QuantumATK Forum
General => News and Announcements => Topic started by: Vaida Arcisauskaite on March 9, 2022, 23:29
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We are very pleased to announce QuantumATK T-2022.03! (https://www.synopsys.com/silicon/quantumatk/resources/release-notes.html) 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. (https://attendee.gotowebinar.com/rt/3228194047131728397?source=forum)
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. (https://solvnet.synopsys.com)
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 (https://www.youtube.com/watch?v=_-SUW5UutXo) 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 (https://www.youtube.com/watch?v=9H-2cNNpf_c) 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. (https://solvnet.synopsys.com)
-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.