2024
Friday, April 26th, Llyod Building Viz Room, 3pm
Adapting Computational Materials Science to Exascales
Logan Ward (Argonne National Laboratory, US)
The simultaneous arrival of Exascale computing resources and major advances in Artificial Intelligence (AI) algorithms presents a wonderful opportunity. Scientists can both acquire new data and learn from it faster than with smaller computers or with less sophisticated empirical models. In this talk, we will discuss the intersection of AI and supercomputing with an emphasis on how they are natural fits for each other. The talk will cover how AI can augment human expertise in the design and evaluation of materials for both energy and CO2 storage materials; and how AI can become even more integrated into computational materials science.
Friday, April 19th, Llyod Building Viz Room, 3pm
Tuning the magnetic interactions in van der Waals Fe3GeTe2 heterostructures
Soumyajyoti Haldar (Institute of Theoretical Physics and Astrophysics, University of Kiel, 24098 Kiel, Germany)
We investigate the impact of mechanical strain, stacking order, and external electric fields on the magnetic interactions of a Fe3GeTe2 monolayer deposited on Germanene using density functional theory [1]. We find that an electric field of E = ±0.5 V/Å applied perpendicular to the Fe3GeTe2/germanene heterostructure leads to significant changes of the exchange constants. We show that the Dzyaloshinskii-Moriya interaction (DMI) in Fe3GeTe2/Germanene is mainly dominated by the nearest neighbours. Furthermore, we demonstrate that the DMI is highly tunable by strain, stacking, and electric field, leading to a large DMI comparable to that of ferromagnetic/heavy metal interfaces. The geometrical change and hybridization effect explain the origin of the high tunability of the DMI at the interface. The magneto crystalline anisotropy energy (MAE) can also be drastically changed by the application of compressive or tensile strain. The tunability of DMI and MAE by using strain allows the occurrence of nanoscale skyrmions [2]. Another major challenge for magnetic skyrmions in atomically thin vdW materials is reliable skyrmion detection. Using rigorous first-principles calculations, we show that all-electrical detection of skyrmions in 2D vdW magnets is feasible via scanning tunnelling microscopy and in planar tunnel junctions with straightforward implementation into device architectures. We use the nonequilibrium Green’s function method for quantum transport in planar junctions, including self-energy due to electrodes and working conditions, going beyond the standard Tersoff- Hamann approximation. An extremely large non-collinear magnetoresistance (NCMR) is observed for nanoscale skyrmions in a vdW tunnel junction based on graphite/Fe3GeTe2/germanene/graphite. The NCMR can be orders of magnitude higher than that reported for conventional transition-metal interfaces. We trace the origin of the NCMR to spin- mixing between spin-up and -down states of pz and dz2 character at the surface atoms and the orbital matching effect at the interface [3].
[1] D. Li, S. Haldar, T. Drevelow, S. Heinze, Phys. Rev. B 107, 104428 (2023). [2] D. Li, S. Haldar, S. Heinze, Nano Lett. 22, 7706 (2022).[3] D. Li, S. Haldar, S. Heinze, Nano Lett. 24, 2496 (2024).
Friday, April 5th, Llyod Building Viz Room, 3pm
Matilda Sipilä (1), Farrokh Mehryary (2), Sampo Pyysalo (2), Filip Ginter (2), and Milica Todorović (1)
1 Department of Mechanical and Materials Engineering, University of Turku; 2 Department of Computing, University of Turku
Scientific text is a promising source of data in materials science, and there is ongoing research on how to utilise textual data in materials discovery. In addition to the more established approaches like named entity recognition or dictionary-based methods, new machine learning tools such as question answering (QA) are becoming available. The advantages of this method are that it is easy to scale and that it does not require manual text labeling or annotating work, but there may be some loss in precision compared to other methods.
We tested the performance of the QA method on the well-known task of information extraction. We extracted bandgap values of halide perovskite materials from scientific literature. Large language models (BERT models) were tuned towards a specific QA task and then used to select the correct answer for the question about materials properties. In comparison to more established methods, the QA method performed well, and we were able to extract correct information from text. This information can be used to map the space of materials properties and find promising new materials solutions. The potential in QA method lies in versatility, accessibility and scalability, since it is easy to use even for researchers with no previous knowledge of language technology and can be easily scaled to extract different materials and properties.
