Atomistic Simulation Software Announcements | QuantumATK - Synopsys

QuantumATK - New Release Notes

We are very pleased to announce QuantumATK T-2022.03. The latest version of the QuantumATK atomic-scale modeling platform includes many new and improved features and performance improvements.

If you are a customer entitled to maintenance services, you can download QuantumATK T-2022.03 installers, new license keys and full release notes directly from SolvNetPlus.

Machine-Learned (ML) Force Fields for Realistic Structures and Thermal Properties

  • 1000-10,000x shorter computational time vs DFT enable ab initio accuracy for large system sizes and time-scales greatly exceeding those accessible to DFT.
  • Use ML Force Fields - Moment Tensor Potentials (MTPs) with molecular dynamics (MD) to:
    • Generate realistic complex structures of novel crystal and amorphous bulk materials, alloys, interfaces, and multilayer stacks
      • Example applications: structure generation of GST materials for PCRAM, high-k metal gate stacks using the Multilayer Builder GUI (watch a video to learn more)
    • Simulate mechanical and thermal properties, e.g., for 2D materials
    • Model surface processes (thermal ALD & ALE)
    • Use in other cases where conventional Force Fields are not available/difficult to develop
  • Available MTP library with pre-trained MTPs for a range of bulk materials and interfaces.

Automated Generation of New Machine-Learned Force Fields

  • Automatic training tools and GUI templates for crystal and amorphous bulk materials, interfaces and molecules 
  • More efficient active-learning based generation of DFT training data during MD by starting from several different initial configurations in parallel 
  • Improved MTP training framework, including tools to finding most different training configurations to reduce MTP training datasets

Machine Learning-Based Surface Process Modeling

  • Efficiently simulate thermal ALD/ALE processes using specifically trained ML Force Fields, MTPs, with ab initio accuracy
  • Obtain parameters for feature scale models to optimize yield
  • Pre-trained MTP is provided for HfCl4 deposition on HfO2 surfaces (ALD)
  • Use a special MTP training protocol to generate ML Force Fields for new processes/materials

Realistic Physics of Complex Materials, Interfaces and Multilayer Stacks

  • Hybrid DFT HSE06-DDH method with LCAO basis sets for accurate electronic property simulations of realistic 1000+ atom systems
    • Extension to metals and interfaces/stacks containing metals (in addition to semi and insulators)
    • 2x speed-up for 1000+ atom systems and up to 20X speed-up for smaller systems
  • 10x more efficient electron-phonon coupling simulations; benefit for mobility simulations of systems with many k- and q-points
  • > 100x faster Hamiltonian Derivatives for systems with large unit cells and more accurate and faster Dynamical Matrix simulations 
    • Due to Wigner-Seitz method, enabling accurate simulations with smaller unit cell dimensions
    • Important for electron-phonon coupling, mobility, phonon bandstructure and DOS, Raman, dielectric tensor, and electrooptical tensor

Realistic Nanoelectronic IV Characteristics

  • Improved inelastic transport in systems with strong electron-phonon coupling, such as bulk-like devices, using the newly implemented One-Shot Self-Consistent Born Approximation method
  • Faster IV calculations and more accurate transport bandgaps with HSE06-DDH-NEGF
  • More accurate on-state calculations using Neumann boundary conditions in the transport direction compared to Dirichlet at the DFT level

Multiscale QuantumATK-Sentaurus Device Workflow for 2D FET Engineering

  • QuantumATK - Sentaurus Device QTX - Sentaurus Device workflow to investigate the impact of various parameters on the 2D material-based FET performance (Id-Vg, Id-Vd, and C-V characteristics)
    • Different 2D materials and number of layers for channel
    • Source/drain materials and orientations
    • Gate stack material parameters
    • Device architecture and dimensions
    • Doping concentrations and interface trap distribution
  • Interactive GUI for setting up and analyzing the workflow results

Novel STT-MRAM Memory Design

  • Model magnetization switching ability of different materials for MTJs in STT-MRAM devices by efficiently computing Spin Transfer Torque at finite bias

Battery Materials Modeling Improvements

  • New ionic conductivity and self-diffusion analysis for battery materials
  • Possibility to include long-range electrostatic interactions estimated from DFT in Force Fields when modeling liquid battery electrolytes

Polymer Simulation Improvements

  • Added Crosslink Builder templates for alcohol-isocyanate and sulfur vulcanization reactions
  • Faster crosslink reaction simulations 
  • Possibility to constrain bond lengths and angles in MD and optimization of molecules

NanoLab GUI Improvements

  • New NanoLab GUI layout, enabling to work efficiently with data intensive projects based on multiple simulations (watch a video to learn more)
  • More stable and efficient Job Manager to submit and monitor jobs
  • Improved plotting framework, including possibility to have dual axes: one logarithmic and another one-linear scale, and color code the data to match the particular axis 

Get QuantumATK T-2022.03

  • If you are a customer entitled to maintenance services, you can access QuantumATK T-2022.03 installers and new license keys directly from SolvNetPlus.
  • QuantumATK T-2022.03 release comes with significant licensing updates and every user who wants to run the new QuantumATK T-2022.03 version, will need to refresh the license file. Contact us or your license administrator for any question.

Additional Resources

Learn more about QuantumATK products

Interested in applying QuantumATK software to your research? Test our software or contact us at to get more information on QuantumATK platform for atomic-scale modeling.