Designing New Molecules with Machine Learning

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Designing New Molecules with Machine Learning

Designing New Molecules with Machine Learning – My group uses molecular simulations, high through-put computing, and machine learning methods to predict the properties of small molecules and how they’re going to interact with surfaces. We do many of these calculations in order to get an understanding of how these properties impact various targets of interest for experimental applications.

And we collect these results in a database and use machine learning techniques to predict properties of new molecules that we haven’t studied yet. A recurring theme in chemical engineering is really how you design molecules for applications. These are things like, “What sort of functional groups are important?” and “How long your polymer chain should be.” These sorts of questions come up all the time, and we usually address these questions sort of with intuition.

A group of students work on the problem and they sort of get an idea of what’s important, and then they make some tweaks, and they try to optimize for a given property. But we’re starting to discover that we can do a much better job of predicting these things up front and using information from different sources to do this more efficiently.

This is where machine learning methods have really helped revolutionize the field of chemical engineering, have really helped get us in this data-driven mindset. Molecular simulations are a great way to augment the experimental data that we have. So it fits really naturally into this workflow: we can use molecular simulations to come up with a quick estimate.

If something is interesting, we do a more detailed calculation. If it’s still interesting, we just do the experiments, and we figure out if it’s actually worth following up on. And this sort of work flow should allow us to really accelerate everything we do in chemical engineering.

One idea that we’re working on right now is electrochemically reducing carbon dioxide to building blocks of interest to the chemical industry. And this idea is very powerful: we can basically reduce waste CO2, and you can also make building-blocks that otherwise would have to come from fossil fuels.

If we’re going to solve a lot of the energy and climate challenges that are on the horizon, we really need new chemical processes. And not just the processes, but we need the things that go into them—like catalysts and surfactants and other ways of dispersing nanomaterials—to really catch up and enable new ways of doing chemical transformations.

Our group is working on ways to make this process more efficient. The students at CMU have been really fantastic for this kind of work. In chemical engineering specifically, we have a strong background in systems engineering and mathematical techniques, a systematic mindset. This idea of data-driven predictions is really core to what a lot of the students in the department want to do.

One of the great things of working with molecular simulations is that as computing costs have come down and the methods have gotten better, the time scale for getting results is quite quick, and we can very efficiently investigate materials of interest to experimentalists. So we already have some interesting results for inner metallics for CO2 reduction.

And we’re hoping we can get these targets out to experimentalists by the end of the year.

 

 

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