Molecular modeling is taking the computerized testing of the behavior of materials one step further. With the help of quantum physics, scientists can now virtually observe and predict on the scale of individual atoms, blurring the lines between physics and chemistry.
 
Molecular modeling is the new frontier for the design and discovery of new materials, enabling the process to become considerably quicker and cheaper. As such, it’s an integral part of Solvay’s digital strategy, along with the digitization of laboratories and the development of data analytics.
 

Complex yet virtual experiments

Nicola-Marzari

 
The objective of molecular modeling is to better understand and predict, through calculations at the atomic scale, the properties and performance of novel or complex materials and devices. Though the term encompasses a wide range of digital techniques, molecular modeling focuses overall on understanding the interactions between atoms or groups of atoms.
 
To do this, it creates quantum-mechanical simulations whose predictive power can suggest, accelerate and even substitute actual physical experiments. “There’s a lot of mystique around quantum mechanics, but it’s actually very simple”, says Nicola Marzari (pictured above), director of the Swiss National Centre of Competence in Research MARVEL (an acronym of Materials’ Revolution), a new center for the computational design and discovery of novel materials in Lausanne. “We can use these models to predict electrical performance in a transistor or the hardness of a piece of diamond, for example, exploring possibilities that have never been tried in a laboratory.”

Quantu-mechanics-Quote-Nicola-Marzari

 
Advancing fundamental research

This is a far-reaching paradigm shift, replacing the cost and time required by working with brick-and-mortar facilities with those of computing engines. Because of this, quantum-mechanical simulations have become massively used tools for scientific discovery and technological advancement. Jean-Yves Delannoy, Modeling Domain leader at Solvay, collaborates with various universities specializing in molecular modeling and especially with EPFL, the prestigious Swiss Federal Institute of Technology.
 
The aim is to broaden the range of skills and competences in modeling within the group to allow the understanding of materials of varying complexity and meet the materials and chemical needs of tomorrow. “With EPFL, we are fortunate enough to communicate with various experts who can respond to Solvay's fundamental projects such as catalysis or solid state electrolytes”, says Delannoy. “Two perfect examples where observing the atomic level is key to understand the performance of the materials.” Molecular modeling is set to become increasingly important in Solvay’s research projects, allowing the group to maintain its leadership position in chemical and material innovation.

(Partially) understanding quantum physics

Superionic-Li

It all started in the 1920s, when scientists such as Erwin Schrödinger and Werner Heisenberg – both attendees of the famous Solvay Conferences –developed simple equations to describe the wavelike behavior of small particles like electrons and reveal how they come together to form molecules. There’s only one catch: these simple equations are very difficult to solve.
 
Here’s why: describing a wave in the sea simply requires knowing its height at every point; describing electron waves entails knowing the equivalent of this height, usually called amplitude, at every point for every electron combination in the system. If a single electron can be say in 1,000 different positions inside a small box, we need to know its amplitude at every point, i.e. 1000 numbers. But if there are two electrons, we need to know the 1000 amplitudes of one electron for every one of the 1000 positions of the other. That is, one million numbers. “When you start looking at 26 electrons, which is the number of electrons in an atom of iron, this requires knowing 1078 amplitudes - a number so large it’s comparable to the number of atoms in the universe,” explains Nicola Marzari.
 
One big breakthrough in solving these equations came when machines replaced the people employed to do the complex calculations. Then Walter Kohn, who later won the Nobel Prize, made quantum mechanics easier to compute thanks to his density-functional theory (DFT), which showed that it is sufficient to use the density of the electrons, rather than their amplitudes, to solve the equations of quantum mechanics. Now, the density of electrons can be represented with a number, and it doesn’t matter if you have 1, 26, or a quadrillion electrons. Interestingly, DFT equations involve an unknown, but approximations to this unknown have gotten better and better over the years, and are now accurate enough to allow researchers to predict how atoms come together in molecules, and what the resulting properties are. They can predict the power of the battery in your phone, the color of a shiny metal in your watch, or the strength of a new alloy in your car.