Atomic theory

Natural sciences
Physics
Quantum mechanics
Topics in quantum mechanics

Related publications (29)

About
Privacy
Disclaimer

Graph Chatbot

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael John Willatt

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

Atomic scale symmetry and polar nanoclusters in the paraelectric phase of ferroelectric materials

Dragan Damjanovic, Emad Oveisi, Sina Hashemi Zadeh, Takuya Hoshina

The nature of the "forbidden" local- and long-range polar order in nominally nonpolar paraelectric phases of ferroelectric materials has been an open question since the discovery of ferroelectricity in oxide perovskites (ABO3). A currently considered model ...

Nature Research2021

Investigation of the fine structure of antihydrogen

Shirit Cohen

At the historic Shelter Island Conference on the Foundations of Quantum Mechanics in 1947, Willis Lamb reported an unexpected feature in the fine structure of atomic hydrogen: a separation of the 2S(1/2) and 2P(1/2) states(1). The observation of this separ ...

2020

Oxidation States, Thouless' Pumps, and Nontrivial Ionic Transport in Nonstoichiometric Electrolytes

Federico Grasselli

Thouless' quantization of adiabatic particle transport permits one to associate an integer topological charge with each atom of an electronically gapped material. If these charges are additive and independent of atomic positions, they provide a rigorous de ...

American Physical Society2020

Atom-density representations for machine learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

2019

Analysis of deposited layers with deuterium and impurity elements on samples from the divertor of JET with ITER-like wall

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Yves Martin, Jonathan Graves, Duccio Testa, Cristian Sommariva, Patrick Blanchard, Bruno Emanuel Ferreira De Sousa Correia

Inconel-600 blocks and stainless steel covers for quartz microbalance crystals from remote corners in the JET-ILW divertor were studied with time-of-flight elastic recoil detection analysis and nuclear reaction analysis to obtain information about the area ...

2019

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Atomistic-continuum coupling of random alloys

William Curtin, Till Junge, Shankha Nag

Random alloys are multicomponent systems where the atomic type on each lattice site is independent of the atom types on any other lattice site. The fluctuations in local atomic configurations inherent to the random alloy prevents the accurate application o ...

IOP PUBLISHING LTD2019

Incorporating long-range physics in atomic-scale machine learning

Michele Ceriotti, Andrea Grisafi

The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configu ...

2019

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to im ...

2018

Page 2 of 3

Analysis of deposited layers with deuterium and impurity elements on samples from the divertor of JET with ITER-like wall

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Yves Martin, Jonathan Graves, Duccio Testa, Cristian Sommariva, Patrick Blanchard, Bruno Emanuel Ferreira De Sousa Correia

2019

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

2018