Friday, March 21st, Llyod Building Viz Room, 3pm
Magnon spin transport in multiferroic materials
Tianxiang Nan (Tsinghua University, China)
Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient electrical manipulation of magnon transport. In this talk, I will first review the recent advances in magnon spin transport in ferrimagnetic and antiferromagnetic materials. Then I will present our strategies to achieve the voltage control of magnon transport in multiferroic materials. We show the electric excitation and control of multiferroic magnon modes in a spin-source/multiferroic/ferromagnet structure. We demonstrate that the ferroelectric polarization can electrically modulate the magnon-mediated spin-orbit torque by controlling the non-collinear antiferromagnetic structure in multiferroic bismuth ferrite thin films with coupled antiferromagnetic and ferroelectric orders. In this multiferroic magnon torque device, magnon information is encoded to ferromagnetic bits by the magnon-mediated spin torque. By manipulating the two coupled non-volatile state variables—ferroelectric polarization and magnetization—we further present reconfigurable logic operations in a single device.
Friday, March 1st, Llyod Building Viz Room, 3pm
A quantum engine in the BEC-BCS crossover
Jennifer Koch (University of Kaiserslautern-Landau, Germany)
Heat engines convert thermal energy into mechanical work both in the classical and quantum regimes. However, quantum theory offers genuine nonclassical forms of energy, different from heat, which so far have not been exploited in cyclic engines to produce useful work. In this talk, I will discuss a recently realized quantum many-body engine fuelled by the energy difference between fermionic and bosonic ensembles of ultracold particles that follows from the Pauli exclusion principle [1]. We employ a harmonically trapped superfluid gas of Lithium-6 atoms close to a magnetic Feshbach resonance, which allows us to effectively change the quantum statistics from Bose-Einstein to Fermi-Dirac by tuning the gas between a Bose-Einstein condensate of bosonic molecules and a unitary Fermi gas (and back) through a magnetic field. The talk will focus on the quantum nature of such a Pauli engine. Additionally, I will present the pressure-volume diagram of the new kind of engine and show how the engine behaves after multiple cycles. Our findings establish quantum statistics as a useful thermodynamic resource for work production.
[1] J. Koch et al., Nature 621, 723 (2023)Friday, February 16th, Llyod Building Viz Room, 3pm
Vibrational thermal transport modeling: background and insights
Lucas Lindsay (Oak Ridge National Laboratory)
This talk will provide an introduction into first principles phonon thermal transport calculations highlighting a variety of insights and predictions that they have enabled. In this context, we will explore the underpinnings of density functional theory derived lattice dynamics and phonon Boltzmann transport models and how these translate into observables and functionalities of various semiconducting and insulating materials. More specifically, we will examine the roles of symmetry, chirality, and conservation conditions in determining inelastic neutron scattering spectra and lattice thermal transport.
L.L. acknowledges support from the U. S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.
Friday, February 9th, Llyod Building Viz Room, 3pm
Self-Driving Fluidic Labs: Accelerated Materials Discovery with Autonomous Experimentation in Flow
Milad Abolhasani (Department of Chemical & Biomolecular Engineering, North Carolina State University)
Accelerating the discovery of new advanced functional materials, as well as green and sustainable ways to synthesize and manufacture them, will have a profound impact on the worldwide challenges in renewable energy, sustainability, and healthcare. The current human-dependent paradigm of experimental research in chemical and materials sciences fails to identify technological solutions for worldwide challenges in a short timeframe. The time-, resource-, and labor-intensive nature of current experimental sciences necessitates the development and implementation of new strategies to accelerate the pace of discovery. Recent advances in reaction miniaturization, lab automation, and data science provide an exciting opportunity to reshape the discovery and manufacturing of new materials related to energy transition and sustainability. In this talk, I will present an overview of our recent efforts to establish a ‘self-driving fluidic lab (SDFL)’ through integration of flow chemistry, robotics, online characterization, and machine learning (ML) for autonomous discovery and manufacturing of emerging advanced functional materials with multi-step chemistries.1-5 I will discuss how modularization of different material synthesis and processing stages in tandem with a constantly evolving ML modeling and decision-making under uncertainty can enable a resource-efficient navigation through high dimensional experimental design spaces (>1020 possible experimental conditions). Example applications of SDFL for the autonomous synthesis of colloidal quantum dots will be presented to illustrate the potential of autonomous robotic experimentation in reducing synthetic route discovery timeframe from >10 years to a few months. Finally, I will present the unique reconfigurability aspect of flow chemistry to close the scale gap in materials research through facile switching from the reaction exploration/exploitation to smart manufacturing mode.
