Cloud native EDA tools & pre-optimized hardware platforms
Machine-Learned Force Fields (ML FFs) provide near-ab initio accuracy for large realistic system sizes and dynamical simulation time-scales greatly exceeding those accessible to Density Functional Theory (DFT). Use ML FFs in QuantumATK to generate realistic complex structures of novel crystal and amorphous materials, alloys, interfaces, and multilayer stacks, simulate thermal and mechanical properties, diffusion and surface processes. Benefit from the pre-trained ML FF library or develop new ML FFs using automated and efficient training and simulation workflows.
The QuantumATK simulation engines enable atomic-scale modeling using multiple simulation methods in one platform : state-of-the-art density functional theory (DFT) with plane wave or LCAO basis sets, semi-empirical methods, conventional FFs (built-in database of 300 potentials) and ML FFs. All simulation engines share a common infrastructure for material property, molecular dynamics (MD), nudged elastic band (NEB), geometry optimization, and other simulations.
Generate amorphous structures for PCRAM, ReRAM, and FeRAM novel memories, solar cells, and other applications. In this example, 80ps ML FF-MD generated am- SiO2 structure of 600 atoms in 11 minutes, whereas it took 10 days to generate 72-atom structure with DFT-MD on 16 cores. Structural parameters obtained with ML FFs are in good agreement with DFT and experimental results.
Build and optimize complex crystalline and amorphous interfaces and multilayer stack structures for semiconductor development applications, such as high-k metal gate (HKMG) (using Multilayer builder GUI) and MRAM magnetic tunnel junction engineering. This example shows a generated structure of a nearly defect-free c-Si|am-SiO2|am-HfO2|am-Ti2N HKMG stack.
Generate glassy amorphous materials with impurities for optoelectronic applications. In this example, ML FF - MD is used to simulate a large-scale 120,000 atom size sodium silicate glass with Na impurities, (Na2O)2(SiO2)40000,, at 2500K.
Study ns-long crystallization and amorphization processes with ML FF - MD in large-scale systems for, e.g., PCRAM novel memory applications. This example depicts crystallization of 2520-atom phase change alloy material Ge2Sb2Te5.
Simulate thermal conductance using ML FFs with ns-long reverse non-equilibrium MD (RNEMD) simulations for developing PCRAM and evaluating self-heating and heat dissipation in devices. Examples include simulating thermal conductance in bulk Ge2Sb2Te5 (2300 atoms), Ge2Sb2Te5|Si (882 atoms), and Si|GaAs (864 atoms) interfaces, monolayer MoS2 (108,000 atoms). Calculated values are in good agreement with experimental and DFT results where available.
Simulate thermal ALD and ALE processes using specifically trained ML FFs with MD. This example shows the simulation of the thermal ALD process: HfCl4 deposition on an HfO2 surface of 4.5 nm2 area. Precursor adsorption energies are consistent with DFT results. Obtained sticking coefficient and coverage values can be used as parameters for feature scale models to optimize the yield of ALD.
QuantumATK offers Moment Tensor Potentials (MTPs), which provide high robust accuracy with lower computational cost compared to other ML Force Fields [2,3]. Benefit from the pre-trained ready-to-use high-quality MTP library (check [4,5] for the list of materials) or develop MTPs for new materials, interfaces and surface processes by using automatic generation workflows described below.
 S. Smidstrup, T. Markussen, P. Vancraeyveld, J. Wellendorf, J. Schneider, T. Gunst, B. Vershichel, D. Stradi, P. A. Khomyakov, U. G. Vej-Hansen, M.-E. Lee, S. T. Chill, F. Rasmussen, G. Penazzi, F. Corsetti, A. Ojanpera, K. Jensen, M. L. N. Palsgaard, U. Martinez, A. Blom, M. Brandbyge, and K. Stokbro, "QuantumATK: An integrated platform of electronic and atomic-scale modelling tools", J. Phys.: Condens. Matter 32, 015901 (2020). arXiv: 1905.02794v2.
 A. V. Shapeev, "Moment tensor potentials: a class of systematically improvable interatomic potentials", Multi-scale Model. & Simul. 14, 1153 (2016).
 Y. Zuo, C. Chen, X. Li, Z. Deng, Y. Chen, J. Behler, G. Csányi, A. V. Shapeev, A. P. Thompson, M. A. Wood, and S. Ping Ong, "Performance and cost assessment of machine learning interatomic potentials", J. Phys. Chem. A 124, 731 (2020).
 ML FF features: https://www.synopsys.com/silicon/quantumatk/resources/feature-list.html#MLforcefield
 Materials in the pre-trained ready-to-use ML FF library: https://docs.quantumatk.com/manual/ForceField.html#pretrained-moment-tensor-potential-mtp-parameter-sets
 Tutorial on automatic ML FF training tools and GUI templates: https://docs.quantumatk.com/tutorials/mtp_hfo2/mtp_hfo2.html
Interested in applying QuantumATK software to your research? Test our software or contact us at email@example.com to get more information on QuantumATK platform for atomic-scale modeling.