Open PhD positions (Click here):
1. High temperature fuel cells and electrolysis, from metadata to longer lifetimes
2. Inverted neural networks for optimizing permanent magnet structures
3. Machine Learning Orchestrated Discovery and Synthesis of Organic Energy Materials
2 Open Postdoc positions (Click here):
Modelling of cathode and electrolyte materials for Mg-ion and Mg-Sulfur batteries
Contact: Project Manager Prof. Tejs Vegge, Head of Section, email: firstname.lastname@example.org Follow @TwitterDev
The current scientific paradigm for discovery of clean energy materials is based on human intuition about how specific properties are connected to their design and composition. The goal of Autonomous Materials Discovery (AiMade), at the Department of Energy Conversion and Storage (DTU Energy) at the Technical University of Denmark (DTU), is to challenge this mindset. In this four year initiative, we will strive to establish an platform for autonomous materials discovery of clean energy materials through creation of a common data-infrastructure and holistic ontology for materials data, connecting data and information from simulations, characterization, synthesis and testing, spanning multiple time- and length-scales.
The structure of AiMade revolves around four central competence areas plus a general machinery, each led by their own expert and encompassing a wide range of different competences required for the full autonomous materials design loop.
We will develop novel software tools to orchestrate the autonomous discovery process and ensure data interoperability between all elements of materials discovery cycle. The data-infrastructure will be developed to match the four pillars, but be versatile enough to accommodate other clean energy materials, applications and techniques. Leader: Tejs Vegge, professor and head of section, Section for Atomic Scale Materials Modelling, DTU Energy.
The metadata area focuses on the processing and structuring of human-generated data from experiments such that it can enter efficiently in the autonomous design loop. Leader: Anke Hagen, professor and head of section, Section for Electrochemistry, DTU Energy.
This competence area revolves around the use of computational methods such as density functional theory and machine-learning for accelerating and guiding the materials design. Using both computational data generated for the purpose and pre-existing data from online databases, the goal is to systematically collect information that can guide the design process. Leader: Ivano E. Castelli, Assistant Professor, Section for Atomic Scale Materials Modelling, DTU Energy.
This competence area is focused on the design of materials for specific target applications. This is done through an inverse design process, which first declares a desired application and subsequently evaluates potential material candidates in close collaboration with the other competence area. Leader: Kaspar Kirstein Nielsen, Associate Professor, Section for Continuum Modeling and Testing, DTU Energy.
The purpose of this area is to integrate the work from all the other competence areas into a complete autonomous design loop, which goes from the computational prediction to the experimental synthesis and characterization. Leader: Johan Hjelm, Associate Professor, Section for Electrochemistry, DTU Energy.
Open Positions at DTU Energy
Mission Innovation and the Clean Energy Materials Innovation Challenge (IC6)
Read more about autonomous materials discovery here(Materials Acceleration Platform (MAP), Alan Aspuru-Guzik, Kristin Persson, ..., Tejs Vegge, Materials Acceleration Platform - Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence, Mission Innovation Challenge 6 White Paper (2018)) and here (Clean Energy Materials Innovation Challenge, Tejs Vegge, Kristian S. Thygesen, Alan Aspuru-Guzik, DTU International Energy Report 2018, 81-88 (2018)).