Wochenübersicht

Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and design of materials. However, this approach for the design of soft materials is highly non-trivial. The main difficulty stems from the importance of entropy, the ubiquity of multi-scale and many-body interactions, and the prevalence of non-equilibrium and active matter systems. The abundance of exotic soft-matter phases with (partial) orientation and positional order like liquid crystals, quasicrystals, plastic crystals, along with the omnipresent thermal noise, makes the classification of these states of matter using ML tools highly non-trivial. In this talk, I will address questions like: Can we use machine learning to autonomously identify local structures [1,2], detect phase transitions [1], classify phases and find the corresponding order parameters [2], can we identify the kinetic pathways for phase transformations [1], and can we use machine learning to coarse-grain our models [3,4,5]? Finally, I will show in this lecture how one can use machine learning to reverse-engineer the particle interactions to stabilize nature’s impossible phase of matter, namely quasicrystals [6]? [1] An artificial neural network reveals the nucleation mechanism of a binary colloidal AB13 crystal G.M. Coli and M. Dijkstra, ASC Nano 15, 4335-4346 (2021). [2] Classifying crystals of rounded tetrahedra and determining their order parameters using dimensionality reduction R. van Damme, G.M. Coli, R. van Roij, and M. Dijkstra, ACS Nano 14, 15144-15153 (2020). [3] Machine learning many-body potentials for colloidal systems G. Campos-Villalobos, E. Boattini, L. Filion and M. Dijkstra, The Journal of Chemical Physics 155 (17), 174902 (2021). [4] Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions G. Campos-Villalobos, G. Giunta, S. Marín-Aguilar and M. Dijkstra, The Journal of Chemical Physics 157 (2), 024902 (2022). [5] Coarse-Grained Many-Body Potentials of Ligand-Stabilized Nanoparticles from Machine-Learned Mean Forces G. Giunta, G. Campos-Villalobos, and M. Dijkstra, ACS Nano (2023). [6] Inverse design of soft materials via a deep learning–based evolutionary strategy G.M. Coli, E. Boattini, L. Filion, and M. Dijkstra, Science Advances 8 (3), eabj6731 (2022).

We are experimentally investigating possible departures from the standard quantum mechanics’ predictions at the Gran Sasso underground laboratory in Italy. In particular, with refined radiation detectors, we are searching signals predicted by the dynamical collapse models (spontaneous emission of radiation) which were proposed to solve the “measurement problem” in quantum physics, and signals indicating a possible violation of the Pauli Exclusion Principle. I shall discuss our recent results which ruled out the natural parameter-free version of the gravity-related collapse model. I shall then present more generic results on testing CSL (Continuous Spontaneous Localization) collapse models and discuss future perspectives. Finally, I shall present the VIP experiment, with which we search for possible violations of the Pauli Exclusion Principle manifested as “impossible” atomic transitions, and muse about the impact of this research in relation to Quantum Gravity models.