Srimukh Prasad Veccham , Joonho Lee, Martin Head-GordonPolBE is an embedded many-body expansion method for mean-field theories like density functional theory. PolBE removes the bottleneck step and reduces the scaling to O(N2) from O(N3).
I am a graduate student advised by Prof. Martin Head-Gordon
in the Department of Chemistry at the University of California Berkeley. I am broadly interested in using numerical methods to solve physical problems. In my PhD, I have focused on developing and identifying methods to solve a range of problems in electronic structure theory. I am also interested in using machine learning methods to predict properties of chemical and biological systems. Before Berkeley, I graduated from Indian Institute of Technology Bombay with a Masters Degree in Chemistry.
I am expecting to graduate in December 2020. I am interested in Computational Chemist, Scientific Programming, and Machine Learning roles. Please feel free to email me if there are any roles I would be a good match for!
University of California, Berkeley
PhD Candidate, Theoretical and Computational Chemistry (August 2015 - Present)
Advisor: Professor Martin Head-Gordon
Indian Institute of Technology, Bombay
Masters of Science, Chemistry; Minor in Electrical Engineering (August 2010 - May 2015)
Department Rank #1
I am interested in gaining insight into physico-chemical systems using simulation and computations.
Along with Dr. Joonho Lee, we developed an embedded many-body expansion method for mean-field systems called the Polarized many-Body Expansion (PolBE). This method takes advantage of the locality of chemical interaction to devise a technique to compute the properties of molecular clusters at O(N2) cost instead of the traditional O(N3). This method is also can also take advantage of modern day high-performance computing architectures by parallelizing efficiently over any number of CPU cores. This technique has been implemented in C++ using Armadillo in Q-Chem .
I have also worked on various aspects of identification and assessment of materials for on-board vehicular hydrogen storage as a part of the Department of Energy’s Hydrogen Materials – Advanced Research Consortium ( HyMARC ). I have used computational methods to investigate metalated catecholates for adsorbing multiple hydrogens at a single site. Recently, I have comprehensively assessed the performance of 55 density functionals for hydrogen storage. For this purpose, I implemented an automated workflow in python using pandas for the end-to-end management of more than 20,000 data points with different metadata structures.
Recently, I explored an idea to predict protein function from protein sequence information using machine learning techniques. In this work, we used the sequence-based deep learning representation of protein sequences called UniRep to predict different properties of the protein like protein function, location of active sites, and role of active sites. Inspired multi-scale modeling in physics and chemistry, we used two different levels of representation: one for the protein sequence (UniRep) and another for the amino acid residue (message passing neural network based link to). The proposed model can identify the role of active site amino acids with 88% accuracy. This model was implemented in python using NumPy and TensorFlow. The implementation can be found on my Github .
Here's a link to my Google Scholar Page. The titles below are also hyperlinked to the full papers.
Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals
Srimukh Prasad Veccham , Martin Head-GordonCompilation of a comprehensive dataset along with highly accurate ab initio reference interaction energies for hydrogen storage applications. We recommend five (out of a total of 55 density functionals assessed) density functionals for predicting the interaction energies of hydrogen with binding units.
An assessment of strategies for the development of solid-state adsorbents for vehicular hydrogen storage
Mark D. Allendorf, Zeric Hulvey, Thomas Gennett, … Srimukh Prasad Veccham , Brandon C. WoodA comprehensive assessment of the state of the art synthetic, analytical, and computational strategies available for making and characterizing materials for hydrogen storage.
High-Temperature Hydrogen Storage of Multiple Molecules: Theoretical Insights from Metalated Catechols
E. Tsivion, Srimukh Prasad Veccham , Martin Head-GordonDensity functional theory was used to investigate metalated catechol decorations to UiO-66 Metal-Organic Framework for hydrogen storage. Our computations reveal that the resulting material has the potential to achieve Department of Energy’s 2020 target of 40g/L.
‘Conformational Simulation’ of Sulfamethizole by Molecular Complexation and Insights from Charge Density Analysis: Role of Intramolecular S···O Chalcogen Bonding
Sajesh P. Thomas, Srimukh Prasad Veccham , Louis J. Farrugia, Tayur N. Guru RowInvestigated the role of intramolecular chalcogen bonding in robustness of the conformation of sulfamethizole, a sulfonamide antibiotic.
Extensive production-level programming experience in Q-Chem (C++)
Software & Packages
Psi4, MRCC, Gaussian, NAMD, VMD, ChemOffice, Armadillo, Pandas, Matplotlib, SciPy, NumPy, Git
Python, C++, C, SQL
TensorFlow, PyTorch, Scikit-Learn, Deep Learning (Neural Networks, CNN, RNN)