Tag: computational materials science

Small Data Materials Design with Machine Learning: When the Average Model Knows Best

Authors:  Danny E. P. Vanpoucke, Onno S. J. van Knippenberg, Ko Hermans, Katrien V. Bernaerts, and Siamak Mehrkanoon
Journal: Journal of Applied Physics 128, 054901 (2020)
doi: 10.1063/5.0012285
IF(2019): 2.286
export: bibtex
pdf: <JApplPhys>   (Open Access)
github: <Amadeus>

 

Vulcanoplot
Graphical Abstract: Correlation plot of the RMSE of the validation set and the intercept value for linear model instances trained on 1000 subsets of a 25 point data set. The distribution of the correlation data is indicated by the black curve.

Abstract

Machine Learning is quickly becoming an important tool in modern materials design. Where many of its successes are rooted in huge data sets, the most common applications in academic and industrial materials design deal with data sets of at best a few tens of data points. Harnessing the power of Machine Learning in this context is therefore of considerable importance. In this work, we investigate the intricacies introduced by these small data sets. We show that individual data points introduce a significant chance factor in both model training and quality measurement. This chance factor can be mitigated by the introduction of an ensemble-averaged model. This model presents the highest accuracy while at the same time it is robust with regard to changing data set size. Furthermore, as only a single model instance needs to be stored and evaluated, it provides a highly efficient model for prediction purposes, ideally suited for the practical materials scientist.

Partitioning the vibrational spectrum: Fingerprinting defects in solids

Authors:  Danny E. P. Vanpoucke
Journal: Computational Materials Science 181, 109736 (2020)
doi: 10.1016/j.commatsci.2020.109736
IF(2019): 2.863
export: bibtex
pdf: <ComputMaterSci>   (Open Access)
github: <Hive-toolbox>

 

Graphical abstract Computational Materials Science 181, 109736 (2020)
Graphical Abstract: Finger printing defects in diamond through the creation of the vibrational spectrum of a defect.

Abstract

Vibrational spectroscopy techniques are some of the most-used tools for materials
characterization. Their simulation is therefore of significant interest, but commonly
performed using low cost approximate computational methods, such as force-fields.
Highly accurate quantum-mechanical methods, on the other hand are generally only used
in the context of molecules or small unit cell solids. For extended solid systems,
such as defects, the computational cost of plane wave based quantum mechanical simulations
remains prohibitive for routine calculations. In this work, we present a computational scheme
for isolating the vibrational spectrum of a defect in a solid. By quantifying the defect character
of the atom-projected vibrational spectra, the contributing atoms are identified and the strength
of their contribution determined. This method could be used to systematically improve phonon
fragment calculations. More interestingly, using the atom-projected vibrational spectra of the
defect atoms directly, it is possible to obtain a well-converged defect spectrum at lower
computational cost, which also incorporates the host-lattice interactions. Using diamond as
the host material, four point-defect test cases, each presenting a distinctly different
vibrational behaviour, are considered: a heavy substitutional dopant (Eu), two intrinsic
point-defects (neutral vacancy and split interstitial), and the negatively charged N-vacancy
center. The heavy dopant and split interstitial present localized modes at low and high
frequencies, respectively, showing little overlap with the host spectrum. In contrast, the
neutral vacancy and the N-vacancy center show a broad contribution to the upper spectral range
of the host spectrum, making them challenging to extract. Independent of the vibrational behaviour,
the main atoms contributing to the defect spectrum can be clearly identified. Recombination of
their atom-projected spectra results in the isolated spectrum of the point-defect.

UV-Curable Biobased Polyacrylates Based on a Multifunctional 2 Monomer Derived from Furfural

Authors: Jules Stouten, Danny E. P. Vanpoucke, Guy Van Assche, and Katrien V. Bernaerts
Journal: Macromolecules 53(4), 1388-1404 (2020)
doi: 10.1021/acs.macromol.9b02659
IF(2019): 5.918
export: bibtex
pdf: <Macromolecules> (Open Access)

 

 

Grapgical abstract ACS Macromolecules 2020
Graphical Abstract: The formation of biobased polyacrylates.

