Publications related to Atomic theory

Characterisation of detachment in the MAST-U Super-X divertor using multi-wavelength imaging of 2D atomic and molecular emission processes

Basil Duval, Artur Perek, Kevin Henricus Annemarie Verhaegh

In this work, we provide the first 2D spatially resolved description of radiative detachment in MAST-U Super-X L-mode divertor plasmas. The Super-X magnetic configuration was designed to achieve reduced heat- and particle loads at the divertor target compa ...

IOP Publishing Ltd2023

Unified theory of atom-centered representations and message-passing machine-learning schemes

Michele Ceriotti, Guillaume André Jean Fraux, Sergey Pozdnyakov, Jigyasa Nigam

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

AIP Publishing2022

Effect of Co-Substitution on Hydrogen Absorption and Desorption Reactions of YMgNi4-Based Alloys

Andreas Züttel, Loris Giovanni Lombardo, Wen Luo, Heena Yang

YMgNi4-based alloys exhibit reversible hydrogen absorption and desorption reactions at near room temperature. Here, we report that Co-substituted YMgNi4-based alloys exhibited higher hydrogen contents and lower hydrogen absorption and desorption reaction p ...

AMER CHEMICAL SOC2022

Structure analysis of beta dicalcium silicate via scanning transmission electron microscope (STEM)

Franco Alberto Zunino Sommariva, Qiao Wang, Junying Zhao, Chaoqi Shi

Beta-dicalcium silicate (beta-C2S), one of the main phases in Portland cement clinker, offers some promising overviews regarding CO2 and energy savings potential compared to alite. Understanding the crystalline structure and reactive sites on beta-C2S surf ...

ELSEVIER SCI LTD2022

The Atomic-Level Structure of Cementitious Calcium Aluminate Silicate Hydrate Determined by NMR

David Lyndon Emsley, Pinelopi Moutzouri

We review our recent paper which resolves the long-standing dilemma of the location and nature of the six-fold coordinated aluminum in calcium aluminate silicate hydrate (C-A-S-H) samples. First principles calculations predict that at high Ca:Si and H2O ra ...

2021

Multi-scale approach for the prediction of atomic scale properties

Michele Ceriotti, Jigyasa Nigam, Andrea Grisafi

Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning ...

2021

Metal-Stabilized Boronate Ester Cages

Rosario Scopelliti, Kay Severin, Farzaneh Fadaei Tirani, Erica Giraldi

Molecular cages with arylboronate ester caps at the vertices are described. The cages were obtained by metal-templated polycondensation reactions of a tris(2-formylpyridine oxime) ligand with arylboronic acids. Suited templates are triflate or triflimide s ...

2021

Investigating Tetrel-Based Neutral Frustrated Lewis Pairs for Hydrogen Activation

Shubhajit Das

Tetrel Lewis acids are a prospective alternative to commonly employed neutral boranes in frustrated Lewis pair (FLP) chemistry. While cationic tetrylium Lewis acids, being isolobal and iso(valence)electronic, are a natural replacement to boranes, neutral t ...

AMER CHEMICAL SOC2021

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Andrea Grisafi

Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...

EPFL2021

Incompleteness of Atomic Structure Representations

Michele Ceriotti, Sergey Pozdnyakov, Michael John Willatt

Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the ...

AMER PHYSICAL SOC2020

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, Albano António De Abreu E Presa De Castro E Sousa, Alessandro Pau, Philippe Janson, Thomas Willem Jan de Geus, Lorenzo Anton-Louis Talà, Pamela Solange Valdés Undurraga, Andrea Salmi, Javier García Hernández, Semin Kwak, Jose Felix Vega Stavro, Hugo Augusto Penedones Fernandes, Riccardo Vannucci, Yao Zhou, Henri Weisen, Richard Pitts, Davide Galassi, Sun Hee Kim, Yann Camenen, Jonathan Marc Philippe Faustin, Jan Horacek, Liang Yao, Raffaele Tito D'Agnolo, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Federico Nespoli, David Pfefferlé, Antonio José Pereira de Figueiredo, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

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