Though much noise has been made of what’s still to come from artificial intelligence (AI), the technology has already changed our daily lives. Machine learning-powered image recognition, text analysis, and language translation tools allow us to navigate the world in previously unimagined ways, and our mobile devices can now predict so much of our behavior based on our past actions.
Now, an international, interdisciplinary team of researchers has devised a way to use machine learning to do something far more complex than foresee a smartphone user’s next move. They’ve built a machine that can predict molecular behavior, a feat that previously required very complex quantum calculations. Their study has been published in Nature Communications.
To create this system that can predict molecular behavior, the researchers trained an algorithm using a small sample set featuring a simple molecule called malonaldehyde. Then, they had the machine predict the complex chemical behaviors of this molecule. The researchers compared those predicted simulations with their current chemical understanding of malonaldehyde and found that the machine was able to learn how the molecule would behave using the limited data it had been trained on.
“By identifying patterns in molecular behavior, the learning algorithm or ‘machine’ we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena,” researcher Mark Tuckerman of New York University explained in a press release.
This work is yet another example of AI’s ability to impact a wide variety of industries, with molecular science joining everything from medical research to psychology and behavioral science. The research demonstrates how machine learning methods can be used to perform difficult tasks of all types so long as the systems are given sufficient data.
The researchers expect that this ability to predict molecular behavior could greatly contribute to the development of pharmaceuticals, as well as simulate molecular designs crucial for improving the performance of today’s new battery technologies, solar cells, and digital displays — basically, anything that used to rely on complex quantum mechanical calculations to model atomic or molecular interactions can benefit from their work.
While their machine does make it possible to model this behavior without involving intricate calculations, streamlining that complicated task is just the jumping-off point, according to Müller: “Now we have reached the ability to not only use AI to learn from data, but we can probe the AI model to further our scientific understanding and gain new insights.”