Concept

Force field (chemistry)

Related publications (31)

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

Thermal transport of glasses via machine learning driven simulations

Federico Grasselli, Paolo Pegolo

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Paolo Pegolo

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach

Ursula Röthlisberger, Sophia Kathryn Johnson

Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to ...

Swiss Chemical Soc2024

Computational Studies on the Stability of Organic-Inorganic Hybrid Halide Lead Perovskites

Mathias Dankl

Perovskite-based solar cells are currently the most rapidly advancing photovoltaic technology but concerns about their long-term stability are still impeding full-scale commercialization. This thesis provides computational insights into some of the stabili ...

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

Roughness Evolution Induced by Third-Body Wear

Jean-François Molinari, Tobias Brink, Antonio Joaquin Garcia Suarez

Surface roughness is a key factor when it comes to friction and wear, as well as to other physical properties. These phenomena are controlled by mechanisms acting at small scales, in which the topography of apparently flat surfaces is revealed. Roughness i ...

2024

Machine-learned interatomic potentials: Recent developments and prospective applications

William Curtin

High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...

Springer Heidelberg2023

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Amer Chemical Soc2023

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

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Berend Smit, Senja Dominique Barthel

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...

Amer Chemical Soc2023

Atomistic modeling of C-S-H: bulk and surface

Ziga Casar

By replacing part of Portland cement with so-called supplementary cementitious materials (SCMs) it is possible to reduce the CO2 footprint of the cement industry. These SCMs are commonly limestone, calcined clay, slag and fly ash. While doing so the early ...

EPFL2023

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

Lattice Green's function method for Atomistic/Continuum coupling

Ankit Gupta

Metal plasticity is an inherently multiscale phenomenon due to the complex long-range field of atomistic dislocations that are the primary mechanism for plastic deformation in metals. Atomistic/Continuum (A/C) coupling methods are computationally efficient ...

EPFL2023

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Kevin Rossi

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Francesco Aquilante

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments sp ...

Washington2023

Advancing first principle-based molecular dynamics of biological systems with machine learning

François Louis Mouvet

Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...

EPFL2023

Computational Studies for the Design of Metalloenzyme Mimics

Guido Frisari

At present, there is no general standard automated method for engineering metalloenzymes, industrially-relevant systems able to catalyze environmentally friendly reactions. One of the most studied natural metalloenzymes is the second isoform of human carbo ...

EPFL2023

Learning Physics-Consistent Particle Interactions

Olga Fink, David Simon Kammer

Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hi ...

Oxford University Press2023

Modelling of metal alloys in realistic conditions with machine learning

Nataliya Lopanitsyna

Computer simulations based on statistical methods have emerged as a powerful tool for studying structure-property relationships at the atomistic level. However, to provide reliable insights into materials in realistic conditions, it is essential to accurat ...

EPFL2023