0000000001299396
AUTHOR
Joakim Linja
showing 9 related works from this author
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?
2020
Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dat…
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiol...
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
<div> <div> <div> <p>We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) -based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster, and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and opti…
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
2021
Machine learning (ML) force fields are one of the most common applications of ML in nanoscience. However, commonly these methods are trained on potential energies of atomic systems and force vectors are omitted. Here we present a ML framework, which tackles the greatest difficulty on using forces in ML: accurate prediction of force direction. We use the idea of Minimal Learning Machine to device a method which can adapt to the orientation of an atomic environment to estimate the directions of force vectors. The method was tested with linear alkane molecules. peerReviewed
Asymmetrical waveguide coupling by a tilted metal nanocone
2017
Kasvava uusiutuvan energian tarve ajaa tiedeyhteisöä etsimään uusia vihreitä energian lähteitä. Osana tätä tavoitetta, WISC-projekti pyrkii yhdistämään infrapunaa keräävän aurinkopaneelin normaaliin ikkunaan. Tässä tutkimuksessa me tutkimme sisääntulevan valon kytkeytymistä kallistuneesta kultananokartiosta laattamaisen aaltojohteen ohjattuihin aaltojohdemoodeihin; tavoitteena on esittää epäsymmetristä aaltojohteeseen kytkeytymistä. Tutkimus suoritettiin simuloimalla Comsolissa käyttäen kolmiulotteista äärellisten elementtien menetelmää. Ulospäin suuntautuva aaltojohteeseen sironneen valon energiavuo mitattiin 2.1 aallonpituuden päässä nanokartiosta. Tulokset viittaavat kahden samanaikaisen…
Feature selection for distance-based regression: An umbrella review and a one-shot wrapper
2023
Feature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for the recently introduced distance-based supervised machine learning model is considered for regression problems. The study is contextualized by first providing an umbrella review (review of reviews) of recent development in the research field. We then propose a saliency-based one-shot wrapper algorithm for FS, which is called MAS-FS. The algorithm is compared with a set of other popular FS algorithms, using a versatile set of simulated and benchmark datasets. Finally, experimental results underline the usefulness of FS for regression, confirm…
Au38Q MBTR-K3
2020
Purpose The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. Background The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating using the MBTR descriptor by Himanen et al. as presented in paper DScribe: Library of descriptors for machine learning in materials science. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe version…
Au38Q MBTR-K3
2020
Purpose The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. Background The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating using the MBTR descriptor by Himanen et al. as presented in paper DScribe: Library of descriptors for machine learning in materials science. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe vers…
Advancing nanomaterials design using novel machine learning methods
2023
Datan käsittely on mullistunut koneoppimismenetelmien yleistymisen myötä. Koneoppimiselle löydetään jatkuvasti uusia sovelluskohteita ja uusia sovellustapoja. Yksi näistä sovelluskohteista löytyy nanotieteen puolelta. Nanotiede on alati laajeneva tieteenala, jonka vaikutuksia löytää nykyään melkein jokaisesta elämän osa-alueesta, kuten lääketieteestä, materiaalisuunnittelusta ja kuluttajatuotteista. Nanotieteen kokeellinen tutkimus on kuitenkin kallista, mutta tätä voidaan lieventää laskennallisen tieteen keinoja hyödyntäen. Laskennallisen tieteen keinot nanotieteen saralla ovat kuitenkin itsessään raskaita ja aikaavieviä, johtuen tutkimuksen vaatimasta tarkkuustasosta. Laskennallisen tiete…