Publications associées à In situ parameterisation of SCC-DFTB repulsive potentials by iterative Boltzmann inversion

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

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

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

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Anne-Clémence Corminboeuf, Veronika Juraskova, Rubén Laplaza Solanas, Frédéric Célerse

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

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...

EPFL2022

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Transferable machine-learning models of complex materials: the case of GaAs

Giulio Imbalzano

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

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

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020

i-PI 2.0: A universal force engine for advanced molecular simulations

Anne-Clémence Corminboeuf, Riccardo Petraglia, Sébastien Pierre Bienvenue, Michele Ceriotti, Bingqing Cheng, David Mark Wilkins, Przemyslaw Juda, Benjamin Aaron Helfrecht, Venkat Kapil, Mariana Rossi Carvalho, Robert Horst Meissner, Alice Cuzzocrea, Gareth Aneurin Tribello, Wei Fang

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explici ...

ELSEVIER SCIENCE BV2019

Gas-Phase Ozone Reactions with a Structurally Diverse Set of Molecules: Barrier Heights and Reaction Energies Evaluated by Coupled Cluster and Density Functional Theory Calculations

Jeremy Samuel Arey, Peter Rudolf Tentscher, Daniela Trogolo

Reactions with ozone transform organic and inorganic molecules in water treatment systems as well as in atmospheric chemistry, either in the aqueous phase, at gas/particle interfaces, or in the gas phase. Computed thermokinetic data can be used to estimate ...

AMER CHEMICAL SOC2019

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

Michele Ceriotti, Mariana Rossi Carvalho, Andrea Grisafi

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially ...

2019

On machine learning force fields for metallic nanoparticles

Kevin Rossi

Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational speed of machin ...

2019

Improving Performance and Accuracy of Hybrid-Functional Based Molecular Dynamics in Plane Waves

Martin Peter Bircher

The simulation of condensed matter in first principles Molecular Dynamics (FPMD) heavily relies on Kohn-Sham Density Functional Theory (KS-DFT) calculations. The accuracy of such simulations is governed by the reliability of the underlying potential energy ...

EPFL2018

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Markus Schneider

Metal cations often play an important role in shaping the three-dimensional structure of peptides. As an example, the model system AcPheAla5LysH+ is investigated in order to fully understand the forces that stabilize its helical structure. In particular, t ...

EPFL2018

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

Michele Ceriotti, Giulio Imbalzano

The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structur ...

2018

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

Nicola Marzari, William Curtin, Daniele Dragoni, Francesco Maresca

The plastic flow behavior of bcc transition metals up to moderate temperatures is dominated by the thermally activated glide of screw dislocations, which in turn is determined by the atomic-scale screw dislocation core structure and the associated kink-pai ...

2018

Performance of van der Waals Corrected Functionals for Guest Adsorption in the M-2(dobdc) Metal-Organic Frameworks

Berend Smit, Johanna Maria Huck

Small-molecule binding in metal-organic frameworks (MOFs) can be accurately studied both experimentally and computationally, provided the proper tools are employed. Herein, we compare and contrast properties associated with guest binding by means of densit ...

American Chemical Society2017

Mapping the conformational free energy of aspartic acid in the gas phase and in aqueous solution

Michele Ceriotti, Kevin Rossi

The conformational free energy landscape of aspartic acid, a proteogenic amino acid involved in a wide variety of biological functions, was investigated as an example of the complexity that multiple rotatable bonds produce even in relatively simple molecul ...

American Institute of Physics2017

Open-ended response theory with polarizable embedding: multiphoton absorption in biomolecular systems

We present the theory and implementation of an open-ended framework for electric response properties at the level of Hartree-Fock and Kohn-Sham density functional theory that includes effects from the molecular environment modeled by the polarizable embedd ...

Royal Soc Chemistry2016