Abstract

The controlled polymerization of a new biobased monomer, 4-oxocyclopent-2-en-1-yl acrylate (4CPA), was
established via reversible addition−fragmentation chain transfer (RAFT) (co)polymerization to yield polymers bearing pendent cyclopentenone units. 4CPA contains two reactive functionalities, namely, a vinyl group and an internal double bond, and is an unsymmetrical monomer. Therefore, competition between the internal double bond and the vinyl group eventually leads to gel formation. With RAFT polymerization, when aiming for a degree of polymerization (DP) of 100, maximum 4CPA conversions of the vinyl group between 19.0 and 45.2% were obtained without gel formation or extensive broadening of the dispersity. When the same conditions were applied in the copolymerization of 4CPA with lauryl acrylate (LA), methyl acrylate (MA), and isobornyl acrylate, 4CPA conversions of the vinyl group between 63 and 95% were reached. The additional functionality of 4CPA in copolymers was demonstrated by model studies with 4-oxocyclopent-2-en-1-yl acetate (1), which readily dimerized under UV light via [2 + 2] photocyclodimerization. First-principles quantum mechanical simulations supported the experimental observations made in NMR. Based on the calculated energetics and chemical shifts, a mixture of head-to-head and head-to-tail dimers of (1) were identified. Using the dimerization mechanism, solvent-cast LA and MA copolymers containing 30 mol % 4CPA were cross-linked under UV light to obtain thin films. The cross-linked films were characterized by dynamic scanning calorimetry, dynamic mechanical analysis, IR, and swelling experiments. This is the first case where 4CPA is described as a monomer for functional biobased polymers that can undergo additional UV curing via photodimerization.

Influence of diamond crystal orientation on the interaction with biological matter

Authors: Viraj Damle, Kaiqi Wu, Oreste De Luca, Natalia Ortí-Casañ, Neda Norouzi, Aryan Morita, Joop de Vries, Hans Kaper, Inge Zuhorn, Ulrich Eisel, Danny E.P. Vanpoucke, Petra Rudolf, and Romana Schirhagl,
Journal: Carbon 162, 1-12 (2020)
doi: 10.1016/j.carbon.2020.01.115
IF(2019): 8.821
export: bibtex
pdf: <Carbon> (Open Access)

 

Graphical Abstract Carbon paper with Romana
Graphical Abstract: The preferential adsorption of biological matter on oriented diamond surfaces.

Abstract

Diamond has been a popular material for a variety of biological applications due to its favorable chemical, optical, mechanical and biocompatible properties. While the lattice orientation of crystalline material is known to alter the interaction between solids and biological materials, the effect of diamond’s crystal orientation on biological applications is completely unknown. Here, we experimentally evaluate the influence of the crystal orientation by investigating the interaction between the <100>, <110> and <111> surfaces of the single crystal diamond with biomolecules, cell culture medium, mammalian cells and bacteria. We show that the crystal orientation significantly alters these biological interactions. Most surprising is the two orders of magnitude difference in the number of bacteria adhering on <111> surface compared to <100> surface when both the surfaces were maintained under the same condition. We also observe differences in how small biomolecules attach to the surfaces. Neurons or HeLa cells on the other hand do not have clear preferences for either of the surfaces. To explain the observed differences, we theoretically estimated the surface charge for these three low index diamond surfaces and followed by the surface composition analysis using x-ray photoelectron spectroscopy (XPS). We conclude that the differences in negative surface charge, atomic composition and functional groups of the different surface orientations lead to significant variations in how the single crystal diamond surface interacts with the studied biological entities.

