Prashant Govindarajan

Prashant Govindarajan

PhD Student at Mila-Quebec AI Institute and Polytechnique Montréal

Publications

You can also find my articles on my Google Scholar profile.

A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation

Published in Digital Discovey, 2024

Property-driven AI-automated material discovery presents unique challenges owing to the complex nature of the chemical structural space and computationally expensive simulations. For crystalline solids, the band gap is an important property for designing semiconductors and batteries. However, optimizing crystals for a target band gap is difficult and not well-explored. Reinforcement learning (RL) shows promise towards optimizing crystals, as it can freely explore the chemical space. However, it relies on regular band gap evaluations, which can only be accurately computed through expensive Density Functional Theory (DFT) simulations. In this study, we propose an active learning-inspired pipeline that combines RL and DFT simulations for optimizing crystal compositions given a target band gap. The pipeline includes an RL policy for predicting atom types and a band gap network that is fine-tuned with DFT data. Preliminary results indicate the need for furthering the state-of-the-art to address the inherent challenges of the problem.

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Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Published in Digital Discovery, 2024

Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material discovery. Recent developments in generative and geometric deep learning have shown promising results in molecule and material discovery but often lack evaluation with high-accuracy computational methods. This work aims to design novel and stable crystalline materials conditioned on a desired band gap. To achieve conditional generation, we: 1. Formulate crystal design as a sequential decision-making problem, create relevant trajectories based on high-quality materials data, and use conservative Q-learning to learn a conditional policy from these trajectories. To do so, we formulate a reward function that incorporates constraints for energetic and electronic properties obtained directly from density functional theory (DFT) calculations; 2. Evaluate the generated materials from the policy using DFT calculations for both energy and band gap; 3. Compare our results to relevant baselines, including a random policy, behavioral cloning, and unconditioned policy learning. Our experiments show that conditioned policies achieve targeted crystal design and demonstrate the capability to perform crystal discovery evaluated with accurate and computationally expensive DFT calculations.

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Behavioral Cloning for Crystal Design

Published in ML4Materials Workshop at ICLR, 2023

Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.

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