Speaker: Emmanuel Fromager, lab. de Chimie Quantique (Unistra)

I will introduce in this presentation an in-principle-exact Born-Huang-based density functional theory of electrons and nuclei [1]. In this approach, the nuclear and (geometry-dependent) electronic densities are used as basic variables. The concept of Kohn-Sham molecule, where electrons interact with the nuclei but not among themselves, and from which both true physical densities can be recovered, in principle exactly, will be introduced within the present formalism.
An exact adiabatic connection formula will be derived and discussed for the Hartree-exchange correlation energy of the electrons within the molecule and, on that basis, a practical adiabatic density-functional approximation will be proposed.
[1] E. Fromager and B. Lasorne, arXiv:2312.15080 (2023)

Contact : Arnaud Gloppe (Arnaud.Gloppe@ipcms.unistra.fr) – Guillaume SCHULL (schull@unistra.fr)

Speaker : Dr. Thijs Stuyver (Chimie ParisTech, PSL)

Abstract :

Machine learning (ML) has had a significant impact on various subfields of science in recent years. Part of the reason for the success of ML is that it enables the generation of predictive models with only limited domain knowledge: usually, only minor modifications to a generic ML algorithm needs to be made to generate effective models for a specific application. That is of course under the condition that sufficient data is available, and then we usually mean hundreds of thousands or even millions of datapoints. In chemistry, there are several predictive tasks for which we have this abundancy of data, but for most specialized applications – particularly those related to chemical reactivity – we do not have this luxury.

              In principle, computational chemistry offers a way out when limited experimental data is available, since it enables data generation in a cheap and easy-to-automate manner. In the first part of my talk, I will explore this approach in a bit more detail and focus specifically on an ML accelerated computational workflow to screen for promising bioorthogonal click reactions that I recently developed.

              While quantum chemical simulations of reactivity tend to be relatively cheap compared to experimental characterizations, the cost of generating sufficient training data for a machine learning model still becomes prohibitive, fast. As such, in the second part of my talk I will discuss strategies to improve the data efficiency of ML-based computational workflows for reactivity prediction. Specifically, I will focus on models based on intermediate valence bond inspired representations, and demonstrate that these outperform conventional machine learning models by a wide margin for hydrogen atom transfer reactions in the low data regime.

References

• Casetti, N.; Alfonso-Ramos, J. E.; Coley, C. W.*; Stuyver, T.*, Combining Molecular Modeling and Machine learning for accelerated reaction screening and discovery, Chem. Eur. J. 2023, e202301957.
• Stuyver, T.*; Jorner, K.; Coley, C. W.*, Reaction profiles for quantum chemistry-computed [3+2] cycloaddition reactions, Sci. Data 2023, 10, 66.
• Stuyver, T.*; Coley, C. W.*, Machine learning‐guided computational screening of new candidate reactions with high bioorthogonal click potential, Chem. Eur. J. 2023, e202300387.
• Alfonso-Ramos, J.; Neeser, R.; Stuyver, T. Repurposing Quantum Chemical Descriptor Datasets for on-the-Fly Generation of Informative Reaction Representations: Application to Hydrogen Atom Transfer Reactions, ChemRxiv 2023, 10.26434/chemrxiv-2023-2n281.

Speaker : Vikram Deshpande, University of Utah, USA

Abstract : Topological materials have burgeoned of late due to their implications for the fields of electronics, spintronics and quantum computing, among others. While their electronic properties are important in their own right, they can also couple in fascinating ways to the lattice. We have developed techniques to deform materials controllably and study their resulting electronic properties in-situ, while complementarily sensing the electronic ground state through the mechanical degree of freedom. In this talk, by way of introduction, I will first present purely electrical measurements on the prototypical topological material, the three-dimensional (3D) topological insulator (TI), wherein we hybridize Dirac cones of 3D TI surfaces controllably to realize the quantum spin Hall effect in the ultrathin limit. Then I will present our recent results applying the above-mentioned mechanical techniques to two different topological materials, namely twisted bilayer graphene (TBG) and the intrinsic magnetic topological insulator (MTI) MnBi2Te4, respectively. We are able to tune the Hofstadter’s spectrum of non-magic angle TBG and induce magnetism in non-magnetic correlated insulating states of magic-angle TBG using isotropic strain, for example, and detect various magnetic states and measure magnetoelastic couplings in the case of MTIs. Our advances present unique routes to tuning and sensing the parameter space of these exciting materials.

Contact : Stéphane BERCIAUD (berciaud@unistra.fr)

Speaker : Benjamin Besga, ILM Lyon

Abstract : The aim of stochastic thermodynamics is to study small non-equilibrium systems subject to thermal fluctuations. Some results from this field will be illustrated using experiments carried out on opto-mechanical systems, mainly colloidal particles in an optical trap. Using non-equilibrium statistical physics will see how we can accelerate the natural dynamics of a system, shorten the mean first passage time on a target, or measure the forces acting on a non-equilibrium probe. Finally, we’ll ask how we can interrogate the quantum limit of these results by looking at the opto-mechanical coupling of a self assembled supercrystal of quantum dots in an optical trap.