Investigation of structural, electronic and magnetic properties of breathing metal–organic framework MIL-47(Mn): a first principles approach

Authors: Mohammadreza Hosseini, Danny E. P. Vanpoucke, Paolo Giannozzi, Masoud Berahman  and Nasser Hadipour
Journal: RSC Adv. 10, 4786-4794 (2020)
doi: 10.1039/C9RA09196C
IF(2019): 3.119
export: bibtex
pdf: <RSC Adv.> (Open Access)

 

Graphical abstract: MIL-47(Mn) paper
Graphical Abstract: The breathing MIL-47(Mn) Metal-Organic Framework. Upon breathing, the electronic structure of this MOF undergoes a transition from an anti-ferromagnetic semiconductor, to a ferromagnetic semi-metal.

Abstract

The structural, electronic and magnetic properties of the MIL-47(Mn) metal–organic framework are investigated using first principles calculations. We find that the large-pore structure is the ground state of this material. We show that upon transition from the large-pore to the narrow-pore structure, the magnetic ground-state configuration changes from antiferromagnetic to ferromagnetic, consistent with the computed values of the intra-chain coupling constant. Furthermore, the antiferromagnetic and ferromagnetic configuration phases have intrinsically different electronic behavior: the former is semiconducting, the latter is a metal or half-metal. The change of electronic properties during breathing posits MIL-47(Mn) as a good candidate for sensing and other applications. Our calculated electronic band structure for MIL-47(Mn) presents a combination of flat dispersionless and strongly dispersive regions in the valence and conduction bands, indicative of quasi-1D electronic behavior. The spin coupling constants are obtained by mapping the total energies onto a spin Hamiltonian. The inter-chain coupling is found to be at least one order of magnitude smaller than the intra-chain coupling for both large and narrow pores. Interestingly, the intra-chain coupling changes sign and becomes five times stronger going from the large pore to the narrow pore structure. As such MIL-47(Mn) could provide unique opportunities for tunable low-dimensional magnetism in transition metal oxide systems.

Parallel Python?

As part of my machine learning research at AMIBM, I recently ran into the following challenge: “Is it possible to do parallel computation using python.” It sent me on a rather long and arduous journey, with the final answer being something like: “very reluctantly“.

Python was designed with one specific goal in mind; make it easy to implement small test programs to see if an idea is worth pursuing. This gave rise to a scripting language with a lot of flexibility, but also with significant limitations, most of which the “intended” user would never meet. However, as a consequence of its success, many are using it going far beyond this original scope (yours truly as well 🙂 ).

Python offers various libraries to parallelize your scripts…most of them wrappers adding minor additional functionality. However, digging down to the bottom one generally ends up at one of the following two libraries: the threading module and the multiprocessing module.

Of course, as with many things python, there is a huge amount of tutorials available with many of great quality.

import threading

Programmers experienced in a programming language such as C/C++, Pascal, or Fortran, may be familiar with the concept of multi-threading. With multi-threading, a CPU allows a program to distribute its work over multiple program-threads which can be performed in parallel by the different cores of the CPU (or while a core is idle, e.g., since a thread is waiting for data to be fetched).  One of the most famous API’s for writing multi-threaded applications is OpenMP. In the past I used it to parallelize my Hirshfeld-I implementation and the phonon-module of HIVE.

For Python, there is no implementation of the OpenMP API, instead there is the threading module. This provides access to the creation of multiple threads, each able to perform their own tasks while sharing data-objects. Unfortunately, python has also the Global Interpreter Lock, GIL for short, which allows only a single thread to access the interpreter at a time. This effectively reduces thread-based parallelization to a complex way of running a code in a serial way.

For more information on “multi-threading” in python, you can look into this tutorial.

import multiprocessing

In addition to the threading module, there is also the multiprocessing module. This module side-steps the GIL by creating multiple processes, each having its own interpreter. This however comes at a cost. Firstly, there is a significant computational cost starting the different processes. Secondly, objects are not shared between processes, so additional work is needed to collect and share data.

