The Batista Group
Pablo Videla

Pablo Videla

Understanding molecule-interface interactions: SFG of proteins, catalysts and hydrocarbons
Understanding the interaction of molecules with interfaces is of fundamental importance since a lot of processes are controlled by the particular way the molecule and surface ‘talk’ to each other. For example, the catalytic efficiency for the reduction of CO2 to CO by surface-immobilized Rhenium complexes crucially depends on the particular crystal face of the substrate used, whereas the stability of molecules of environmental importance, such as terpenes or carbamates, varies depending on particular surface-molecule interactions. One of the main focus of the group is to unravel how the underlying substrate-adsorbate interactions modulated the orientation, energetics and reactivity of different molecules at interfaces using a variety of computational methods ranging from Molecular Dynamics simulations to Quantum Mechanics calculations. Since a lot of the experimental information of substrate-molecule interactions come from surface-specific spectroscopies such as Sum Frequency Generation (SFG), the development of theoretical models to simulate and interpret the different spectral features of the SFG spectra is an active research area of the group. By combining electronic structure calculations with computational modelling, we have simulated both 1D-SFG and 2D-SFG for rigid and flexible molecules on different interface environment, providing insight into the interpretation of congested experimental spectral signals and disentangling the influence of surface interactions in determining molecular structure, orientation and ordering at the interfaces. This project involves an active collaboration with different experimental groups: Elsa Yan , Clifford Kubiak , Tianquan Lian , Poul Petersen , Luis Vellarde , Franz Geiger .

Krystle Reiss

Krystle Reiss

Natural Water Oxidation and Oxygen Evolution
My research primarily consists of the study of the oxygen-evolving complex (OEC) of photosystem II. The sole producer of molecular oxygen found in nature, the OEC is a unique metal complex composed of four highly charged manganese centers held together by a network of μ-oxo bridges and protein residues. While a fascinating system in itself, our ultimate goal is to use the OEC to create synthetic mimics for renewable fuel production. Our primary tools for studying this complex system are two-layer QM/MM models, which take advantage of the low cost of molecular mechanics (MM) for the large protein layer while saving expensive quantum mechanics (QM) for the much smaller OEC. With these model systems in hand, we can calculate IR spectra, S-state transitions, EXAFS, and other properties that can be matched to real world experiments. We have also built parameters for a fully classical OEC that can be implemented in molecular dynamics simulations to compliment the more static QM/MM models.

Classical Molecular Dynamics and Allosteric Pathways
We use classical MD simulations to explore a number of other protein systems, including MIF and PTPs. In these systems, we are typically exploring allosteric pathways, which are prime targets for drug development. We work closely with several experimental groups, both at Yale and beyond, meeting weekly to discuss results and ongoing projects.

Ray Kelly

Ray Kelly

Controlling Catalysis with Electric Fields
We are designing catalytic systems that allow for the control of reaction thermodynamics and kinetics by electric fields. The attachment of molecular catalysts to electrode surfaces enables their control by interfacial electric fields. Our fundamental theoretical studies provide design rules for catalysts that can exploit electric field effects. Simply modulating the applied potential can significantly alter the reactivity of these catalysts. We have theoretically demonstrated the precise control of the hydricity of a surface-attached catalyst. Now, we are working to design field-controllable catalytic systems with unprecedented reactivity. This work is done in collaboration with the experimental groups of Tianquan Lian at Emory University and Clifford Kubiak at the University of California, San Diego.

Catalyst Design and Mechanisms
Addressing contemporary challenges in alternative energy requires the design of improved electrocatalysts with higher selectivity and selectivity. As part of the Yale Solar Group Collaboration (with Brudvig, Crabtree, and Schmuttenmaer), we are working on new electrocatalysts for water oxidation using earth-abundant metals. Further, we are designing catalysts for the selective reduction of carbon dioxide to CO, without the production of formate or hydrogen.

Additionally, we are working to understand catalysis on noble metal surfaces. In particular, we are investigating the selective transfer hydrogenation from tertiary amines alkynes yielding cis alkenes and the aromatization of cyclic enones to form phenols. DFT calculations elucidate the effects of metal surface and hydrogen donor on selectivity. This work is a collaboration with the experimental groups of Gary Haller at Yale and Eszter Baráth at Technische Universität München.

Conductivity in Protein Crystals
Amyloid proteins are important candidates for biomaterials due to their ability to form highly-ordered, self-replicating structures. We have investigated the conductivity of short amyloid peptides to obtain design rules for enhanced conductivity. We find that stacked tyrosines allow for micrometer-long conductivity through a hole hopping mechanism, with the energetics and proximity of the proton acceptor determining the transport rate. This work was performed with the Malvankar Group at Yale.

Jessica Freeze

Jessica Freeze

Machine Learning
Inverse design of molecules for applications in green energy and environmental resuscitation can be greatly aided by implementation and design of machine learning methods. Predicting properties of catalytic interest from cheaply provided input parameters offers increased throughput and speed in the generation of crucial innovations for preserving and healing our environments.

