Publications associées à Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

A data-driven interpretation of the stability of organic molecular crystals

Michele Ceriotti, Edgar Albert Engel, Maria Pakhnova

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

ROYAL SOC CHEMISTRY2023

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

Effects of surface rigidity and metallicity on dielectric properties and ion interactions at aqueous hydrophobic interfaces

Philip Robin Loche

Using classical molecular dynamics simulations, we investigate the dielectric properties at interfaces of water with graphene, graphite, hexane, and water vapor. For graphite, we compare metallic and nonmetallic versions. At the vapor-liquid water and hexa ...

AIP Publishing2022

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

Modeling non-covalent interactions in condensed phase

Veronika Juraskova

The modeling of non-covalent interactions, solvation effects, and chemical reactions in complex molecular environment is a challenging task. Current state-of-the-art approaches often rely on static computations using implicit solvent models and harmonic ap ...

EPFL2022

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

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

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

Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization

Matteo Dal Peraro, Luciano Andres Abriata

Continuous assessment of transferable forcefields for molecular simulations is essential to identify their weaknesses and direct improvement efforts. The latest efforts focused on better describing disordered proteins while retaining proper description of ...

2021

Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

Rohit Karnik, Luc Sébastien Bondaz

Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...

AIP Publishing2021

Revisiting Cation Complexation and Hydrogen Bonding of Single-Chain Polyguluronate Alginate

Modifying the properties of bio-based materials has garnered increasing interest in recent years. In related applications, the ability of alginates to complex with metal ions has been shown to be effective in liquid-to-gel transitions, useful in the develo ...

American Chemical Society2021

Structural and analytical identifications of biomolecules by cold ion spectroscopy of non-covalent complexes

Erik Saparbayev

Non-covalent interactions are ubiquitous in nature. The diversity and functionality of these interactions are employed by nature as a universal tool for building the molecular mechanism of life. Although weak, these interactions play a key role in both for ...

EPFL2021

Thermomechanical properties of honeycomb lattices from internal-coordinates potentials: the case of graphene and hexagonal boron nitride

Nicola Marzari, Francesco Libbi

Lattice dynamics in low-dimensional materials and, in particular, the quadratic behaviour of the flexural acoustic modes play a fundamental role in their thermomechanical properties. A first-principles evaluation of these can be very demanding, and can be ...

IOP PUBLISHING LTD2021

Modelling long-range interactions in multiscale simulations of ferromagnetic materials

Doghonay Arjmand

Atomistic-continuum multiscale modelling is becoming an increasingly popular tool for simulating the behaviour of materials due to its computational efficiency and reliable accuracy. In the case of ferromagnetic materials, the atomistic approach handles th ...

SPRINGER2020

Salt enhances the thermostability of enteroviruses by stabilizing capsid protein interfaces

Tamar Kohn, Matteo Dal Peraro, Simon Meister, Alessio Prunotto

Enteroviruses are common agents of infectious disease that are spread by the fecal-oral route. They are readily inactivated by mild heat, which causes the viral capsid to disintegrate or undergo conformational change. While beneficial for the thermal treat ...

American Society for Microbiology2020

Geometric landscapes for material discovery within energy–structure–function maps

Seyedmohamad Moosavi, Berend Smit, Henglu Xu

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal–organic frameworks (MOFs). To aid disco ...

2020

Machine learning for metallurgy II. A neural-network potential for magnesium

William Curtin, Binglun Yin, Eleanor Yi Kei Mak

Interatomic potentials are essential for studying fundamental mechanisms of deformation and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are far above the scales accessible to first-principles studies. Existing pot ...

2020

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Anne-Clémence Corminboeuf, Michele Ceriotti, Veronika Juraskova, Kevin Rossi

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

AMER CHEMICAL SOC2020