Neural networks in quantum chemistry

Neural networks in quantum chemistry
Schematic diagram of descriptors (left) as inputs to the neural networks (right), along with hidden layers, and output.

Project reference: 2002

Neural networks (NN) and deep machine learning are two success stories in modern artificial intelligence. They have led to major advances in image recognition, automatic text generation, and even in self-driving cars. NNs are designed to model the way in which the brain performs a task or function of interest. It can perform complex computations with ease.

Quantum chemistry is a powerful tool to study properties of molecules and their reactions. The rapid development of HPC has greatly encouraged chemists to use quantum chemistry to understand, model, and predict molecular properties and their reactions, properties of nanometer materials, and reactions and processes taking place in biological systems.

An essential paradigm of chemistry is that the molecular structure defines chemical properties. Inverse chemical design turns this paradigm on its head by enabling property-driven chemical structure exploration [1].

The main goal of this project is to investigate NN frameworks which can emulate electronic wavefunction in local atomic orbital representation as a function of molecular composition and atom positions or other molecular descriptors and representations. Other objective is to apply NN frameworks as predictor of molecular properties (HOMO-LUMO gap, charges of atoms or evidence of hydrogen bonds) based on structural properties of these molecules. 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. To do so, we will compare GASPI/GPI-2 (http://www.gpi-site.com/gpi2) CPU asynchronous parallel implementation with CUDA.

[1] Schütt, K.T., Gastegger, M., Tkatchenko, A. et al. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat Commun 10, 5024 (2019)

Schema of GASPI matrix-matrix multiplication and memory layout on a rank (parallel process).

Schematic diagram of descriptors (left) as inputs to the neural networks (right), along with hidden layers, and output.

Project Mentor: Ing. Marián Gall, PhD.

Project Co-mentor: Doc. Mgr. Michal Pitoňák, PhD.

Site Co-ordinator: Mgr. Lukáš Demovič, PhD.

Learning Outcomes:
Student will learn a lot about Neural Networks, GASPI (C/C++ or Fortran) and CUDA.

Student Prerequisites (compulsory)
Basic knowledge of C/C++ or Fortran, MPI, basic chemistry/physics background.

Student Prerequisites (desirable):
Advanced knowledge of C/C++ or Fortran and MPI, BLAS libraries and other HPC tools. Basic knowledge of GASPI, neural networks, quantum chemistry background.

Training Materials:

Workplan:

  • Week 1: training;
  • Weeks 2-3: introduction to neural network, GASPI, quantum chemistry and efficient implementation of algorithms,
  • Weeks 4-7: implementation, optimization and extensive testing/benchmarking of the codes,
  • Week 8: report completion and presentation preparation

Final Product Description:
Expected project result is (C/C++ or Fortran) GASPI implementation of (selected) neural network algorithm, applied to quantum chemistry problem. Code will be benchmarked and compared to CUDA implementation.

Adapting the Project: Increasing the Difficulty:
Writing own NN algorithm using CUDA.

Adapting the Project: Decreasing the Difficulty:
Applying existent NN implementation to quantum chemistry problems.

Resources:
Student will have access to the necessary learning material, as well as to our local IBM P775 supercomputer and x86 infiniband clusters. The software stack we plan to use is open source.

Organisation:
Computing Center, Centre of Operations of the Slovak Academy of Sciences

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