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Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

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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

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

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Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

2022

Nuclei with Up to A=6 Nucleons with Artificial Neural Network Wave Functions

Giuseppe Carleo

The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrodinger equation involves non-trivial difficulties, due to the non-per ...

SPRINGER WIEN2022

Many-Body Self-Interaction and Polarons

Alfredo Pasquarello, Stefano Falletta

We address the many-body self-interaction in relation to polarons in density functional theory. Our study provides (i) a unified theoretical framework encompassing many-body and one-body forms of self-interaction and (ii) an efficient semilocal scheme for ...

2022

Universal pair polaritons in a strongly interacting Fermi gas

Jean-Philippe Brantut, Victor Youri Helson, Kevin Etienne Robert Roux, Hideki Konishi

Cavity quantum electrodynamics (QED) manipulates the coupling of light with matter, and allows several emitters to couple coherently with one light mode1. However, even in a many-body system, the light–matter coupling mechanism has so far been restricted t ...

2021

A general and efficient framework for atomistic machine learning

Félix Benedito Clément Musil

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

Tris(hydroxymethyl)aminomethane (Tris) has been used as the buffer in bioactivity testing for over two decades and has become a standard choice for the scientific community. While it is believed to be non-interacting, the extent of its interactions with ti ...

Amsterdam2021

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Michele Ceriotti, Kevin Rossi, Veronika Juraskova

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

AMER CHEMICAL SOC2020