Scientists develop machine-learning method to predict the behavior of molecules

October 11, 2017, New York University
A new learning algorithm is illustrated on a molecule known as malonaldehyde that undergoes an internal chemical reaction. The distribution of red points corresponds molecular configurations used to train the algorithm. The blue points represent configurations generated independently by the learning algorithm. The turquoise points confirm the predictions in an independent numerical experiment. Credit: Leslie Vogt.

An international, interdisciplinary research team of scientists has come up with a machine-learning method that predicts molecular behavior, a breakthrough that can aid in the development of pharmaceuticals and the design of new molecules that can be used to enhance the performance of emerging battery technologies, solar cells, and digital displays.

The work appears in the journal Nature Communications.

"By identifying patterns in , 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," explains New York University's Mark Tuckerman, a professor of chemistry and mathematics and one of the paper's primary authors.

The paper's other primary authors were Klaus-Robert Müller of Berlin's Technische Universität (TUB) and the University of California Irvine's Kieron Burke.

The work combines innovations in machine learning with physics and chemistry. Data-driven approaches, particularly in the area of machine learning, allow everyday devices to learn automatically from limited sample data and, subsequently, to act on new input information. Such approaches have transformed how we carry out common tasks like online searching, text analysis, image recognition, and language translation.

In recent years, related development has occurred in the natural sciences, with efforts directed toward engineering, materials science, and molecular design. However, machine- learning approaches in these fields have generally not explored the creation of methodologies—tools that could advance science in ways that have already been achieved in banking and public safety.

The research team created a machine that can learn complex interatomic interactions, which are normally prescribed by complex , without having to perform such intricate calculations.

In constructing their machine, the researchers created a small sample set of the molecule they wished to study in order to train the algorithm and then used the machine to simulate complex chemical behavior within the molecule. As an illustrative example, they chose a chemical process that occurs within a simple molecule known as malonaldehyde. To weigh the viability of the tool, they examined how the machine predicted the chemical behavior and then compared their prediction with our current chemical understanding of the molecule. The results revealed how much the machine could learn from the limited training data it had been given.

"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," remarks Klaus-Robert Müller, professor for machine learning at Technical University of Berlin.

A video demonstrating, for the first time, a chemical process that was modelled by machine learning—a proton transferring within the malonaldehyde molecule—can be viewed here: wp.nyu.edu/tuckerman_group/res … ch/machine-learning/ .

Explore further: Chemists teach computer program to model forces between atoms accurately

More information: Felix Brockherde et al, Bypassing the Kohn-Sham equations with machine learning, Nature Communications (2017). DOI: 10.1038/s41467-017-00839-3

Related Stories

Quantum machine learning

September 14, 2017

Language acquisition in young children is apparently connected with their ability to detect patterns. In their learning process, they search for patterns in the data set that help them identify and optimize grammar structures ...

Machine learning tackles quantum error correction

August 15, 2017

(Phys.org)—Physicists have applied the ability of machine learning algorithms to learn from experience to one of the biggest challenges currently facing quantum computing: quantum error correction, which is used to design ...

Machine learning can predict rate of memory change

July 31, 2017

In new research published today, researchers have created a machine learning algorithm that is able to form two distinct groups of people who have early memory problems known as mild cognitive impairment. The algorithm was ...

Recommended for you

AI and 5G in focus at top mobile fair

February 24, 2018

Phone makers will seek to entice new buyers with better cameras and bigger screens at the world's biggest mobile fair starting Monday in Spain after a year of flat smartphone sales.

Google Assistant adds more languages in global push

February 23, 2018

Google said Friday its digital assistant software would be available in more than 30 languages by the end of the years as it steps up its artificial intelligence efforts against Amazon and others.

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.