Implementation of the recursive method for calculating Green's functions in the parallel quantum transport simulator

NEGF formalizam moćan je teorijski okvir za računanje kvantnog transporta i analizu nanostruktura, ali uključuje resursno zahtjevan izračun inverza velikih matrica radi dobivanja retardirane Greenove funkcije. Rekurzivna metoda može se koristiti za izračun Greenove funkcije uz izvršenje znatno manjeg broja računskih operacija, bez izračuna punog inverza. Ovaj rad opisuje implementaciju rekurzivne metode u postojećem kvantnotransportnom simulatoru SPARQS. Performanse implementacije testirane su simulacijama bizmutenskih nanovrpci različitih dimenzija na klasteru za paralelno računanje. Izmjerena vremena izvršavanja za rekurzivnu metodu bila su značajno manja u svim testiranim slučajevima, s promatranim ubrzanjima na razini od nekoliko puta do nekoliko redova veličine boljim u usporedbi sa standardnom metodom.NEGF formalism is a powerful framework for quantum transport and for the analysis of nanostructures, but requires a resource-consuming inversion calculation of large matrices to obtain the retarded Green’s functions. Recursive method can be employed to calculate the Green’s function with significantly less operations, evading the full matrix inversion. This paper describes the implementation of the method into an existing quantum transport simulator SPARQS. Performance of the implementation is tested on a parallel computing cluster by simulating bismuthene nanoribbons of various sizes. Observed execution times for the recursive method were lower in all cases, with execution acceleration ranging from several times to several orders of magnitude compared to the standard method

Predicting electronic and transport properties of silicene nanoribbons using a neural network

Računalne simulacije danas su standardni dio procesa istraživanja i razvoja nanomaterijala i nanokomponenti. Simulacije su ubrzane optimizacijama i masovnom paralelizacijom, ali usprkos tome, vrijeme izvođenja i dalje je dugotrajno. Neuronske mreže nedavno su pokazale potencijal za unapređenje tog procesa. Ovaj rad istražuje performanse neuronskih mreža na primjeru predikcije transportnih svojstava silicenskih nanovrpci (SiNR). Neuronska mreža s tri skrivena sloja trenirana je i evaluirana nad setom od ~10000 nanovrpci širine 2,1 nm dobivenih atomističkim simulacijama. Nakon toga performanse iste mreže (bez novog treniranja) testirane su za nanovrpce drugih širina. Mreža je pokazala dobru kvalitetu predikcije za SiNR širine 2,1 nm, a dodatna testiranja demonstrirala su da neuronska mreža ima potencijal za predviđanje svojstava nanovrpci i za veći raspon dimenzija.Numerical simulations are nowadays a standard part of nanomaterial and nanodevice research and development. Various optimizations and massively parallel processing techniques are used to accelerate simulation process, but time to solution is still relatively high. Neural networks (NN) have recently shown potential for improvement of that process. This paper tests NN performance on silicence nanoribbon (SiNR) example, with the goal of predicting their transport properties. Atomistic simulations were used to generate a set of ~10000 SiNRs 2.1 nm wide, which was then used for training and evaluation. Additionally, the network was tested (without further training) on nanoribbons with different widths. NN has shown good prediction performance for 2.1 nm wide SiNRs and testing with other SiNR widths has demonstrated network’s potential for predicting the nanoribbon properties

Implementation of the recursive method for calculating Green's functions in the parallel quantum transport simulator

NEGF formalizam moćan je teorijski okvir za računanje kvantnog transporta i analizu nanostruktura, ali uključuje resursno zahtjevan izračun inverza velikih matrica radi dobivanja retardirane Greenove funkcije. Rekurzivna metoda može se koristiti za izračun Greenove funkcije uz izvršenje znatno manjeg broja računskih operacija, bez izračuna punog inverza. Ovaj rad opisuje implementaciju rekurzivne metode u postojećem kvantnotransportnom simulatoru SPARQS. Performanse implementacije testirane su simulacijama bizmutenskih nanovrpci različitih dimenzija na klasteru za paralelno računanje. Izmjerena vremena izvršavanja za rekurzivnu metodu bila su značajno manja u svim testiranim slučajevima, s promatranim ubrzanjima na razini od nekoliko puta do nekoliko redova veličine boljim u usporedbi sa standardnom metodom.NEGF formalism is a powerful framework for quantum transport and for the analysis of nanostructures, but requires a resource-consuming inversion calculation of large matrices to obtain the retarded Green’s functions. Recursive method can be employed to calculate the Green’s function with significantly less operations, evading the full matrix inversion. This paper describes the implementation of the method into an existing quantum transport simulator SPARQS. Performance of the implementation is tested on a parallel computing cluster by simulating bismuthene nanoribbons of various sizes. Observed execution times for the recursive method were lower in all cases, with execution acceleration ranging from several times to several orders of magnitude compared to the standard method

Predicting electronic and transport properties of silicene nanoribbons using a neural network

