Publications associées à Many-body problem

Efficient and insightful descriptors for representing molecular and material space

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Absolute energy levels of liquid water from many-body perturbation theory with effective vertex corrections

Alfredo Pasquarello, Thomas Bischoff, Aleksei Tal

We demonstrate the importance of addressing the F vertex and thus going beyond the GW approximation for achieving the energy levels of liquid water in manybody perturbation theory. In particular, we consider an effective vertex function in both the polariz ...

National Academy of Sciences2024

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Friederike Metz

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...

NATURE PORTFOLIO2023

A recipe for cracking the quantum scaling limit with machine learned electron densities

Mario Geiger

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

IOP Publishing Ltd2023

Learning Physics-Consistent Particle Interactions

Olga Fink, David Simon Kammer, Zhichao Han

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hi ...

Oxford University Press2023

koopmans: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals

Nicola Marzari, Nicola Colonna, Edward Baxter Linscott, Giovanni Borghi, Ngoc Linh Nguyen, Riccardo De Gennaro, Andrea Ferretti

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity conditio ...

Amer Chemical Soc2023

Wigner Gaussian dynamics: Simulating the anharmonic and quantum ionic motion

Lorenzo Monacelli

The atomic motion controls important properties of materials, such as thermal transport, phase transitions, and vibrational spectra. However, simulating the ionic dynamics is exceptionally challenging when quantum fluctuations are relevant (e.g., at low te ...

AMER PHYSICAL SOC2023

Engineering random spin models with atoms in a high-finesse cavity

Jean-Philippe Brantut, Nick Jacob Sauerwein, Tigrane Arthur Cantat Moltrecht

Random spin models play a key role in our understanding of disorder and complex many-body systems. Two all-to-all interacting, disordered models have now been realized using a cavity quantum electrodynamics platform. ...

NATURE PORTFOLIO2023

Hidden-nucleons neural-network quantum states for the nuclear many-body problem

Giuseppe Carleo

We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...

AMER PHYSICAL SOC2022

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Jigyasa Nigam, Andrea Grisafi, Max David Veit

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022