Summer 2021 projects in SIN www.aalto.fi/physics-sin
Project – Software workflow for surface electronic structure predictions
Background – In computational science it is generally more common to use various codes for different tasks and workflow, rather than a large single package, with numerous internal plugins. This way enables more dynamic development and possibility to optimize every package for maximal performance. On the other hand, it puts higher pressure on developers and very often also users to link the data- workflow, so it would work together with different pre- or post-calculations. In this project you can learn how to create a new link between a major computation package and a post-processing tool in scientific computation. The major computation software in this case
will be Siesta, which is a widespread package for calculation of the atomic and electronic structures of molecules, surfaces and 2D or 3D materials. The post- processing tool that you will put your hands-on is a PP-STM code. The
code enables simulation of a commonly used surface technique – Scanning Tunneling Microscopy (STM) – from four electronic structure calculating codes. STM can obtain information about atomic and electronic structure of the top-most layer of the measured sample with the atomic resolution. But to understand its results computer simulations are frequently used. Your task will be to write a reading procedure in python for the PP-STM code, using a small SIESTA calculations and prepared examples. To incorporate another code spreading the possibilities of both codes will be fruitful for SIESTA user as well as PP-STM users.
Team – the project is part of development of PP-STM code (Dr. Ondřej Krejčí). Small DFT calculations through SIESTA will be conducted by Dr. Orlando J. Silveira.
Learn – basic ideas behind Scanning Tunneling microscopy and its simulation.
Essentials of electronic structure of materials calculations and Python programming.
Task – create and test a workflow and a reading procedure to get electronic structure from SIESTA package to PP-STM simulation code.
Geometry:
LUMO
HOMO
PP-STM simulations:
Electronic Structure
calculations PP-STM
model
Project – Design of novel two dimensional ferromagnetic heterostructures using density functional theory
Background – Van der Waals (vdW) heterostructures have recently gained attention in the development of artificial materials by combining two or more materials with different properties, and the rational design of new heterostructures allows the development of new quantum phases that have not been observed yet in naturally occurring materials. Although experiments are paramount to describe the properties of new materials, frequently they are very costly and/or have a large time demand (or sometimes are even impossible to be realized), and that is where computational simulations play an important role. In this project, you can learn the basic foundations of the density functional theory (DFT), which is a methodology widely used to model computationally the electronic structure of molecules and crystals, and have the opportunity to investigate promising novel properties considering a relativistic approach. The project will focus mainly on the combination of ferromagnetism and
an ordered quantum fluid of electrons, which is realized by bringing together a metal trihalide, such as the CrX3 ( X = Br, Cl or I) with the transition metal dichalcogenide NbSe2 in its charge density wave (CDW) phase. Your task will be to perform large scale
DFT calculations on these CrX3/NbSe2 heterostructure using the software quantum espresso and obtain their electronic properties. Further analyses of topological properties will be realized with post processing tools such as the Wannier90 and WannierTools packages.
Team – the project is part of our 2D quantum materials modelling project (Dr.
Orlando Silveira), also linking to local experimental STM measurements (Dr.
Shawulienu Kezilebieke).
Learn – basic ideas behind the design of new properties by bringing together different two-dimensional materials
Task – perform large scale DFT calculations to get the electronic structure of heterostructures and work with post processing tools for investigation of topological properties
Project – Towards chemical identification of a single atom
Background – the holy grail of high-resolution microscopy is to recognize single atoms in real space, identify the element and without damaging the measured sample. Various microscopy techniques are able to fulfill two of three above aspects, but achieving all of them has only been possible in very carefully prepared situations, without any generalization as yet. One of the most powerful family of experimental techniques – Scanning Probe Microscopy (SPM) – can visualize single atoms and also recognize the electrostatic field around the atoms.
However, to identify the measured sample, help from computer simulations based on quantum physical approaches is usually necessary. A new approach to overcome these costly calculations is to work with databases and big data, and to compare the experimental measurements with already precalculated data or their (machine learned) interpolation. In this project, we would like to push SPM, and in particular Kelvin Probe Force Microscopy (KPFM), towards recognition of chemical elements. The investigator will help us through the creation of an engine for restoring the electrostatic field around small molecules from already precalculated density matrices. The restored electrostatic fields can than work as a database for comparison with experimental data.
Team – the project is directly related with our theoretical study of KPFM imaging (Dr. Ondřej Krejčí), molecular database construction (Dr. Fedor Urtiev) and molecular imaging in SPM (Niko Oinonen,). The above-mentioned studies are part of a cooperation between our theoretical group and the experimental atomic scale physics group in the department.
