QuantumATK T-2022.03 version released on Mar 7, 2022

[Vaida Arcisauskaite]

We are very pleased to announce QuantumATK T-2022.03! The latest version of the QuantumATK atomic-scale modeling platform includes many new exciting features and performance improvements.  Attend a free Synopsys webinar on Mar 24, 2022 to learn more.

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.


Highlights of the QuantumATK T-2022.03 Release

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 (I_d-V_g, I_d-V_d 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.