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Discovery and Insight with Advanced Models Of Nanoscale Dimensions

Generating knowledge about new materials and obtaining insight in their properties at the nanoscale level are highly relevant to the scientific objectives of the EU. Here, I propose to advance the current state of the art in atomi... ver más

31/10/2016

ETH Zürich

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

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Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-10-31 No tenemos la información de la convocatoria

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1 Participantes

ETH Zürich

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Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Generating knowledge about new materials and obtaining insight in their properties at the nanoscale level are highly relevant to the scientific objectives of the EU. Here, I propose to advance the current state of the art in atomistic modeling of complex systems. I aim at providing and establishing new tools that will allow for the description of large multi-component/multi-phase systems at experimental temperature and pressure with predictive power and controlled error. Generality and ease of use will be key. Building upon my experience, I have identified two clear needs that I will address. One need is a capable implementation, i.e. suitable for large condensed phase systems, of electronic structure theories that go beyond traditional DFT. Powerful linear scaling methods with excess accuracy are essential to validate, on the complex systems themselves, the use of DFT. The second need is an automatic approach for extracting empirical models from raw electronic structure data. Empirical methods are essential to perform simulations that are multiscale in time, space, and accuracy. This automatic approach must be able to generate models beyond the intuition and patience of an individual scientist using advanced optimization methods such as genetic algorithms or neural networks. Models must have a built-in estimate of their quality. The latter feature will allow for enhancing/correcting these empirical approaches automatically with first principles calculations whenever necessary. Massively parallel computing will be the enabling technology. In line with my track record, I will establish these new methods by demonstrating their potential through challenging applications. Example applications will be in diverse fields, including sustainable energy production, catalysis, environment and health. By making these tools freely and openly available to both academia and industry the benefit for the community as a whole will be significant.

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