BITMAP · aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials

aB-IniTio calculations and MAchine learning for suPerconducting collective phenomena in novel materials

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 aimed at unveiling promising overlaps by studying novel quantum materials and their long-range electronic order by means of artificial intelligence-based methods. In conclusion of the action, BITMAP managed to find an interesting meeting point between the physics of interacting electrons and artificial intelligence. This meeting point consisted in the application of data science and signal processing methods to the many body problem, a formidable task that cannot be tackled with traditional tools.

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