@article{12150, abstract = {Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of nonadditive systems very well. In particular, we recover exact diagonalization in all regimes tested and observe substantial improvement over the standard coherent state estimates in the most challenging intermediate-coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications.}, author = {Rzadkowski, Wojciech and Lemeshko, Mikhail and Mentink, Johan H.}, issn = {2469-9969}, journal = {Physical Review B}, number = {15}, publisher = {American Physical Society}, title = {{Artificial neural network states for nonadditive systems}}, doi = {10.1103/physrevb.106.155127}, volume = {106}, year = {2022}, } @phdthesis{10759, abstract = {In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data.}, author = {Rzadkowski, Wojciech}, issn = {2663-337X}, pages = {120}, publisher = {Institute of Science and Technology Austria}, title = {{Analytic and machine learning approaches to composite quantum impurities}}, doi = {10.15479/at:ista:10759}, year = {2022}, } @unpublished{10762, abstract = {Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications.}, author = {Rzadkowski, Wojciech and Lemeshko, Mikhail and Mentink, Johan H.}, booktitle = {arXiv}, pages = {2105.15193}, title = {{Artificial neural network states for non-additive systems}}, doi = {10.48550/arXiv.2105.15193}, year = {2021}, } @article{8644, abstract = {Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings. A general question is whether new phases, not present in the uncoupled limit, may arise. We use machine learning and a suitable quasidistance between different points of the phase diagram to study layered spin models, in which the spin variables constituting each of the uncoupled systems (to which we refer as layers) are coupled to each other via an interlayer coupling. In such systems, in general, composite order parameters involving spins of different layers may emerge as a consequence of the interlayer coupling. We focus on the layered Ising and Ashkin–Teller models as a paradigmatic case study, determining their phase diagram via the application of a machine learning algorithm to the Monte Carlo data. Remarkably our technique is able to correctly characterize all the system phases also in the case of hidden order parameters, i.e. order parameters whose expression in terms of the microscopic configurations would require additional preprocessing of the data fed to the algorithm. We correctly retrieve the three known phases of the Ashkin–Teller model with ferromagnetic couplings, including the phase described by a composite order parameter. For the bilayer and trilayer Ising models the phases we find are only the ferromagnetic and the paramagnetic ones. Within the approach we introduce, owing to the construction of convolutional neural networks, naturally suitable for layered image-like data with arbitrary number of layers, no preprocessing of the Monte Carlo data is needed, also with regard to its spatial structure. The physical meaning of our results is discussed and compared with analytical data, where available. Yet, the method can be used without any a priori knowledge of the phases one seeks to find and can be applied to other models and structures.}, author = {Rzadkowski, Wojciech and Defenu, N and Chiacchiera, S and Trombettoni, A and Bighin, Giacomo}, issn = {13672630}, journal = {New Journal of Physics}, number = {9}, publisher = {IOP Publishing}, title = {{Detecting composite orders in layered models via machine learning}}, doi = {10.1088/1367-2630/abae44}, volume = {22}, year = {2020}, } @article{7956, abstract = {When short-range attractions are combined with long-range repulsions in colloidal particle systems, complex microphases can emerge. Here, we study a system of isotropic particles, which can form lamellar structures or a disordered fluid phase when temperature is varied. We show that, at equilibrium, the lamellar structure crystallizes, while out of equilibrium, the system forms a variety of structures at different shear rates and temperatures above melting. The shear-induced ordering is analyzed by means of principal component analysis and artificial neural networks, which are applied to data of reduced dimensionality. Our results reveal the possibility of inducing ordering by shear, potentially providing a feasible route to the fabrication of ordered lamellar structures from isotropic particles.}, author = {Pȩkalski, J. and Rzadkowski, Wojciech and Panagiotopoulos, A. Z.}, issn = {10897690}, journal = {The Journal of chemical physics}, number = {20}, publisher = {AIP Publishing}, title = {{Shear-induced ordering in systems with competing interactions: A machine learning study}}, doi = {10.1063/5.0005194}, volume = {152}, year = {2020}, } @article{415, abstract = {Recently it was shown that a molecule rotating in a quantum solvent can be described in terms of the “angulon” quasiparticle [M. Lemeshko, Phys. Rev. Lett. 118, 095301 (2017)]. Here we extend the angulon theory to the case of molecules possessing an additional spin-1/2 degree of freedom and study the behavior of the system in the presence of a static magnetic field. We show that exchange of angular momentum between the molecule and the solvent can be altered by the field, even though the solvent itself is non-magnetic. In particular, we demonstrate a possibility to control resonant emission of phonons with a given angular momentum using a magnetic field.}, author = {Rzadkowski, Wojciech and Lemeshko, Mikhail}, journal = {The Journal of Chemical Physics}, number = {10}, publisher = {AIP Publishing}, title = {{Effect of a magnetic field on molecule–solvent angular momentum transfer}}, doi = {10.1063/1.5017591}, volume = {148}, year = {2018}, }