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ML Potentials

Constructing Intermolecular Potentials by Combining Physics and Machine Learning

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "ML Potentials" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITE DU LUXEMBOURG 

Organization address
address: 2 AVENUE DE L'UNIVERSITE
city: ESCH-SUR-ALZETTE
postcode: 4365
website: http://wwwen.uni.lu

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Luxembourg [LU]
 Total cost 160˙800 €
 EC max contribution 160˙800 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-03-15   to  2020-03-14

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITE DU LUXEMBOURG LU (ESCH-SUR-ALZETTE) coordinator 160˙800.00

Map

 Project objective

Statistical-learning approaches are emerging as powerful alternatives to direct approaches to solving the electronic Schrödinger equation for determining the energy and other properties of molecules. Despite the recent success of methods like deep neural networks, these methods are limited to relatively small molecules. The issue is that predicting long-range intermolecular interactions with machine learning requires sampling the vast diversity of chemical environments that occur on an extended length scale, leading to a combinatorial explosion in the amount of training data that is required. To solve this problem, the functional form of the long-range interactions is taken from physical models, but the parameters that enter those expressions (atomic charges/multipoles; induced charges/multipoles; van der Waals coefficients) are determined by combining physical insight with machine learning. In this model, machine learning is used only to predict short-range phenomena like the dependence of atomic charges/multipoles on the molecular structure and the dependence of induced atomic charges/multipoles on the local electric field. The resulting machine-learned physically-motivated atomistic intermolecular potentials are valid for molecules of any size, but only require training data from small- and medium-sized molecules. This development will provide molecular energies with the accuracy of quantum methods, at the computational cost of classical molecular mechanics approaches. This not only allows one to compute interaction energies for large molecules (e.g., the binding energy between a drug and a receptor), but allows the computational screening of molecules based on computed interaction energies. In addition to its transformative computational utility, this pioneering strategy—using physical insight to build a model, then using machine learning methods for the parameters in the model—can be extended to many other problems in chemistry, physics, and materials science.

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The information about "ML POTENTIALS" are provided by the European Opendata Portal: CORDIS opendata.

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