Efficient and Accurate Density Functional Theory for Materials by Design

Research area

Ab initio simulations on massive parallel supercomputers, has become in the recent years, a powerful investigation tool widely used both for basic or applied research in the field of condensed matter. The BigDFT package (http://bigdft.org), developed in the framework of an European project coordinated by L_Sim, is at present in its production version and is routinely used in hybrid and parallel computers. BigDFT is a code with optimal features of reliability and precision, suitable to address different issues connected to materials design for energy or microelectronic applications. The Laboratory is at present involved in several projects (funded either by French or European Funding Agencies) related to the topic of electronic structure calculations for the former applications [1-2] and for potential energy surface explorations [3].

The project will be carried out at L_Sim, under the supervision of Dr. Pascal Pochet. This project involves collaboration with the group of N. Mingo at LITEN. The LITEN team has ongoing activities in the areas of transport in solids, and in high throughput computation of materials properties [4-6]. The postdoc will interact with the LITEN group to apply machine learning techniques for the study of material properties carried out with BigDFT, towards the design of new functional materials.

[1] Deciphering mechanisms of enhanced-retarded oxygen diffusion in doped Si, D. Timerkaeva, D. Caliste, and P. Pochet, Appl. Phys. Lett.103 251909 (2013).

[2] S. Krishnan, G. Brenet, E. Machado-Charry, D. Caliste, L. Genovese T. Deutsch and P. Pochet, Revisiting the domain model for lithium intercalated graphite, Appl. Phys. Lett. 103 251904 (2013).

[3] E. Machado-Charry, L. K. Beland, D. Caliste, L. Genovese,T. Deutsch, N. Mousseau and P. Pochet, Optimized energy landscape exploration using the ab initio based ART-nouveau; J. Chem. Phys.135, 034102 (2011).

[4] J. Carrete, Wu Li, N. Mingo, S. Wang, S. Curtarolo, Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling, Phys. Rev. X, 4, 011019 (2014)

[5] J Carrete, N Mingo, S Wang, S Curtarolo, nanograined half‐Heusler semiconductors as advanced Thermoelectrics: An Ab Initio High‐Throughput Statistical Study, Advanced Functional Materials, 24, 7427–7432 (2014).

[6] S. Curtarolo, G. L. W. Hart, M. Buongiorno Nardelli, N. Mingo, S. Sanvito, and O. Levy, The high-throughput highway to computational materials design, Nature Materials, 12 (3), 191-201 (2013).

For more details about ongoing work, visit http://inac.cea.fr/L_Sim/ and http://www-liten.cea.fr/uk/activites_rd/mgt3.htm

The net salary range is 2,100€ - 2,400€ per month, depending on seniority, qualification and experience. Positions are available immediately but the starting date has to be scheduled with a minimum of three months in advance. The position is for one year renewable for one additional year upon mutual agreement.

Application conditions

The applicant must have strong skills in quantum physics, atomistic simulations and solid state physics, licensed by a PhD or an acknowledged research experience in the domain. A good knowledge of computer programming and environment is considered as an extra merit.

Interested candidates should send curriculum vitae, list of publications (preprints of unpublished papers are also accepted, but in a separate list) and arrange for two to three references addressed to:

Pascal Pochet

INAC, CEA Grenoble, cedex 9

F-38054 Grenoble, France

Clarification or further details can be obtained via email to [email protected] and [email protected]