msc project: machine-learning models for molecular simulations

Free energy surface Background Photoactive molecules have countless applications in energy conversion and storage, including, e.g., triplet-triplet annihilation up-conversion, singlet fission, and molecular solar thermal storage. The functionality of these materials is based on their structure being sensitive to light. The absorption of a photon can, e.g., trigger a change in the structure of an individual molecule or change the interaction between. Goal The goal is to build models for the prediction of molecular properties such as energy, forces, and absorption strength as a function of the atomic configuration with an accuracy that is on-par with the underlying first- principles calculations but at a fraction of the computational cost. Scope and techniques • Development of a framework for model construction, validation, and sampling using Python • Linear models based on atomic scale descriptors • Kernel methods (sGDML) MSc project: Machine-learning models for molecular simulations DOI: 10.1063/1.5078687 Potential energy surface Kernel method (sGDML) Photoactive molecules Strongly collaborative environment Collaboration with experiment Potential PhD project For more information: https://materialsmodeling.org Contact Prof. Paul Erhart [email protected]

Upload: others

Post on 21-Jun-2022

1 views

Category:

Documents

0 download

Report

Download

Embed Size (px):

TRANSCRIPT

Page 1: MSc project: Machine-learning models for molecular simulations

Free energy surface

BackgroundPhotoactive molecules have countless applications in energy conversion andstorage, including, e.g., triplet-triplet annihilation up-conversion, singletfission, and molecular solar thermal storage. The functionality of thesematerials is based on their structure being sensitive to light. The absorptionof a photon can, e.g., trigger a change in the structure of an individualmolecule or change the interaction between.GoalThe goal is to build models for the prediction of molecular properties suchas energy, forces, and absorption strength as a function of the atomicconfiguration with an accuracy that is on-par with the underlying first-principles calculations but at a fraction of the computational cost.Scope and techniques• Development of a framework for model construction,

validation, and sampling using Python• Linear models based on atomic scale descriptors• Kernel methods (sGDML)

MSc project: Machine-learningmodels for molecular simulations

DOI: 10.1063/1.5078687

Potential energy surface

Kernel method (sGDML) Photoactive molecules

Strongly collaborative environmentCollaboration with experimentPotential PhD project

For more information:https://materialsmodeling.org

ContactProf. Paul Erhart [email protected]

WestGrid / Compute Canada · Introduction to MD simulations Outline: Introduction Basic concepts of Molecular Dynamics Simulations. Examples of Simulations using Molecular Dynamics

Molecular Dynamics Simulations of Gold Nanomaterials

Molecular Biophysics III – dynamics Molecular Dynamics Simulations - 01/13/05

Rocket Engine Transient Simulations - MSC Software Corporation