Ammonia Oxidation
Destruction of waterways through nitrification from fertilizer run-off presents a current and pressing problem. Designing catalysts to oxidize ammonia to nitrogen gas may be a solution. Efficient design of these catalysts and understanding their mechanisms will assist in these goals. Throughout design it is paramount that implementation side effects be examined and optimized so as to avoid possible harm via their insertion.

Quantum Computing
The promise of exponential speedup and the inclusion of interactions previously thought to be too expensive in chemical calculations positions quantum computing as a key asset for future chemists. In the regime of near-term quantum computers, we aim to guide innovation towards solving real chemical challenges on the quantum computers of today.

Facheng Guo

Facheng Guo

Mars van Krevelen Catalytic Methane Combustion via Formate Intermediates Over Hematite: Surface Studies and DFT (collaboration with Pfefferle Group at Yale)
Effective methane utilization for clean power generation and value-added chemicals has been the subject of growing attention worldwide for decades, yet challenges persist mostly in relation to methane activation under mild conditions. Here, we report an earth-abundant material, hematite, to be a highly effective and thermally stable catalyst for catalytic methane combustion (CMC) at low temperatures (< 500°C), with performance comparable to that of precious metal-based catalysts. I am focusing on the mechanism of this low-temperature reaction.

Surface-Attached Catalysts (collaboration with Lian Group at Emory and Kubiak Group at UCSD)
Heterogenization of homogenous catalysts on electrode surfaces provides a valuable approach for characterization of catalytic processes under in operando conditions using surface selective spectroelectrochemical methods. Ligand design plays a central role in the attachment mode and the resulting functionality of the heterogenized catalyst, as determined by the orientation of the catalyst relative to the surface and the nature of specific interactions that modulate the redox properties under the heterogeneous electrode conditions. I simulate the sum frequency generation spectra for catalysts standing on electrode surfaces.

Sooting Tendencies of Phenolic Hydrocarbons
Lignin is an abundant renewable energy resource that constitutes a significant fraction of all biomass. Its chemical structure is a complex network of monolignol subunits that contain hydroxy-substituted benzenoid rings; thus, fuel processing of lignin produces phenols. The objectives of this study were to measure the sooting tendencies of lignin-derived phenols, compare them to the aromatic hydrocarbons that are found in current fuels, and use simulations to identify the relevant chemical kinetic pathways. I am working on the experimental part of this project.

Matthew Guberman-Pfeffer

Matthew Guberman-Pfeffer

From the modeling of antigen recognition to the spectral modulation of light harvesting pigments and the structural basis for electrically conductive bacterial filaments, my research seeks to disclose the “secret motions and causes” of phenomena “for the effecting of all things possible” (Sir Francis Bacon), I love using computational methods to obtain structural or mechanistic insights that may aid the development of new materials. My postdoctoral research currently focuses on elucidating the mechanisms of energy and electron transfer in natural and synthetic systems.

Ningyi Lyu

Ningyi Lyu

Quantum Dynamics with Tensor Decomposition Methods
Recent developments in tensor decomposition methods have helped to alleviate the exponentially high burden for computations on high-dimensional tensors. Since the many-body quantum system wavefunctions are by postulation high-dimensional tensors, these methods provide practical tools to compute, for example time propagation, on these wavefunctions. My research focuses on exploiting tensor decomposition methods to simulate molecular properties that are of chemical interest.

Micheline Soley

Micheline Soley

My goal is to apply quantum control, chemistry, and spectroscopy to research at the forefront of quantum science and quantum computing. My current projects include the design of global optimization algorithms using low-rank tensor-train decompositions and qubits, simulation of pump-probe spectra for dissociating and isomerizing molecules, and development of quantum propagation schemes for highly multidimensional systems. I am exploring the extension of the theory of reflectionless scattering modes to controlled chemistry and implementation of new control schemes in circuit QED quantum computing systems. This research in the Batista Group is in collaboration with Professor Steve Girvin and Professor Douglas Stone at Yale University, Dr. Erik T. J. Nibbering at the Max-Born Institute, Paul Bergold at the Technical University of Munich, and Professor Alex Gorodetsky at the University of Michigan.

Brandon Allen

Brandon Allen

My research is mainly focused on understanding the biophysical mechanisms behind protein conformational changes through molecular dynamics and quantum dynamics. These dynamics are analyzed with tools employed from graph theory, information theory, and network analysis. I am also interested in quantum computers, specifically in their application to understanding biological systems.

Haote Li

Haote Li

Machine learning is a rapidly evolving field that brings new ideas into academia every day. My research focuses on combining machine learning with chemistry. The goal is to develop novel methods that aid new chemistry findings. For example, my past works involve using the sequence-to-sequence model to generate new molecules with desired chemical properties and predicting time-evolving functions that explain chemical phenomena. In addition, since the invention of the graphical neural network (GNN) has created a very natural description of molecules, I am studying this model to make accurate predictions for the NMR shifts of molecules and protein primary sequences.