Using the “Pool” class, things are somewhat simplified, as can be seen in the code-fragment below.  With the pool class one creates a set of threads/processes available for your program. Then through the function apply_async function it is possible to run processes in parallel. (Note that you need to use the “async” version of the function, as otherwise you end up with running things serial …again)

  1. import multiprocessing as mp
  2.  
  3. def doOneRun(id:int): #trivial function to run in parallel
  4. return id**3
  5.  
  6.  
  7.  
  8. num_workers=10 #number of processes
  9. NRuns=1000 #number of runs of the function doOneRun
  10.  
  11. pool=mp.Pool(processes=num_workers) # create a pool of processes
  12. drones=[pool.apply_async(doOneRun, args=nr) for nr in range(NRuns)] #and run things in parallel
  13.  
  14. for drone in drones: #and collect the data
  15. Results.collectData(drone.get()) #Results.collectData is a function you write to recombine the separate results into a single result and is not given here.
  16.  
  17. pool.close() #close the pool...no new tasks can be run on any of the processes
  18. pool.join() #collapse all threads back into the main thread

 

how many cores does my computer have?

If you are used to HPC applications, you always want to get as much out of your machine as possible. With regard to parallelization this often means making sure no CPU cycle is left unused. In the example above we manually selected the number of processes to spawn. However, would it not be nice if the program itself could just set this value to be equal to the number of physical cores accessible?

Python has a large number of functions claiming to do just that. A few of them are given below.

  •  multiprocessing.cpu_count(): returns the number of logical cores it can find. So if you have a modern machine with hyper-threading technology, this will return a multiple of the number of physical cores (and you will be over-subscribing your CPU.
  • os.cpu_count(): same as multiprocessing.cpu_count().
  • psutil.cpu_count(logical=False): This implementation gives the same default behavior, however, the parameter logical allows for this function to return the correct number of cores in a single CPU. Indeed a single CPU. HPC architectures which contain multiples CPUs per node will again return an incorrect number, as the implementation makes use of a python “set”, and as such doesn’t increment for the same index core on a different CPU.

In conclusion, there seems to be no simple way to obtain the correct number of physical cores using python, and one is forced to provide this number manually. (If you do have knowledge of such a function which works in both windows and unix environments and both desktop and HPC architectures feel free to let me know in the comments.)

All in all, it is technically possible to run code in parallel using python, but you have to deal with a lot of python quirks such as GIL.

Casting Keynotes: The Virtual Lab

Last Tuesday? I had the pleasure of competing in the casting keynotes competition of the TEDx UHasselt chapter. An evening filled with interesting talks on subjects ranging from the FAIR principles of open-data (by Liebet Peeters)  to the duty not stay silent in the face of “bad ideas” and leading a life of purpose. An interesting presentation was the one by Ann Bessemans on visual prosody to improve reading skills in young children as well as reading experience, more specifically the transfer of non-literal-content, for non-native speakers. There was also time for some humor, with the dangerous life of Tim Biesmans, who suffers from peanut-allergies. For him, death lurks around every corner, even in a first-date’s kiss. During my talk, I traced the evolution of computational research as the third paradigm of scientific discovery, showing you can find computational research in every field, and why it is evolving at its break-neck speed.

During the event, both the public and a jury voted on the best presentation, which would then have to present at the TEDx UHasselt in 2020.

And the Winner is …drum roll… Danny Vanpoucke!

So this story will continue during the 2020 TEDx event at UHasselt, and I hope to see you there 🙂

Casting Keynotes

top: Full action shots of my presentation. Moore’s Law as driving force behind computational research, and pondering the meaning of Artificial Intelligence. Bottom: Yes, I won 🙂

 

Universiteit Van Vlaanderen

A bit over 1 month ago, I told you about my adventure at the film studio of “de Universiteit Van Vlaanderen“. Today is the day the movie is officially released. You can find it at the website of de Universiteit Van Vlaanderen: Video. The video is in Dutch as this is a science-communication platform aimed at the local population, presenting the expertise available at our local universities.

 

In addition to this video, I was asked by Knack magazine to write a piece on the topic presented. As computational research is my central business I wrote a piece on the subject introducing the general public to the topic. The piece can be read here (in Dutch).