Računalne simulacije danas su standardni dio procesa istraživanja i razvoja nanomaterijala i nanokomponenti. Simulacije su ubrzane optimizacijama i masovnom paralelizacijom, ali usprkos tome, vrijeme izvođenja i dalje je dugotrajno. Neuronske mreže nedavno su pokazale potencijal za unapređenje tog procesa. Ovaj rad istražuje performanse neuronskih mreža na primjeru predikcije transportnih svojstava silicenskih nanovrpci (SiNR). Neuronska mreža s tri skrivena sloja trenirana je i evaluirana nad setom od ~10000 nanovrpci širine 2,1 nm dobivenih atomističkim simulacijama. Nakon toga performanse iste mreže (bez novog treniranja) testirane su za nanovrpce drugih širina. Mreža je pokazala dobru kvalitetu predikcije za SiNR širine 2,1 nm, a dodatna testiranja demonstrirala su da neuronska mreža ima potencijal za predviđanje svojstava nanovrpci i za veći raspon dimenzija.Numerical simulations are nowadays a standard part of nanomaterial and nanodevice research and development. Various optimizations and massively parallel processing techniques are used to accelerate simulation process, but time to solution is still relatively high. Neural networks (NN) have recently shown potential for improvement of that process. This paper tests NN performance on silicence nanoribbon (SiNR) example, with the goal of predicting their transport properties. Atomistic simulations were used to generate a set of ~10000 SiNRs 2.1 nm wide, which was then used for training and evaluation. Additionally, the network was tested (without further training) on nanoribbons with different widths. NN has shown good prediction performance for 2.1 nm wide SiNRs and testing with other SiNR widths has demonstrated network’s potential for predicting the nanoribbon properties

Implementation of the recursive method for calculating Green's functions in the parallel quantum transport simulator

NEGF formalizam moćan je teorijski okvir za računanje kvantnog transporta i analizu nanostruktura, ali uključuje resursno zahtjevan izračun inverza velikih matrica radi dobivanja retardirane Greenove funkcije. Rekurzivna metoda može se koristiti za izračun Greenove funkcije uz izvršenje znatno manjeg broja računskih operacija, bez izračuna punog inverza. Ovaj rad opisuje implementaciju rekurzivne metode u postojećem kvantnotransportnom simulatoru SPARQS. Performanse implementacije testirane su simulacijama bizmutenskih nanovrpci različitih dimenzija na klasteru za paralelno računanje. Izmjerena vremena izvršavanja za rekurzivnu metodu bila su značajno manja u svim testiranim slučajevima, s promatranim ubrzanjima na razini od nekoliko puta do nekoliko redova veličine boljim u usporedbi sa standardnom metodom.NEGF formalism is a powerful framework for quantum transport and for the analysis of nanostructures, but requires a resource-consuming inversion calculation of large matrices to obtain the retarded Green’s functions. Recursive method can be employed to calculate the Green’s function with significantly less operations, evading the full matrix inversion. This paper describes the implementation of the method into an existing quantum transport simulator SPARQS. Performance of the implementation is tested on a parallel computing cluster by simulating bismuthene nanoribbons of various sizes. Observed execution times for the recursive method were lower in all cases, with execution acceleration ranging from several times to several orders of magnitude compared to the standard method

Predicting electronic and transport properties of silicene nanoribbons using a neural network

Računalne simulacije danas su standardni dio procesa istraživanja i razvoja nanomaterijala i nanokomponenti. Simulacije su ubrzane optimizacijama i masovnom paralelizacijom, ali usprkos tome, vrijeme izvođenja i dalje je dugotrajno. Neuronske mreže nedavno su pokazale potencijal za unapređenje tog procesa. Ovaj rad istražuje performanse neuronskih mreža na primjeru predikcije transportnih svojstava silicenskih nanovrpci (SiNR). Neuronska mreža s tri skrivena sloja trenirana je i evaluirana nad setom od ~10000 nanovrpci širine 2,1 nm dobivenih atomističkim simulacijama. Nakon toga performanse iste mreže (bez novog treniranja) testirane su za nanovrpce drugih širina. Mreža je pokazala dobru kvalitetu predikcije za SiNR širine 2,1 nm, a dodatna testiranja demonstrirala su da neuronska mreža ima potencijal za predviđanje svojstava nanovrpci i za veći raspon dimenzija.Numerical simulations are nowadays a standard part of nanomaterial and nanodevice research and development. Various optimizations and massively parallel processing techniques are used to accelerate simulation process, but time to solution is still relatively high. Neural networks (NN) have recently shown potential for improvement of that process. This paper tests NN performance on silicence nanoribbon (SiNR) example, with the goal of predicting their transport properties. Atomistic simulations were used to generate a set of ~10000 SiNRs 2.1 nm wide, which was then used for training and evaluation. Additionally, the network was tested (without further training) on nanoribbons with different widths. NN has shown good prediction performance for 2.1 nm wide SiNRs and testing with other SiNR widths has demonstrated network’s potential for predicting the nanoribbon properties

Lower Limits of Contact Resistance in Phosphorene Nanodevices with Edge Contacts

Edge contacts are promising for improving carrier injection and contact resistance in devices based on two-dimensional (2D) materials, among which monolayer black phosphorus (BP), or phosphorene, is especially attractive for device applications. Cutting BP into phosphorene nanoribbons (PNRs) widens the design space for BP devices and enables high-density device integration. However, little is known about contact resistance (RC) in PNRs with edge contacts, although RC is the main performance limiter for 2D material devices. Atomistic quantum transport simulations are employed to explore the impact of attaching metal edge contacts (MECs) on the electronic and transport properties and contact resistance of PNRs. We demonstrate that PNR length downscaling increases RC to 192 Ω µm in 5.2 nm-long PNRs due to strong metallization effects, while width downscaling decreases the RC to 19 Ω µm in 0.5 nm-wide PNRs. These findings illustrate the limitations on PNR downscaling and reveal opportunities in the minimization of RC by device sizing. Moreover, we prove the existence of optimum metals for edge contacts in terms of minimum metallization effects that further decrease RC by ~30%, resulting in lower intrinsic quantum limits to RC of ~90 Ω µm in phosphorene and ~14 Ω µm in ultra-narrow PNRs