Strojno učenje u fizici čvrstog stanja i statističkoj fizici

In this work, we study an alternative approach to simulating molecular dynamics of large systems over long time periods; the one using machine learning instead of DFT. Studied system is a ruthenium surface which is interacting with hydrogen atoms. We use neural networks to obtain the regression model for the studied system, and train several different architectures of neural networks in order to find the optimal one. For input we use Gaussian descriptors which take Cartesian coordinates of the atoms and translate them in a more suitable form. In this case, there are 20 descriptors for each type of atoms, meaning that input layer of neural network has in total 40 nodes. Optimal architecture was found to be the one with three hidden layers with 50, 30, and 10 nodes, respectively. It was shown how our regression model behaves depending on number of training steps, importance of used descriptors was analyzed, and it was shown how model behaves if Zernike descriptors are used instead of Gaussian, or if cutoff radius is altered.U ovom radu proučavamo alternativni pristup simuliranju molekularne dinamike velikih sustava preko dugih vremenskih perioda; korištenje strojnog učenja umjesto DFT-a. Proučavani sustav sastoji se od površine rutenija koja međudjeluje s atomima vodika. Za konstruiranje regresijskog modela koristimo neuronske mreže te treniramo nekoliko različitih arhitektura mreža kako bismo našli optimalnu. Kao ulaz koristimo Gaussove deskriptore koji kartezijeve koordinate atoma pretvore u oblik pogodniji za opis sustava. U ovom slučaju, postoji 20 deskriptora za svaku vrstu atoma, što znači da u ulaznom sloju neuronske mreže imamo 40 čvorova. Pokazano je da optimalna arhitektura neuronske mreže sadrži tri skrivena sloja, od kojih prvi ima 50 čvorova, drugi 30, a treći 10. Osim toga, pokazano je kako se taj regresijski model ponaša ovisno o broju koraka prilikom treniranja, analizirana je važnost korištenih deskriptora te je proučeno ponašanje modela u slučaju korištenja Zernike deskriptora umjesto Gaussovih, ili mijenjanja polumjera obuhvaćanja atoma

Strojno učenje u fizici čvrstog stanja i statističkoj fizici

In this work, we study an alternative approach to simulating molecular dynamics of large systems over long time periods; the one using machine learning instead of DFT. Studied system is a ruthenium surface which is interacting with hydrogen atoms. We use neural networks to obtain the regression model for the studied system, and train several different architectures of neural networks in order to find the optimal one. For input we use Gaussian descriptors which take Cartesian coordinates of the atoms and translate them in a more suitable form. In this case, there are 20 descriptors for each type of atoms, meaning that input layer of neural network has in total 40 nodes. Optimal architecture was found to be the one with three hidden layers with 50, 30, and 10 nodes, respectively. It was shown how our regression model behaves depending on number of training steps, importance of used descriptors was analyzed, and it was shown how model behaves if Zernike descriptors are used instead of Gaussian, or if cutoff radius is altered.U ovom radu proučavamo alternativni pristup simuliranju molekularne dinamike velikih sustava preko dugih vremenskih perioda; korištenje strojnog učenja umjesto DFT-a. Proučavani sustav sastoji se od površine rutenija koja međudjeluje s atomima vodika. Za konstruiranje regresijskog modela koristimo neuronske mreže te treniramo nekoliko različitih arhitektura mreža kako bismo našli optimalnu. Kao ulaz koristimo Gaussove deskriptore koji kartezijeve koordinate atoma pretvore u oblik pogodniji za opis sustava. U ovom slučaju, postoji 20 deskriptora za svaku vrstu atoma, što znači da u ulaznom sloju neuronske mreže imamo 40 čvorova. Pokazano je da optimalna arhitektura neuronske mreže sadrži tri skrivena sloja, od kojih prvi ima 50 čvorova, drugi 30, a treći 10. Osim toga, pokazano je kako se taj regresijski model ponaša ovisno o broju koraka prilikom treniranja, analizirana je važnost korištenih deskriptora te je proučeno ponašanje modela u slučaju korištenja Zernike deskriptora umjesto Gaussovih, ili mijenjanja polumjera obuhvaćanja atoma

