Open Chemistry

Mentors: Student: Perminder Singh

In avogadrolibs repo, there are ambient occlusion / shading but nothing for fog or blur effects. Moreover, we currently lack real-time structure generation and efficient rendering. The overall goal is to bring new features like adding depth-of-field for fog and blur rendering options, adding tessellation shaders for efficient rendering and real time secondary structure generation.

Mentors: Student: RiyaSingh

The DeepChem project aims to create high-quality, open-source tools to democratize the use of deep learning in drug discovery, materials science, quantum chemistry, and biology. Among its suite of various machine learning models, it has a wide variety of graphs-based neural network model implementations aimed to solve the applications such as predicting the solubility of small drug-like molecules, binding affinity for small molecules to protein targets, analyzing protein structures, and existing useful descriptors. This project seeks to bring a new tool to the DeepChem suite for solving message passing problems based on the recent advancements in the GNNs research. The project aims to implement a Multiplex Molecular Graph Neural Network (MXMNet) model, a Graph Neural Network (GNN) to Deepchem.

Mentors: Student: Mayank Goel

The goal of this project is to develop an Active Learning based Bayesian Geometry Optimizer(ALBGO) using PyTorch and BoTorch for optimizing the conformers locally and globally in internal coordinates, with support for several computational models like force fields, quantum chemistry and ML-based force fields. Bayesian Optimization is a highly effective technique for global optimization of "black box" functions and tuning hyperparameters. Since it does not require gradients, it can efficiently optimize hyper parameters for a Deep neural network or even electronic structure methods without derivatives. The project involves using Bayesian Optimization with an active learning approach to optimize conformers. The major work involves finding the right set of kernels for the Gaussian Process model for different types of molecular variables like Vibrational energy, bond lengths etc. and tuning them. We also an active learning approach to help the algorithm converge towards the solution much more quickly. Finally, we benchmark and compare the performance of our optimizer against other conformer optimization methods in use, like BFGS and gradient-based optimization algorithms. This project has the potential to significantly improve the optimization capabilities of researchers in several fields by reducing the cost of optimization and improving optimization performance. The ALBGO algorithm will be a valuable addition to the optimization landscape, and its PyTorch-based implementation will make it easily accessible to a wide range of users including non-chemists.

Mentors: Student: Anchal Malani

CalcUS aims to democratize access to computational chemistry by providing a user-friendly web interface to simplify running and analyzing quantum mechanical calculations. These calculations allow the researchers to better understand the properties of molecules or the underlying mechanisms of chemical reactions. Quantum chemistry projects can involve performing calculations on a large number of structures (10-100) with different parameters(1-10). Currently, these calculations have to be launched individuallyand sequentially. This project aims to add the feature to make this process seamless and highly automated, from launching the calculations to reporting the results. This is a proposal to implement; ● Variation of the calculation web UI, aimed specifically at batch calculations with variable parameters ● Design and implement the workflow to handle these batch calculations ● Implement results gathering and reporting in a convenient format ● Write relevant unit and/or integration tests.

Mentors: Student: Žarko Ivković

The necessity of having an application for the automated generation of input files for computational chemistry codes is evident. The ccinput is an open-source command line tool and a python library. NW Chem is a popular, open-source software used in computational chemistry for many different areas. It’s free, easy to install, and can perform the vast majority of calculations any chemist might need. Therefore, I propose contributing to ccinput by adding a possibility to generate input files for NW Chem for main calculations.