Materials Process Modelling

First principles atomistic modelling of materials processing. This encompasses atomic layer deposition (ALD), atomic layer etching (ALE) and molecular layer deposition (MLD), as well as metal deposition on substrates. This can be used to assess deposition chemistry and enable selection of suitable process chemistries.

Atomistic materials process modelling

Deposition and etch of thin films is dominated by atomic layer deposition (ALD), atomic layer etching (ALE) and, more recently, molecular layer deposition for hybrid materials (MLD); these are grouped under the term “atomic level processing”. Since atomic level processing is driven by the chemistry of precursors at surfaces it is a natural candidate for atomistic level modelling, using first principles density functional theory (DFT). DFT models surfaces and surface chemistry to a good level of accuracy, treating hundreds of atoms at a time.

We can determine the thermodynamics of potential reactions and compute activation barriers for particular processes, allowing us to assess if the chemistry is feasible and what reaction by-products are possible. Furthermore, one can use activation barriers from DFT as input to kinetic Monte Carlo simulations to explore film growth on much larger and complex models, obtaining results that can be directly compared to experiment.

We offer expertise in the materials modelling group on ALD, ALE and MLD, as well as on the deposition of metals and their surface chemistry for studies of barrier and interconnect materials.

Tyndall’s high performance computing cluster is a heterogeneous system of approx. 4000 CPU cores, high memory (minimum 192 GB) and significant storage. It is one of the largest Tier-2, local clusters in Ireland. The cluster runs Linux, with Fortran and C compilers, as well as Python and Matlab. It is a heterogeneous system that runs many modelling codes in parallel to allow a significant scale of modelling across multiple scales. Experts in the materials modelling group can be consulted to run these modelling jobs.

The HPC cluster runs a range of modelling software focused on atomistic simulation of materials and devices, including Quantum Espresso, QuantumATK, ABINIT, GULP, DFTB, LAMMPS, VASP and TURBOMOLE. It runs these codes in parallel on a queue system with reasonable performance between 4 and 96 CPUs.

There is a large group of experienced users from PhD students, to researchers and senior researchers with expertise in the different modelling software who are available to work with ASCENT+ Users to efficiently use this offering.

ALD, ALE, deposition, etch, modelling, DFT, interconnect


  • 4,000 CPU cores
  • Nodes of 48, 64 or 96 cores
  • AMD and Intel CPUs
  • High RAM (192 – 256 GB / node)
  • 120 TB storage
  • Intel 2019 compiler suite (Fortran, C)
  • Python
  • PBS queue system
  • MPI and OpenMP
  • Software: VASP (if applicant has a licence), Quantum Espresso, ABINIT, Gulp, DFTB, LAMMPS, Quantum-ATK

Ascent+ facility

Platform Technology

  • Disruptive Devices

Key Enabling Capability

  • Modelling / Databases

Typical Application

Modelling of atomic layer etching (ALE) is beginning to take off, giving new insights into the chemistry of this process and contributing to growing its application in device processing. Using the Tyndall HPC cluster, we have explored the modelling of ALE of oxides and metals. In 2020, we published two papers on this topic, enabled by access to the HPC cluster: [DOI: 10.1021/acsami.0c06628] and [DOI: 10.1021/acs.chemmater.9b05021]


Key Enabling Capability

Modelling / Databases

Platform Technology

Disruptive Devices