Strojno učenje u fizici čvrstog stanja i statističkoj fizici

In this work, we study an alternative approach to simulating molecular dynamics of large systems over long time periods; the one using machine learning instead of DFT. Studied system is a ruthenium surface which is interacting with hydrogen atoms. We use neural networks to obtain the regression model for the studied system, and train several different architectures of neural networks in order to find the optimal one. For input we use Gaussian descriptors which take Cartesian coordinates of the atoms and translate them in a more suitable form. In this case, there are 20 descriptors for each type of atoms, meaning that input layer of neural network has in total 40 nodes. Optimal architecture was found to be the one with three hidden layers with 50, 30, and 10 nodes, respectively. It was shown how our regression model behaves depending on number of training steps, importance of used descriptors was analyzed, and it was shown how model behaves if Zernike descriptors are used instead of Gaussian, or if cutoff radius is altered.U ovom radu proučavamo alternativni pristup simuliranju molekularne dinamike velikih sustava preko dugih vremenskih perioda; korištenje strojnog učenja umjesto DFT-a. Proučavani sustav sastoji se od površine rutenija koja međudjeluje s atomima vodika. Za konstruiranje regresijskog modela koristimo neuronske mreže te treniramo nekoliko različitih arhitektura mreža kako bismo našli optimalnu. Kao ulaz koristimo Gaussove deskriptore koji kartezijeve koordinate atoma pretvore u oblik pogodniji za opis sustava. U ovom slučaju, postoji 20 deskriptora za svaku vrstu atoma, što znači da u ulaznom sloju neuronske mreže imamo 40 čvorova. Pokazano je da optimalna arhitektura neuronske mreže sadrži tri skrivena sloja, od kojih prvi ima 50 čvorova, drugi 30, a treći 10. Osim toga, pokazano je kako se taj regresijski model ponaša ovisno o broju koraka prilikom treniranja, analizirana je važnost korištenih deskriptora te je proučeno ponašanje modela u slučaju korištenja Zernike deskriptora umjesto Gaussovih, ili mijenjanja polumjera obuhvaćanja atoma

DNA Sequence Polishing Using Deep learning

Cilj ovog rada bio je implementirati model temeljen na dubokom uˇcenju koji bi pove´cao toˇcnost sastavljenog genoma. Konstruirani model temelji se na arhitekturi rezidualne neuronske mreže te postiže toˇcnost ve´cu od trenutno najboljih alata temeljenih na klasiˇcnom raˇcunarstvu, no zaostaje ne nadmašuje najbolje alate temeljene na dubokom ucˇenju. Med¯utim, zbog ogranicˇenja u vidu prostora za pohranu podataka i vremena treniranja modela, detaljnija analiza tek treba biti napravljena za više bakterijskih uzoraka i razliˇcite arhitekture mreže.Goal of this work was to implement a deep learning model in order to increase the accuracy of the assembled genome. This model is based on a residual network architecture and achieves higher accuracy than classical state-of-the-art tools, but falls behind the best deep-learning tools available. However, due to limitations in terms of storage and time for training the model, further analysis has to be conducted for different bacteria datasets and network architectures

DNA Sequence Polishing Using Deep learning

Cilj ovog rada bio je implementirati model temeljen na dubokom uˇcenju koji bi pove´cao toˇcnost sastavljenog genoma. Konstruirani model temelji se na arhitekturi rezidualne neuronske mreže te postiže toˇcnost ve´cu od trenutno najboljih alata temeljenih na klasiˇcnom raˇcunarstvu, no zaostaje ne nadmašuje najbolje alate temeljene na dubokom ucˇenju. Med¯utim, zbog ogranicˇenja u vidu prostora za pohranu podataka i vremena treniranja modela, detaljnija analiza tek treba biti napravljena za više bakterijskih uzoraka i razliˇcite arhitekture mreže.Goal of this work was to implement a deep learning model in order to increase the accuracy of the assembled genome. This model is based on a residual network architecture and achieves higher accuracy than classical state-of-the-art tools, but falls behind the best deep-learning tools available. However, due to limitations in terms of storage and time for training the model, further analysis has to be conducted for different bacteria datasets and network architectures

