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Neel Redkar

Neel Redkar

2023 Davidson Fellow
$25,000 Scholarship

Age: 18
Hometown: San Ramon, CA

Engineering: “CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture

About Neel

Hey there! My name is Neel Redkar and if I had to describe myself in one word it would be a maker. I love to create, be it research projects, startups, or concocting a new cookie recipe. Odds are if you ever bump into me, I’ll be knee deep in a research paper rabbit hole or hacking away on (yet another) side project. I’m currently an incoming first year at the University of California, Los Angeles, hoping to double major in Philosophy and Computer Science.

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"To me being a Davidson Fellow means being able to interact with and be friends with other amazing creators, working together to make the world a better place!"

Project Description

Almost 51 percent of US carbon dioxide (CO2) emissions are from factories. Unfortunately, carbon capture (storage/conversion of CO2 emissions) is currently inefficient and unprofitable. One new approach, electrocatalysis, splits trapped CO2 into sellable carbon monoxide (CO) and oxygen (O2); however, this method is still expensive and inefficient. My goal was to improve this idea by designing Metal Organic Frameworks (essentially catalytic metal sponges) for this conversion while maintaining low production costs. Using a novel biology-inspired neural network I designed, I found a MOF that was more conversion- efficient, consumed less energy, and cost significantly less than existing MOFs—decreasing energy costs and providing usable hydrocarbon fuel for revenue. MIT’s MOFSimplify tests validated that the MOF could be successfully produced.

Deeper Dive

Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emission—almost 51% of total US carbon emissions are from factories. The effort to prevent CO2 from going into the environment is called carbon capture. Carbon capture decreases CO2 and also yields steam that can be used to produce energy, decreasing net energy costs by 25-40%, though the isolated CO2 must be sequestered through expensive means. Current materials used in CO2 capture are lacking either in efficiency, sustainability, or cost. Electrocatalysis of CO2 is a new approach where CO2 can be split and the components used industrially as fuel, saving transportation costs, creating financial incentives. Metal Organic Frameworks (MOFs) are crystals made of organo-metals that can adsorb, filter, and electro-catalyze CO2. The current available MOFs are expensive to manufacture and inefficient. As an avid research paper nerd that often falls into deep rabbit holes like this, an idea popped into my head. What if I could cross apply my knowledge in AI to make this process more efficient? As a maker, unafraid, I dove deeper into the literature.

Therefore, the engineering goal for this project was to design a novel MOF that can adsorb CO2 and use electrocatalysis to convert it to CO and O efficiently while maintaining a low manufacturing cost. After months of iterations, a novel biology-inspired active transfer learning neural network was developed, due to limited available data on 15 MOFs. Using the Cambridge Science Database with 10K MOFs, the model used incremental mutations to fit a trained fitness hyper-heuristic function. Eventually, a novel Se-MOF was converged on. The structure was validated by MIT MOFSimplify test showing the model had a complex understanding of the material space. This novel network could be implemented for other gas separations and catalysis applications using sparse datasets.

The greatest challenge would have to be tackling the extreme lack of data in niche areas of MOF research like electrocatalysis. To tackle the problem, I initially tried various training methods like trying to generate MOF’s with rollouts similar to chess algorithms or using techniques like actor critic models. After trying various algorithms I realized that none of the regular algorithms would work for this task—and that I needed to try something new. What eventually worked the best was actually taking inspiration from my AP Biology class! By essentially taking ideas from evolutionary radiation, the same way isolated species diffuse into new niches, I was able to create an algorithm that used extremely low amounts of data—decreasing the normal 10k datapoints to just 15! The pandemic also affected my project in a significant way, where I was then unable to gain access to a wet lab to synthesize the MOF—as the project was done independently— though to combat this I was able to perform rigorous computational tests & literature cross references.

In the face of continuous industrial pollution and the missing financial incentives to go green, this method could be a game-changer. Essentially by providing a way to turn CO2 into fuel, factories could gain profits from emitting less CO2 and selling the fuel they create. It offers significant financial incentives, potentially tipping factories towards becoming carbon neutral. The Metal-Organic Framework (MOF) could revolutionize the carbon capture process by eliminating sequestration, taking needless pipelines and pumping stations out of the equation. Turning CO2 into a net positive resource would be a great first step for environmental transition. Plus, the O2 that's produced could simply be returned as a net surplus to our atmosphere or repurposed industrially. The algorithm itself is hugely multipurpose. Possibilities include revolutions in artificial photosynthesis, water treatment, energy efficient desalination/electrolysis, and minimal data gas separation tasks—bringing the countless promised applications of MOFs from months of development to just a couple days.


If you could magically become fluent in any language, what would it be?

If fluent means complete mastery over a language, it would have to be machine language which would give me complete mastery over anything computer related. Though in general I feel like learning a symbolic language like Mandarin would be extremely interesting to see how it might affect how it might affect analytical processing.

What is your favorite tradition or holiday?

I love hands on making—baking and cooking included! Thanksgiving is my favorite holiday because of how I get to go hands on and share my creations (food) with others (my family). The themes of togetherness & thankfulness are values I especially love too.

What are the top three foreign countries you’d like to visit?

Switzerland—For the amazing natural beauty & extremely interesting political past.

Japan—I feel like the culture here is at a dichotomy to where I grew up in, and I find the differences fascinating.

Iceland—I have visited here before, but the raw naturalness & force of nature never seize to amaze me.

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In The News

San Francisco – The Davidson Fellows Scholarship Program has announced the 2023 scholarship winners. Among the honorees is 18-year-old Neel Redkar of San Ramon. Redkar won a $25,000 scholarship for his project, CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture. He is one of only 21 students across the country to be recognized as a 2023 scholarship winner.

Download the full press release here