And of course, before I forget, this weekend there was also the half-yearly daylight saving exercise with our clocks.[and in Dutch]

 

Can Europium Atoms form Luminescent Centres in Diamond: A combined Theoretical-Experimental Study

Authors: Danny E. P. Vanpoucke, Shannon S. Nicley, Jorne Raymakers, Wouter Maes, and Ken Haenen
Journal: Diam. Relat. Mater 94, 233-241 (2019)
doi: 10.1016/j.diamond.2019.02.024
IF(2019): 2.650
export: bibtex
pdf: <DiamRelatMater>

 

Spin polarization around the various Eu-defect models in diamond. Blue and red represent the up and down spin channels respectively
Graphical Abstract: Spin polarization around the various Eu-defect models in diamond. Blue and red represent the up and down spin channels respectively.

Abstract

The incorporation of Eu into the diamond lattice is investigated in a combined theoretical-experimental study. The large size of the Eu ion induces a strain on the host lattice, which is minimal for the Eu-vacancy complex. The oxidation state of Eu is calculated to be 3+ for all defect models considered. In contrast, the total charge of the defect-complexes is shown to be negative: -1.5 to -2.3 electron. Hybrid-functional electronic-band-structures show the luminescence of the Eu defect to be strongly dependent on the local defect geometry. The 4-coordinated Eu substitutional dopant is the most promising candidate to present the typical Eu3+ luminescence, while the 6-coordinated Eu-vacancy complex is expected not to present any luminescent behaviour. Preliminary experimental results on the treatment of diamond films with Eu-containing precursor indicate the possible incorporation of Eu into diamond films treated by drop-casting. Changes in the PL spectrum, with the main luminescent peak shifting from approximately 614 nm to 611 nm after the growth plasma exposure, and the appearance of a shoulder peak at 625 nm indicate the potential incorporation. Drop-casting treatment with an electronegative polymer material was shown not to be necessary to observe the Eu signature following the plasma exposure, and increased the background
luminescence.

Universiteit Van Vlaanderen: Will we be able to design new materials using our smartphone in the future?

Yesterday, I had the pleasure of giving a lecture for the Universiteit van Vlaanderen, a science communication platform where Flemish academics are asked to answer “a question related to their research“. This question is aimed to be highly clickable and very much simplified. The lecture on the other hand is aimed at a general lay public.

I build my lecture around the topic of materials simulations at the atomic scale. This task ended up being rather challenging, as my computational research has very little direct overlap with the everyday life of the average person. I deal with supercomputers (which these days tend to be bench-marked in terms of smartphone power) and the quantum mechanical simulation of materials at the atomic scale, two other topics which may ring a bell…but only as abstract topics people may have heard of.

Therefor, I crafted a story taking people on a fast ride down the rabbit hole of my work. Starting from the almost divine power of the computational materials scientist over his theoretical sample, over the reality of nano-scale materials in our day-to-day lives, past the relative size of atoms and through the game nature of simulations and the salvation of computational research by grace of Moore’s Law…to the conclusion that in 25 years, we may be designing the next generation of CPU materials on our smartphone instead of a TIER-1 supercomputer. …did I say we went down the rabbit hole?

The television experience itself was very exhilarating for me. Although my actual lecture took only 15 minutes, the entire event took almost a full day. Starting with preparations and a trial run in the afternoon (for me and my 4 colleagues) followed by make-up (to make me look pretty on television 🙂 … or just to reduce my reflectance). In the evening we had a group diner meeting the people who would be in charge of the technical aspects and entertainment of the public. And then it was 19h30. Tensions started to grow. The public entered the studio, and the show was ready to start. Before each lecture, there was a short interview to test sound and light, and introduce us to the public. As the middle presenter, I had the comfortable position not to be the first, so I could get an idea of how things went for my colleagues, and not to be the last, which can really be destructive on your nerves.

At 21h00, I was up…

and down the rabbit hole we went. 

 

 

Full periodic table, with all elements presented with their relative size (if known)

Full periodic table, with all elements presented with their relative size (if known) created for the Universiteit van Vlaanderen lecture.