17-Dec-2021 - Technische Universität Berlin

New algorithm drives use of AI in material sciences

Tracking Spooky Action at a Distance: New Deep Learning algorithm learns complex molecular dynamics

The use of AI in classical sciences such as chemistry, physics, or mathematics remains largely uncharted territory. Researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) at TU Berlin and Google Research have successfully developed an algorithm to precisely and efficiently predict the potential energy state of individual molecules using quantum mechanical data. Their findings, which offer entirely new opportunities for material scientists, have now been published in the paper "SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects" in Nature Communications.

"Quantum mechanics, among other things, examines the chemical and physical properties of a molecule based on the spatial arrangement of its atoms. Chemical reactions occur based on how several molecules interact with each other and are a multidimensional process," explains BIFOLD co-director Professor Dr. Klaus-Robert Müller. Being able to predict and model the individual steps of a chemical reaction at the molecular or even atomic level is a long-held dream of many material scientists.

Every individual atom in focus

The potential energy surface, which refers to the dependence of a molecule’s energy on the arrangement of its atomic nuclei, plays a key role in chemical reactivity. Knowledge of the exact potential energy surface of a molecule allows researchers to simulate the movement of individual atoms, such as during a chemical reaction. As a result, they gain a better understanding of the atoms’ dynamic, quantum mechanical properties and can precisely predict reaction processes and outcomes. "Imagine the potential energy surface as a landscape with mountains and valleys. Like a marble rolling over a miniature version of this landscape, the movement of atoms is determined by the peaks and valleys of the potential energy surface: this is called molecular dynamics," explains Dr. Oliver Unke, researcher at Google Research in Berlin.

Unlike many other fields of application of machine learning, where there is a nearly limitless supply of data for AI, generally only very few quantum mechanical reference data are available to predict potential energy surfaces, data which are only obtained through tremendous computing power. "On the one hand, exact mathematical modelling of molecular dynamic properties can save the need for expensive and time-consuming lab experiments. On the other hand, however, it requires disproportionately high computing power. We hope that our novel Deep Learning algorithm – a so-called transformer model which takes a molecule’s charge and spin into consideration – will lead to new findings in chemistry, biology, and material science while requiring significantly less computing power," says Müller.

In order to achieve particularly high data efficiency, the researchers' new Deep Learning model combines AI with known laws of physics. This allows certain aspects of the potential energy surface to be precisely described with simple physical formulas. Consequently, the new method learns only those parts of the potential energy surface for which no simple mathematical description is available, saving computing power. "This is extremely practical. AI only needs to learn what we ourselves do not yet know from physics," explains Müller.

Spatial separation of cause and effect

Another special feature is that the algorithm can also describe nonlocal interactions. "Nonlocality" in this context means that a change to one atom, at a particular geometric position of the molecule, can affect atoms at a spatially separated geometric molecular position. Due to the spatial separation of cause and effect – something Albert Einstein referred to as "spooky action at a distance" – such properties of quantum systems are particularly hard for AI to learn. The researchers solved this issue using a transformer, a method originally developed for machine processing of language and texts or images. "The meaning of a word or sentence in a text frequently depends on the context. Relevant context-information may be located in a completely different section of the text. In a sense, language is also nonlocal," explains Müller. With the help of such a transformer, the scientists can also differentiate between different electronic states of a molecule such as spin and charge. "This is relevant, for example, for physical processes in solar cells, in which a molecule absorbs light and is thereby placed in a different electronic state," explains Oliver Unke.

Facts, background information, dossiers

  • artificial intelligence
  • material science
  • chemical reactions
  • molecules
  • Machine Learning
  • deep learning

More about TU Berlin

  • News

    Print tumors in the laboratory

    Using a bioink made from alginate and human cells, researchers at the Technical University (TU) Berlin and other institutions have printed a three-dimensional model of a cancer metastasis in healthy tissue. They used a commercially available bio-printer for this purpose, so that the tumor m ... more

    The 7th BioProScale Symposium took place again as in-person event in Berlin

    On the subject of “Scaling Up and Down of Bioprocesses: Technological Innovation and Cell Physiology Insights”, the 7th Symposium was again held on-site in Berlin from March 28 to 31, 2022. In parallel, there was also the possibility to follow the presentations online. The symposium was org ... more

    6th BioProScale Symposium 2021 – held virtually for the first time

    From the 29 to 31 March 2021 the 6th BioProScale Symposium was held virtually for the first time. It was organized by the Chair of Bioprocess Engineering at Technische Universität Berlin, the Institute for Fermentation and Biotechnology in Berlin (IfGB) as part of the Versuchs- und Lehranst ... more

  • q&more articles

    Water instead of mineral oil

    Optical active substances made from chiral catalysis form the basis of many drugs. Expensive precious-metal catalysts are required for the chemical production, which due to their thermal instability decompose at high temperatures and can therefore only be used once. The innovative process o ... more

  • Authors

    Dr.-Ing. Henriette Nowothnick

    born in 1980, studied Chemistry at the Berlin University of Technology. She received a doctorate in 2010 in the research group of Prof. R. Schomäcker on the control of the reaction process of the Suzuki coupling in micro emulsions with the aim of catalyst and product separation. From 2011 t ... more

    Dipl. Ing. Sonja Jost

    born in 1980, studied Industrial Engineering / Technical Chemistry at the Berlin University of Technology. From 2006 to 2011, she received various research fellowships in the field of homogeneous chiral catalysis. From 2011 to 2012, she was the project manager of a project receiving third-p ... more

q&more – the networking platform for quality excellence in lab and process

The q&more concept is to increase the visibility of recent research and innovative solutions, and support the exchange of knowledge. In the broad spectrum of subjects covered, the focus is on achieving maximum quality in highly innovative sectors. As a modern knowledge platform, q&more offers market participants one-of-a-kind networking opportunities. Cutting-edge research is presented by authors of international repute. Attractively presented in a high-quality context, and published in German and English, the original articles introduce new concepts and highlight unconventional solution strategies.

> more about q&more

q&more is supported by: