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

Publications associées (55)

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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

Crystallography, thermodynamics and phase transitions in refractory binary alloys

Anirudh Raju Natarajan

We investigate phase stability in all binary alloys comprised of elements from groups 4 (Ti, Zr, Hf), 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. First-principles calculations of the energy landscapes along crystallographic pathways that connect ...

Elsevier2020

A class of 2-local finite spectra which admit a v1/2-self-map

Philip Egger

At the prime 2, Behrens, Hill, Hopkins and Mahowald showed that M-2 (1, 4) admits a 32-periodic v(2)-self-map. More recently, in joint work with Mahowald, we showed that A(1) also admits a 32-periodic v(2)-self-map. This leads to the question of whether th ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Optical element

Niels Quack, Gergely Huszka

The present invention concerns an optical grating comprising a body defining a first external surface and a second external surface. The first external surface defines a periodic structure comprising a plurality of elongated protrusions and a plurality of ...

2020

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

The Periodic Table and Kinetics?

André Merbach, Lothar Helm

Mendeleev in his first publication ordered the chemical elements following an apparent periodicity of properties such as atomic volume and valence. The reactivity of the elements was only studied systematically many years later. To illustrate the systemati ...

2019

Engineered acoustic mismatch for anchor loss control in contour mode resonators

Luis Guillermo Villanueva Torrijo, Annalisa De Pastina, Andrea Lozzi

Improving the quality factor (Q) of electromechanical resonators is of paramount importance for different applications, ranging from RF filtering to sensing. In this paper, we present a modified fabrication process flow for contour mode resonators to simul ...

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

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

2019

Crystalline and correlated phases in two-dimensional transition metal dichalcogenides

Diego José Pasquier

This thesis is dedicated to the study of various aspects of the electronic structure of two-dimensional transition metal dichalcogenides (TMDs) of chemical composition MX

_2

(where M is a transition metal atom and X= S, Se, Te), using a combination of \te ...

EPFL2019

Crystalline and correlated phases in two-dimensional transition metal dichalcogenides

Diego José Pasquier

This thesis is dedicated to the study of various aspects of the electronic structure of two-dimensional transition metal dichalcogenides (TMDs) of chemical composition MX

_2

(where M is a transition metal atom and X= S, Se, Te), using a combination of \te ...

EPFL2019