Periodic Reporting for period 1 - BITMAP (aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials)
Période du rapport: 2021-02-01 au 2023-01-31
Artificial intelligence and quantum materials are now revolutionising the way we relate to the world. The first, in its machine learning and deep learning facets, is now present in every aspect of our lives, from smartphones to automation in our homes. Quantum materials, obeying the bizarre rules of quantum mechanics, promise instead to play a fundamental role in the field of new technologies and sustainable energies.
However, it is not easy to imagine a meeting point between these two revolutionary spheres. Nevertheless, more and more often in recent years, artificial intelligence and the quantum description of the world around us have intersected, thus giving rise to new methods for studying the microscopic behaviour of matter.
Phenomena and properties such as magnetism and superconductivity owe their origin to the way elementary particles, electrons, interact with each other. The description of these interactions, and the phenomenologies that emerge from them, have occupied the minds of the best physicists for more than a century. Many theoretical models have thus been developed, but none of them has so far been able to give a definite answer to the “many electron problem”.
The principal objectives of the Marie Curie Action BITMAP “aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials” are those of finding new intersections between the realms of quantum materials and machine learning. Both realms articulate themselves into multifaceted declinations, and BITMAP aims at unveiling promising overlaps by studying novel quantum materials and their long-range electronic order by means of artificial intelligence-based methods.
However, it is not easy to imagine a meeting point between these two revolutionary spheres. Nevertheless, more and more often in recent years, artificial intelligence and the quantum description of the world around us have intersected, thus giving rise to new methods for studying the microscopic behaviour of matter.
Phenomena and properties such as magnetism and superconductivity owe their origin to the way elementary particles, electrons, interact with each other. The description of these interactions, and the phenomenologies that emerge from them, have occupied the minds of the best physicists for more than a century. Many theoretical models have thus been developed, but none of them has so far been able to give a definite answer to the “many electron problem”.
The principal objectives of the Marie Curie Action BITMAP “aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials” are those of finding new intersections between the realms of quantum materials and machine learning. Both realms articulate themselves into multifaceted declinations, and BITMAP aims at unveiling promising overlaps by studying novel quantum materials and their long-range electronic order by means of artificial intelligence-based methods.
The BITMAP project is divided into 3 work packages (WPs).
WP1 focuses on the theoretical modelling of the electronic properties of novel quantum materials. This modelling is primarily based on the density functional theory, that provides a solid and acclaimed framework to predict and unveil the quantum behaviour of electrons in molecules and solids. During the first two years of the outgoing phase at the Center for Computational Quantum Physics in NewYork, several classes of quantum materials have been investigated, namely Weyl semimetals, two-dimensional hexagonal and triangular topological insulators and Kagome metals, resulting in 9 scientific publications.
WP2 seeks novel intersections between the problem of interacting electrons, ubiquitous in quantum materials, and the tools of artificial intelligence. In this framework, a cutting edge method in many-body physics, known as the Functional Renormalisation Group, has been augmented by deep neural networks. This research has led, so far, to a pivotal scientific publication and several new ideas that will likely end up being published.
WP3 concentrates instead on the electronic long range orders of quantum materials, ranging from superconductivity to magnetism. Starting from the electronic properties investigated in WP1, WP3 used so-called vertex based methods, such as the weak coupling Renormalisation Group and the Random Phase Approximation, to explore phase diagrams of collective phenomena. 4 scientific publications have emerged, so far, as a result of this research line.
WP1 focuses on the theoretical modelling of the electronic properties of novel quantum materials. This modelling is primarily based on the density functional theory, that provides a solid and acclaimed framework to predict and unveil the quantum behaviour of electrons in molecules and solids. During the first two years of the outgoing phase at the Center for Computational Quantum Physics in NewYork, several classes of quantum materials have been investigated, namely Weyl semimetals, two-dimensional hexagonal and triangular topological insulators and Kagome metals, resulting in 9 scientific publications.
WP2 seeks novel intersections between the problem of interacting electrons, ubiquitous in quantum materials, and the tools of artificial intelligence. In this framework, a cutting edge method in many-body physics, known as the Functional Renormalisation Group, has been augmented by deep neural networks. This research has led, so far, to a pivotal scientific publication and several new ideas that will likely end up being published.
WP3 concentrates instead on the electronic long range orders of quantum materials, ranging from superconductivity to magnetism. Starting from the electronic properties investigated in WP1, WP3 used so-called vertex based methods, such as the weak coupling Renormalisation Group and the Random Phase Approximation, to explore phase diagrams of collective phenomena. 4 scientific publications have emerged, so far, as a result of this research line.
This Marie Curie Action has pushed the frontiers of quantum materials forward in a numerous ways. For instance, BITMAP has contributed to the theoretical discovery, and consequent realisation, of a novel triangular quantum spin Hall insulator, that we dubbed “Indenene” as it consists of a single layer of the chemical element Indium. Quantum spin Hall insulators are currently seen as one of the holy grails for future electronics, because they can feature electrons’ transport without dissipation, with a great socio-economic impacts towards a more sustainable world and green energy.
BITMAP has also introduced a new way of using machine learning tools, such as deep neural networks and neural ordinary differential equations, for the many-body description of interacting electrons. This achievement has the potential to enlarge the state of art protocol currently used by scientists to tackle the physics of complex quantum materials.
Overall, BITMAP has explored several different cutting edge avenues in contemporary solid state physics, bringing modern theoretical tools togethers and seeking intersections with artificial intelligence, a field that nowadays is revolutionising every aspect of our life.
BITMAP has also introduced a new way of using machine learning tools, such as deep neural networks and neural ordinary differential equations, for the many-body description of interacting electrons. This achievement has the potential to enlarge the state of art protocol currently used by scientists to tackle the physics of complex quantum materials.
Overall, BITMAP has explored several different cutting edge avenues in contemporary solid state physics, bringing modern theoretical tools togethers and seeking intersections with artificial intelligence, a field that nowadays is revolutionising every aspect of our life.