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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2017
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
4365 ESCH-SUR-ALZETTE
Luxembourg
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.