Learn – basic ideas behind quantum chemical and density functional theory calculations. Creation of tools for big data and programming in computationally powerful languages (C/Fortran/GPU programming).
Task – recover the electrostatic field around small molecules from database using given density matrices.
Project – Atomic assembly of designer quantum materials
Background – Constructing novel materials with engineered electronic properties is becoming reality. These designer materials are characterized by engineered electronic responses with atomically precise geometries, lattice symmetries and controlled interactions. Such ingredients can result in ultimately controllable materials that have large, robust and quick responses to small stimuli (with applications in quantum technologies, flexible electronics, high-selectivity and high- sensitivity sensors, and optoelectronic components). Longer term, the biggest impact is expected through a profound change in the way we view materials and what can be achieved through a designer materials approach.
In this project, we will build on this opportunity by introducing a designer materials approach based on a close interplay between atomistic modelling, machine learning (ML) and experiments. Nanofabrication will be facilitated by manipulation of atomic species using Scanning Probe Microscopy (SPM) and this requires the development of a deep learning approach to account for the variability and uncertainty in measurements.
Team – the project will be supported by our machine learning in SPM team (Niko Oinonen, Fabio Priante, Dr. I-Ju Chen) and deep learning groups in computer science (Prof. Alexander Ilin). It will also link to local experts in experimental SPM and quantum materials (Prof. Peter Liljeroth).
Learn – basic ideas behind Scanning Probe Microscopy and its simulation. Deep learning methodologies and application.
Task – develop a reinforcement learning approach for design of a simple quantum lattice using SPM atomic manipulation.
Project –GPU acceleration of STM simulations
Background – one of the main difficulties of every code, is not to build the code itself, but also to make it as effective as possible and user-friendly. This is even more problematic in science due to difficulties with rapid development cycles and a niche market. In this project you will have possibility to learn and/or practice skills that can be later used in
science or industry – GPU programming. The code you will be working with – PP-STM – is a code for simulations of Scanning Tunneling Microscopy (STM), which is an important tool in surface science for recognizing surface or material structure. STM is also one of the key
techniques in emerging field of on-surface synthesis, through which you can compose otherwise inaccessible materials/molecules. On the other hand, STM data in most cases needs comparison with simulations to recognize the sample. The PP- STM code is a python code extended with C++ for heavy numeric computation on a CPU. But the simple structure of the code, computing on independent grid points allows it to be efficiently parallelized through a GPU. The GPU acceleration allows the code to do the same kind of calculations approximately 100x times faster. In these ways you can make the PP-STM much more effective.
Team – the project is part of development of PP-STM code (Dr. Ondřej Krejčí). The student can use help of other SIN group members experienced in GPU computing (Dr. Filippo Federici Canova).
Learn – programming in computationally powerful languages (C++/OpenCl or CUDA). Basics of STM principles and STM simulations. GitHub collaborative code repository.
Task – create a GPU branch of the PP-STM code.
Project – Computational AFM in solid-liquid interfaces
Background – Interfacial water characterization is a fundamental topic in surface science. It enables us to understand processes such as biomineralization and chemical reactions in aqueous environments as well as to tailor surface’s properties to specific technological needs.
Among the most promising materials for controlling
surface characteristics are 2D materials. Their primary application is in the generation of new electronic devices; a clear characterization of their solid-liquid interfaces is imperative for their use as lab-on-chip technologies, where accurate control along the surface is necessary. Recently, atomic force microscopy (AFM) has been used to access the solid-liquid interface of 2D materials in water. However, experimental data interpretation is always a challenge in solid-liquid interfaces, as spatial resolution is deteriorated by the movement of liquid and its interactions with the AFM tip. Computational methods can solve this problem by modeling these systems at the atomic level. Among these methods, molecular dynamics (MD) is one of the most versatile and efficient. In this project, your goal will be to perform calculations at the solid-liquid interface of three different 2D materials in water to accurately characterize the arrangement of the liquid molecules on the materials’
surfaces.
Team – Throughout the project, you will collaborate with a post-doc (Dr. Ygor M.
Jaques) and a PhD student (Yashasvi S. Ranawat). The data generated in the project will be added to the library of surfaces that our MERLIN project is currently developing.
Learn – In this project, you will learn standard MD methodology as well as more advanced methods based on free energy calculations. The MD engine that will be used is the LAMMPS code together with the PLUMED plugin.
Task – Implement a MD model for simulations of the 2D materials graphene, MoS2 and WSe2 interacting with water. Obtain force profiles from the simulations that can be directly compared with available experimental data.
