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Summer of HPC: Neural networks in quantum chemistry

Summer of HPC: Neural networks in quantum chemistry. Join our team in Bratislava for the summer of 2021 and gain experience in programming neural networks and their usage in predicting molecular properties.

Summer of HPC: Neural networks in quantum chemistry

Join our team in Bratislava for the summer of 2021 and gain experience in programming neural networks and their usage in predicting molecular properties. PRACE partner countries and students studying at academic institutions in Europe have the opportunity to participate in summer internships at a HPC centre. Summer of HPC is a PRACE programme that offers summer placements at HPC centres across Europe to late-stage undergraduate and master’s students.

The main goal of this project is to investigate ability of NN frameworks to simulate molecular properties, where NN can emulate electronic wavefunction in local atomic orbitals representation as a function of molecular composition and atom positions or other molecular descriptors and representations. Our objective is to apply NN frameworks as predictor of molecular properties (energies, charges on atoms or evidence of hydrogen bonds) based on structural properties of these molecules (atomic positions). Implementation of NN frameworks will be performed using widely adopted TensorFlow library in Python code. For generation of molecular descriptors of chemical systems we apply DScribe library, which can be incorporated as a module directly in Python code. Next to the aforementioned “application part” of the project, we also plan to (in)validate the widely accepted fact, that GPGPUs are superior execution platform for NNs to CPUs.

Late-stage undergraduate and master’s students are invited to apply. Background in quantum-chemistry or physics is needed. We would alsowelcome advanced knowledge of fortran, basic knowledge of MPI, BLAS libraries and other HPC tools. Previous experience in HPC is not required as training will be provided. The most important attribute is a desire to learn, and share experiences with HPC.

Student will have access to the necessary learning material, as well as to our local IBM P775 supercomputer and x86 infiniband clusters.

The summer programme begins on July 1st and ends on August 30th 2021. At the end of the internship students present their projects and may win a prize for the best project!

Applications deadline is April 12th 2021.

Apply now

Training materials

More info about this project

BeeGFS in Practice — Parallel File Systems for HPC, AI and Data-Intensive Workloads 6 Feb - This webinar introduces BeeGFS, a leading parallel file system designed to support demanding HPC, AI, and data-intensive workloads. Experts from ThinkParQ will explain how parallel file systems work, how BeeGFS is architected, and how it is used in practice across academic, research, and industrial environments.
When a production line knows what will happen in 10 minutes 5 Feb - Every disruption on a production line creates stress. Machines stop, people wait, production slows down, and decisions must be made under pressure. In the food industry—especially in the production of filled pasta products, where the process follows a strictly sequential set of technological steps—one unexpected issue at the end of the line can bring the entire production flow to a halt. But what if the production line could warn in advance that a problem will occur in a few minutes? Or help decide, already during a shift, whether it still makes sense to plan packaging later the same day? These were exactly the questions that stood at the beginning of a research collaboration that brought together industrial data, artificial intelligence, and supercomputing power.
Who Owns AI Inside an Organisation? — Operational Responsibility 5 Feb - This webinar focuses on how organisations can define clear operational responsibility and ownership of AI systems in a proportionate and workable way. Drawing on hands-on experience in data protection, AI governance, and compliance, Petra Fernandes will explore governance approaches that work in practice for both SMEs and larger organisations. The session will highlight internal processes that help organisations stay in control of their AI systems over time, without creating unnecessary administrative burden.