DNA Sequence Polishing Using Deep learning

Cilj ovog rada bio je implementirati model temeljen na dubokom uˇcenju koji bi pove´cao toˇcnost sastavljenog genoma. Konstruirani model temelji se na arhitekturi rezidualne neuronske mreže te postiže toˇcnost ve´cu od trenutno najboljih alata temeljenih na klasiˇcnom raˇcunarstvu, no zaostaje ne nadmašuje najbolje alate temeljene na dubokom ucˇenju. Med¯utim, zbog ogranicˇenja u vidu prostora za pohranu podataka i vremena treniranja modela, detaljnija analiza tek treba biti napravljena za više bakterijskih uzoraka i razliˇcite arhitekture mreže.Goal of this work was to implement a deep learning model in order to increase the accuracy of the assembled genome. This model is based on a residual network architecture and achieves higher accuracy than classical state-of-the-art tools, but falls behind the best deep-learning tools available. However, due to limitations in terms of storage and time for training the model, further analysis has to be conducted for different bacteria datasets and network architectures

Femtosecond laser-induced desorption of hydrogen molecules from Ru(0001): A systematic study based on machine-learned potentials

Femtosecond laser-induced dynamics of molecules on metal surfaces can be seamlessly simulated with all nuclear degrees of freedom using ab-initio molecular dynamics with electronic friction (AIMDEF) and stochastic forces which are a function of a time-dependent electronic temperature. This has recently been demonstrated for hot-electron mediated desorption of hydrogen molecules from a Ru(0001) surface covered with H and D atoms [Juaristi et al., Phys. Rev. B 2017, 95, 125439]. Unfortunately, AIMDEF simulations come with a very large computational expense that severely limits statistics and propagation times. To keep ab-initio accuracy and allow for better statistical sampling, we have developed a neural network interatomic potential of hydrogen on the Ru(0001) surface based on data from ab-initio molecular dynamics simulations of recombinative desorption. Using this potential we simulated femtosecond laser-induced recombinative desorption using varying unit cells, coverages, laser fluences, and isotope ratios with reliable statistics. As a result, we can systematically study a wide range of these parameters and follow dynamics over longer times than hitherto possible, demonstrating that our methodology is a promising way to realistically simulate femtosecond laser-induced dynamics of molecules on metals. Moreover, we show that previously used cell sizes and propagation times were too small to obtain converged results

Femtosecond laser-induced desorption of hydrogen molecules from Ru(0001): A systematic study based on machine-learned potentials

Published as part of The Journal of Physical Chemistry C virtual special issue “Hot Electrons in Catalysis”.Femtosecond laser-induced dynamics of molecules on metal surfaces can be seamlessly simulated with all nuclear degrees of freedom using ab initio molecular dynamics with electronic friction (AIMDEF) and stochastic forces, which are a function of a time-dependent electronic temperature. This has recently been demonstrated for hot-electron-mediated desorption of hydrogen molecules from a Ru(0001) surface covered with H and D atoms [Juaristi, J. I. Phys. Rev. B 2017, 95, 125439]. Unfortunately, AIMDEF simulations come with a very large computational expense that severely limits statistics and propagation times. To keep ab initio accuracy and allow for better statistical sampling, we have developed a neural network interatomic potential of hydrogen on the Ru(0001) surface based on data from ab initio molecular dynamics simulations of recombinative desorption. Using this potential, we simulated femtosecond laser-induced recombinative desorption using varying unit cells, coverages, laser fluences, and isotope ratios with reliable statistics. As a result, we can systematically study a wide range of these parameters and follow dynamics over longer times than hitherto possible, demonstrating that our methodology is a promising way to realistically simulate femtosecond laser-induced dynamics of molecules on metals. Moreover, we show that previously used cell sizes and propagation times were too small to obtain converged results.This work has been supported in part by Croatian Science Foundation under the project UIP-2020-02-5675. J.I.J. and M.A. acknowledge financial support by the Gobierno Vasco-UPV/EHU [Project No. IT1569-22] and by the Spanish MCIN/AEI/10.13039/501100011033 [Grant No. PID2019-107396GB-I00]. P.S. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG), through project Sa 547-18.Peer reviewe