*25/03/2020*

## Where does this passion for all things nano come from?

I've always been attracted to lots of different things at the same time. In fact, that's what I liked about the École Polytechnique—the opportunity to learn as much about maths as economics, physics or biology. It's also what draws me to nanosciences, which have held my attention since 2005 and where you get to work with biologists, physicians and chemists, along with specialists in pharmaceutics and materials.

## You insist on the fact that your software for the virtual prototyping of nanoscopic objects will be generic and adapted to all of these disciplines. How is that possible?

I know that it could come across as something of a paradox. Contrary to software for the prototyping of aircraft, cars or any other manufactured products, we have much less room for manoeuvre when it comes to choosing a design on a nano scale. The physical constraints are much more powerful. For example, the distances between atoms cannot be arbitrary and molecular conformations are governed by complex laws. In spite of this, dynamic molecular simulation can be used to represent proteins, polymers, and materials in a unified manner, like a set of interacting particles.

## How did you manage to simplify this painstaking approach?

By using a so-called adaptive system, which helps concentrate calculations on only one part of the system—the most important part, i.e. where atoms move the most. In 2008, we started to develop our own software, known as SAMSON, as part of an ANR (French national research agency) project. SAMSON is used for virtual experiments. For example, it allows the user to modify the geometry of a molecule and visualise its new stable configuration in real time.

## What do you intend to do with your ERC grant?

In early 2011, we introduced the "adaptive Hamiltonian" theory, which is used to formalise adaptive particle simulations in a rigorous manner. First and foremost, this theory proves that, although these simulations are adaptive, they can be used to predict properties that concern biologists, physicians, and the like. The ERC grant will help us develop the theory and design a whole set of related simulation algorithms. For example, we will need to identify which simplification parameters to select in order to speed up a maximum number of calculations and check which size of molecules our method works on. We will integrate all the algorithms thus developed into SAMSON. My aim is to make SAMSON an open development environment for nanosystem design around which a community of users and developers may arise. My grant will provide me with the resources needed. With €1.5 million, I intend to build up my team and, over five years, recruit three doctoral students, two postdoctoral students, and an engineer. A serene working environment.

### Proof by Hamiltonian, an idea rich in its simplicity

Hamiltonian is a mathematical operator that can be used to describe a system of particles. It is the sum of the kinetic energy (energy linked to particle movement) and the potential energy (energy linked to interaction between particles). Stéphane Redon's stroke of genius was to modify the Hamiltonian—to turn it into an adaptive Hamiltonian—so that the mass of each particle is dependent on its kinetic energy. In its simplest version, the slower a particle moves, the greater mass it will be assigned, until infinity, thus blocking the movement of the particle in question. This is an ingenious and rigorous way of determining where and how calculations need to be concentrated.