The building blocks of most observable matters are electrons and nuclei. Following the laws of quantum mechanics, their behavior can be described in terms of their wave function, sort of a diffuse cloud that is related to the probability of observing them in a given point and time. By solving the Schrodinger equation, it is possible to make models and predictions of any material, including water. But there is a catch. As the number of electrons and nuclei increases, the complexity involved soon become intractable even with the fastest supercomputers, and even after a century of celebrated progress in optimizing such calculations. In fact, quantum mechanical calculations are still unaffordable for systems with more than a few hundred atoms, or for a time period longer than a nanosecond, which is 1/1,000,000,000th of a second.
To overcome these harsh limitations, the researchers exploited an artificial neural network (ANN) to learn the atomic interactions from quantum mechanics. The architecture of ANNs can be represented as several layers of interconnected nodes, which mimics the structure of the neurons in a human brain.
For ubiquitous and technologically essential system of water, a first-principles thermodynamic description not only leads to excellent agreement with experiments, but also reveals the crucial role of nuclear quantum fluctuations in modulating the thermodynamic stabilities of different phases of water.
Credit: Michele Ceriotti
The ANN first learns quantum mechanical interactions between atoms, and then make speedy predictions about the energy and forces for a system of atoms, bypassing the need to perform expensive quantum mechanical calculations.
So far, it all rather sounds like a typical success story of machine learning. However, there are subtleties. The ANN has a residual error compared to the actual quantum mechanical calculations: most of the times it introduce a small noise, and sometime it makes a wild guess if an input is very different from anything it has learned. How to avoid the pitfalls of the ANN?
Instead of employing ANN on its own to make predictions about a system of atoms, the researchers used it as a surrogate model. In essence, computing properties of materials at a finite temperature usually involves many computation steps, the laborious and repetitive parts can be delegated to the cheap surrogate model.
Finally, the difference between the surrogate and the ground truth, which is the difference between the ANN and quantum mechanics, can be accounted for and subtracted from the final predictions.
With these techniques, the researchers were thus able to to reproduce several thermodynamic properties of water from quantum mechanics, including the density of ice and water, the difference in melting temperature for normal and heavy water, and the stability of different forms of ice.
Moreover, the study reveals several physical insights on what give ice and water systems their peculiar properties. One of the most notable findings is the that nuclear quantum fluctuations, which is the tendency for light elements such as hydrogen to behave more like a diffuse cloud rather than a localized particle, promote hexagonal packing of molecules inside ice, which ultimately leads to the six-fold symmetry of snowflakes.
Bingqing Cheng | EurekAlert!
Unraveling materials' Berry curvature and Chern numbers from real-time evolution of Bloch states
18.02.2019 | Max-Planck-Institut für Struktur und Dynamik der Materie
Gravitational waves will settle cosmic conundrum
15.02.2019 | Simons Foundation
For the first time, an international team of scientists based in Regensburg, Germany, has recorded the orbitals of single molecules in different charge states in a novel type of microscopy. The research findings are published under the title “Mapping orbital changes upon electron transfer with tunneling microscopy on insulators” in the prestigious journal “Nature”.
The building blocks of matter surrounding us are atoms and molecules. The properties of that matter, however, are often not set by these building blocks...
Scientists at the University of Konstanz identify fierce competition between the human immune system and bacterial pathogens
Cell biologists from the University of Konstanz shed light on a recent evolutionary process in the human immune system and publish their findings in the...
Laser physicists have taken snapshots of carbon molecules C₆₀ showing how they transform in intense infrared light
When carbon molecules C₆₀ are exposed to an intense infrared light, they change their ball-like structure to a more elongated version. This has now been...
The so-called Abelian sandpile model has been studied by scientists for more than 30 years to better understand a physical phenomenon called self-organized...
Physicists from the University of Basel have developed a new method to examine the elasticity and binding properties of DNA molecules on a surface at extremely low temperatures. With a combination of cryo-force spectroscopy and computer simulations, they were able to show that DNA molecules behave like a chain of small coil springs. The researchers reported their findings in Nature Communications.
DNA is not only a popular research topic because it contains the blueprint for life – it can also be used to produce tiny components for technical applications.
11.02.2019 | Event News
30.01.2019 | Event News
16.01.2019 | Event News
18.02.2019 | Interdisciplinary Research
18.02.2019 | Process Engineering
18.02.2019 | Studies and Analyses