Publications related to A smooth basis for atomistic machine learning

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

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

Plane Waves Versus Correlation-Consistent Basis Sets: A Comparison of MP2 Non-Covalent Interaction Energies in the Complete Basis Set Limit

Ursula Röthlisberger, Justin Villard, Martin Peter Bircher

Second-order Moller-Plesset perturbation theory (MP2) is the most expedient wave function-based method for considering electron correlation in quantum chemical calculations and, as such, provides a cost-effective framework to assess the effects of basis se ...

Washington2023

Adaptive isogeometric methods with C-1 (truncated) hierarchical splines on planar multi-patch domains

Rafael Vazquez Hernandez

Isogeometric analysis is a powerful paradigm which exploits the high smoothness of splines for the numerical solution of high order partial differential equations. However, the tensor-product structure of standard multivariate B-spline models is not well s ...

WORLD SCIENTIFIC PUBL CO PTE 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

A Characterisation Of The Continuum Gaussian Free Field In Arbitrary Dimensions

Juhan Aru

We prove that under certain mild moment and continuity assumptions, the d-dimensional continuum Gaussian free field is the only stochastic process satisfying the usual domain Markov property and a scaling assumption. Our proof is based on a decomposition o ...

ECOLE POLYTECHNIQUE2022

Optimal radial basis for density-based atomic representations

Michele Ceriotti, Sergey Pozdnyakov, Jigyasa Nigam, Félix Benedito Clément Musil, Alexander Jan Goscinski

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations ...

AIP Publishing2021

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

Reduction of the sign problem near T=0 in quantum Monte Carlo simulations

Frédéric Mila, Jonathan D'Emidio

Building on a recent investigation of the Shastry-Sutherland model [S. Wessel et al., Phys. Rev. B 98, 174432 (2018)], we develop a general strategy to eliminate the Monte Carlo sign problem near the zero-temperature limit in frustrated quantum spin models ...

AMER PHYSICAL SOC2020

New Results in Integer and Lattice Programming

Christoph Hunkenschröder

An integer program (IP) is a problem of the form

\min \{f(x) : \, Ax = b, \ l \leq x \leq u, \ x \in \Z^n\}

, where

A \in \Z^{m \times n}

,

b \in \Z^m

,

l,u \in \Z^n

, and

f: \Z^n \rightarrow \Z

is a separable convex objective function. The problem o ...

EPFL2020

A smooth basis for atomistic machine learning

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Efficient and insightful descriptors for representing molecular and material space

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Plane Waves Versus Correlation-Consistent Basis Sets: A Comparison of MP2 Non-Covalent Interaction Energies in the Complete Basis Set Limit

Adaptive isogeometric methods with C-1 (truncated) hierarchical splines on planar multi-patch domains

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

A Characterisation Of The Continuum Gaussian Free Field In Arbitrary Dimensions

Optimal radial basis for density-based atomic representations

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

Reduction of the sign problem near T=0 in quantum Monte Carlo simulations

New Results in Integer and Lattice Programming

Adaptive isogeometric methods with C-1 (truncated) hierarchical splines on planar multi-patch domains

Efficient and insightful descriptors for representing molecular and material space

New Results in Integer and Lattice Programming

Plane Waves Versus Correlation-Consistent Basis Sets: A Comparison of MP2 Non-Covalent Interaction Energies in the Complete Basis Set Limit

Optimal radial basis for density-based atomic representations

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

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

A Characterisation Of The Continuum Gaussian Free Field In Arbitrary Dimensions

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Reduction of the sign problem near T=0 in quantum Monte Carlo simulations