochemical devices are an important part of this mix of technologies, and among these, fuel cells constitute some of the cleanest and most sustainable technologies. Several key hurdles to harnessing the potential of fuel cells (as well as various other electrochemical technologies) remain to be surmounted. The team of investigators are focusing on anion exchange membrane fuel cells that have advantages over other types of fuel cells in not requiring precious metals and being operable with a variety of fuels at low temperature. The project is employing a cohesive strategy involving mathematical and computer modeling of specific materials components that may, in turn, guide the synthesis of new materials, the characterization and testing of these materials in actual fuel cells, and the determination of optimal design principles to govern future materials engineering in this area. The project is also providing education and training for graduate and post-graduate researchers in both theoretical and experimental aspects of materials science and engineering, thus ensuring the competence and creativity of the next generation of STEM researchers.


The understanding and design of cost-effective and reliable polymer architectures for use as ion-conducting membranes is an important challenge facing emerging electrochemical device technologies. Currently available proton exchange membranes are problematic due to high cost, environmental concerns of fluoroplymers, and often poor performance under nonideal conditions. Additional challenges in proton exchange membranes fuel cell applications include difficult water management due to electro-osmosis, high fuel crossover, and the requirement of expensive platinum catalysts. Fuel cells based on anion exchange membranes have the potential to alleviate most of these problems. However, little systematic knowledge of how best to design these materials exists at present despite the fact that liquid-electrolyte alkaline fuel cells were among the first fuel cells to be developed. The team of researchers is applying an integrated, iterative theoretical-experimental approach towards the targeted syntheses of polymers, the first-principles computer simulations of specific polymer chemistries, the mathematical and experimental characterization of structures/morphologies, and the measurement and computational modeling of long-range hydroxide ion transport. Through this cohesive effort, the team of investigators is aiming to advance fundamental science and engineering knowledge in the area of fuel cells membranes and to deduce a set of fundamental design principles for anion exchange membranes that accelerate the time between concept and production of practically useful materials.

Mark Tuckerman of New York University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop methods and software for the prediction of molecular crystal structure. This award is cofunded by the CISE/ACI Software Reuse Venture Fund. In the science of materials, ordered arrays of molecules forming structures known as molecular crystals play an essential role in the pharmaceutical, electronics, and defense industries. Often, the crucial question is which crystals should be made for a particular application. It is worth noting that one of the most widely used pharmaceutical molecular crystals, aspirin, was discovered essentially by accident. Typically, in crystal engineering, it is necessary to screen large databases of potential candidate compounds. Unfortunately, making and characterizing molecular crystals in the laboratory is generally time consuming and costly, rendering a trial-and-error approach through such a database impractical. How many more important molecular crystal systems might be discovered if a systematic, targeted approach could be applied? Theory and computation, which can, in principle, rapidly predict molecular crystal structures and their properties, are uniquely poised to play a key role in creating such a targeted approach. What is needed, however, are robust algorithms for making these predictions. The Tuckerman group develops computational techniques and software for predicting the crystal structures a given compound can form and ranking them according to a thermodynamic property known as free energy, which has been recognized in the scientific community as the proper figure of merit for such a ranking but has remained an elusive property to determine. The Tuckerman group also adapts these algorithms for studying the conformational preferences of short chains of amino acids known as oligopeptides in order to explore the role these important biological molecules play in immunogenicity and the design of new classes of pharmaceuticals. Tuckerman and his coworkers are engaged in many software activities including developing a computer package for crystal structure prediction, improving the efficiency of their molecular dynamics software, PINY-MD and continuing to contribute software to many community software codes. All of the software developed in this project is made available to the broader research community.

The basic properties of molecular materials in the solid state are often strongly influenced by the details of their crystal structures and the existence of polymorphs. Experimental determination of these structures is costly and time-consuming, which places increased importance on the role of theory and computation. Similarly, the biochemical function of small oligopeptides, from immunogenicity to inhibition, is affected by their equilibrium conformations in different environments. Computational prediction of structure in complex systems such as these is challenging due to the so-called rare-event sampling problem on a rough potential energy landscape, which arises when attempting to study the equilibrium thermodynamics and kinetics of many complex systems. Roughness on an energy surface refers to the existence of high barriers to conformational and structural changes. The Tuckerman group has proposed to develop robust free-energy based enhanced sampling algorithms and software for overcoming the rare-event problem that arises in the crystal structure prediction and conformational sampling of oligopeptides, thereby allowing favored structures to be identified and thermodynamically ranked in an efficient manner. In the proposed methods, the free energy landscape is expressed in terms of select set of collective variables (CVs) designed to distinguish the different structural motifs in these systems. The CVs are first be subject to new surface navigation techniques in order to identify the minima and saddles points, collectively referred to as "landmarks" on the landscape, and then targeted for enhanced sampling in order to produce the free energy ranking of the landmarks. The new techniques are applied to predict the crystal structures and polymorphs of both rigid and flexible small organic molecules, to study the conformational free energy landscape of an immunogenic peptide binding to the major histocompatibility complex, and to understand the influence of mechanical force on the unfolding mechanism of â-hairpin peptide. Software creation will be accelerated via hackathons organized by the Tuckerman group. Education of students in rare-event methods is aided through workshops organized at New York University's global campus sites. Finally, the Tuckerman group reaches out to underrepresented groups via national organizations having a presence in New York City in order to help devise and participate in STEM-related educational activities.

In this project funded by the Chemical Structure, Dynamics and Mechanisms A program in the Division of Chemistry, Professors Zlatko Bacic and Mark Tuckerman of New York University are performing theoretical calculations on molecules confined in cages formed by other molecules, or within a single C60 "Buckyball" molecule. They are interested in understanding how molecules such as water (H2O), hydrogen (H2) and other small molecules move when they are in restricted spaces. This theoretical study will improve the interpretation of experimental measurements, for example the results of a technique called inelastic neutron scattering (INS). It will also have implications for the control of material properties at the molecular level (for advanced "nanoelectronics" applications), and possibly for the design of efficient and economical hydrogen storage media for energy technologies. Two graduate students are directly involved as researchers in this project. The conceptual advances and new results from this research will be integrated into educational materials for undergraduates. Finally, methodology developments achieved in this project will be incorporated into the Principal Investigators' user-friendly software tools that will be made freely available to the general community.

An array of sophisticated theoretical and computational approaches, some developed in the realization of this project, ranging from multidimensional bound-state and scattering methods to path-integral molecular dynamics (PIMD) simulations, is implemented in these investigations. The INS spectra of H2O confined in C60 are calculated with high accuracy utilizing the newly developed methodology for quantum simulation of the INS spectra of nanoconfined polyatomic molecules. The PIMD simulations of the crystalline H2O in C60, an extraordinary 3D cubic lattice of highly quantum H2O dipoles, each confined inside C60, shed light on its dielectric properties and free energetics, including the ferroelectric phase transition, arising from the many-body dipolar correlations. Path-integral simulations also probe the temperature and pressure dependence of the free energetics and approximate diffusion rates of H2 and D2 molecules in the sII clathrate hydrates, simple and binary, accounting for quantum effects and framework flexibility. These studies address the fundamental problem of the diffusion of molecular hydrogen in the quantum regime, inside a chemically and structurally complex environment. Quantum treatment of the condensed-phase effects, including the proton disorder of the framework water molecules, on the "rattling" dynamics and the INS spectra of H2 in the sII clathrate hydrate addresses the general question of how the dynamical and spectroscopic properties of guest molecules inside a host environment evolve and approach their bulk limits.