Our Team
Describe your team here.
B.A., Chemistry and Mathematics, University of Pennsylvania, Philadelphia, PA
Ph.D., Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL (Sharon Hammes-Schiffer)
Postdoctoral Research Associate, Department of Chemistry, Princeton University, Princeton, NJ (CCSC CSI)
Associate Research Scientist, Department of Chemistry, Columbia University, New York, NY (CCCE)
Research interests:
- Electrochemical interfaces
- Charge transfer
- Electronic structure
- Machine learning for molecular simulation
Steven joined Schrödinger in 2018 as a Postdoctoral Fellow. He is responsible for developing force fields specific to metal complexes to advance metalloenzyme drug discovery. Steven obtained his Ph.D. from The University of Toledo, where he was awarded an ORNL research fellowship. His thesis work focused on the application of neutron scattering and computational methods to gain a deeper understanding of enzymatic catalysis.
M.S. Chemistry, Indian Institute of Technology, Madras
Ph.D. Physical Chemistry, Indiana University (Krishnan Raghavachari)
Research Interest
Force Field Development
Supramolecular Systems
Fragment-Based Methods
Battery Simulation
Robert joined Schrödinger in 2009, is responsible for advancing Schrödinger’s computational science platform. He also leads the computational chemistry team within Schrödinger’s drug discovery group. Robert obtained his Ph.D. from Columbia University, where he was awarded NSF and DHS research fellowships. His thesis work with Professor Richard Friesner involved developing methods to quantify the role of solvent in protein-ligand binding. Robert has co-authored multiple patent applications, and continues to publish extensively on a wide variety of topics in computational chemistry.
Ph.D., Theoretical Chemistry, University of Pennsylvania (Nitzan Group)
Research Interests:
- Electronic structure methods
- Battery simulation
Mat is responsible for leading the materials science program at Schrödinger. Prior to joining Schrödinger in 2012, he was a senior scientist and account manager at Materials Design, Inc. and Accelrys, Inc. Before that he held the prestigious E.R. Davidson Fellowship in theoretical chemistry at Indiana University and earlier was the Manager of the Scientific Simulation and Modeling Group at Zyvex Corporation. Mat has worked with Fortune 500 companies to advance the adoption of atomic-scale materials modeling techniques in diverse industries including aerospace, electronics and specialty chemicals. He has made significant research contributions in areas such as computational spectroscopy, organic optoelectronic materials, nanocarbon-polymer interfaces, thin-film precursors and deposition processes and battery electrolyte additives; with his work being cited more than 5000 times.
Leif obtained his Ph.D. from Ohio State University in 2011 where he worked with John M. Herbert and was a presidential fellow. His doctoral work included development of mixed quantum classical dynamical methods applied to the aqueous electron and development of fragment based ab-initio methods utilizing Symmetry Adapted Perturbation Theory. Following his doctoral work Leif studied non-adiabatic dynamics with John C. Tully at Yale University. Since joining Schrodinger in 2013 as a scientist Leif has focused on developing novel automation methodologies for transition state finding and reactivity predictions. More recently he has worked on expanding the domain of applicability of transferable neural network potential energy surfaces to include applications to ionic systems, intermolecular interactions and reactive regions of the potential energy surface.
Karl joined Schrödinger in 2013. He is responsible for machine learning research and development across the company. Karl received his Bachelor’s degree in Computer Science from the University of Virginia, and his Master’s degree specializing in Machine Learning from Georgia Tech. Recently he has been concentrating on molecular generative design, combining computational chemistry and chemoinformatics approaches to molecular property prediction, and general purpose machine learned energy potentials.
James is a Senior Machine Learning Scientist and developer of Schrödinger-ANI. He joined Schrödinger in 2016. He is responsible for the implementation of neural network force fields and for research into novel neural network methods including charge effects. Prior to his role in the Machine Learning group, he worked on the creation of the OPLS3e force field and made major improvements to Schrödinger's automated Force Field Builder. He received his Ph.D. in Chemical Engineering from Cornell University under Prof. Paulette Clancy, studying all-atom modeling for solution-processed solar cell materials.
BA Chemistry and Chemical Physics, Rutgers University (G.C.Dismukes) 2014-2018.
Friesner Group and Reichman Group
Research interests- Localized Electronic Structure methods
- Strongly Correlated materials
- Electrocatalysis
- Photochemistry
- Artificial Photosynthesis
- Lithium-based energy storage
Ph.D. Materials Science and Engineering, Seoul National University (2020)
B.S. Materials Science and Engineering, Seoul National University (2015)
Urban Group (https://urban-group.cheme.columbia.edu)
Research interests
Development and application of thermodynamic methods to the electrolyte/anode interface in Li-ion batteries.
B.S. Chemistry, Peking University (Hong Jiang)
Ph.D. Chemistry, MIT (Troy Van Voorhis)
Berkelbach Group
Research Interests
• New methods for computing electronic structure
• Quantum embedding
• Electronic excited states
B.S. Chemistry Brooklyn College/Macaulay Honors College (City University of New York) (Alexander Greer) 2013
M.S. Computational Chemistry, Yale University 2016
Ph.D. Computational Chemistry, Yale University (Victor Batista) 2018
Research Interests
- Computational chemistry
- Physical organic chemistry
- Drug discovery
- Solar fuels
- Vibrational spectroscopy
- Catalysis
- Metalloproteins
- Quantum Monte Carlo
- Polymers
- Li batteries
Research Interests:
Development of machine learning models for electrode-electrolyte interfaces in Li-ion batteries
Anand Chandrasekaran joined Schrodinger in 2019 as an Applications Scientist focusing on Machine Learning techniques in Materials Science. He graduated from the group of Prof.Nicola Marzari in the Swiss Federal Institute of Technology, Lausanne with a PhD in Materials Science. Before joining Schrodinger, Anand worked in the group of Prof. Rampi Ramprasad on a number of topics such as polymer informatics, machine-learning force-fields, and machine-learning for electronic structure calculations.