References.
[1] Abolhasani, M.; Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nature Synthesis, 2, 483–492, 2023.
[2] Volk, A. A.; Epps, R. W.; Yonemoto, D. T.; Masters, B. S.; Castellano, F. N.; Reyes, K. G.; Abolhasani, M. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nature Communications, 14 (1), 1403, 2023. [3] Volk, A. A.; Abolhasani, M. Autonomous flow reactors for discovery and invention. Trends in Chemistry, 3 (7), 519-522, 2021. [4] Delgado-Licona, F.; Abolhasani, M. Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery. Advanced Intelligent Systems, 5, 2200331, 2023.[5] Epps, R. W.; Bowen, M. S.; Volk, A. A.; Abdel-Latif, K.; Han, S.; Reyes, K. G.; Amassian, A.; Abolhasani, M. Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. Advanced Materials, 32 (30), 2001626, 2020.
Friday, February 2nd, 2024, Lloyd Building Viz Room, 3pm
Antiferromagnetic Tunnel Junctions for Spintronics
Evgeny Y. Tsymbal (Department of Physics and Astronomy, University of Nebraska, Lincoln, NE 68588, USA)
Antiferromagnetic (AFM) spintronics has emerged as a subfield of spintronics, where an AFM Néel vector is used as a state variable. Due to being robust against magnetic perturbations, producing no stray fields, and exhibiting ultrafast dynamics, antiferromagnets can serve as promising functional materials for spintronic applications. To realize this potential, efficient electric control and detection of the AFM Néel vector are required. This talk features fundamental properties of AFM tunnel junctions (AFMTJs) as spintronic devices where such electric control and detection can be realized [1]. We emphasize critical requirements for observing a large tunneling magnetoresistance (TMR) effect in AFMTJs with collinear [2] and noncollinear [3,4] AFM electrodes, such as crystallinity of the junction, AFM metals exhibiting momentum-dependent spin polarization [2,3], and/or AFM metals supporting Néel spin currents [5]. We further discuss the unique property of non-collinear antiferromagnets to sustain virtually 100% spin polarization [4], the possibility of magnetic tunnel junctions (MTJs) with a single ferromagnetic electrode [6], and spin torques that are capable of Néel vector switching [5]. Overall, AFMTJs have potential to become a new standard for spintronics providing larger magnetoresistive effects, few orders of magnitude faster switching speed, and much higher packing density than conventional MTJs.
- D.-F. Shao and E. Y. Tsymbal, Antiferromagnetic tunnel junctions for spintronics, arXiv: 2312.13507 (2023).
- D.-F. Shao, S.-H. Zhang, M. Li, C.-B. Eom, and E. Y. Tsymbal, Spin-neutral currents for spintronics, Nat. Commun. 12, 7061 (2021).
- J. Dong, X. Li, G. Gurung, M. Zhu, P. Zhang, F. Zheng, E. Y. Tsymbal, and J. Zhang, Tunneling magnetoresistance in noncollinear antiferromagnetic tunnel junctions, Phys. Rev. Lett. 128, 197201 (2022).
- G. Gurung, D.-F. Shao, and E. Y. Tsymbal, Extraordinary tunneling magnetoresistance in antiferromagnetic tunnel junctions with antiperovskite electrodes, arXiv:2306.03026 (2023).
- D.-F. Shao, Y.-Y. Jiang, J. Ding, S.-H. Zhang, Z.-A. Wang, R.-Ch. Xiao, G. Gurung, W. J. Lu, Y. P. Sun, and E. Y. Tsymbal, Néel spin currents in antiferromagnets, Phys. Rev. Lett. 130, 216702 (2023).
- K. Samanta, Y.-Y. Jiang, T. R. Paudel, D.-F. Shao, and E. Y. Tsymbal, Tunneling magnetoresistance in magnetic tunnel junctions with a single ferromagnetic electrode, arXiv:2310.02139 (2023).
Friday, January 26th, 2024, Lloyd Building Viz Room, 3pm
Machine learning interatomic potentials for phase change compounds
Marco Bernasconi (Department of Materials Science, University of Milano-Bicocca, Milano, Italy)
Phase change materials such as the flagship Ge2Sb2Te5 (GST) compound are exploited in key enabling technologies including non-volatile electronic memories and neuromorphic computing [1]. In the phase change electronic memory, the two digital states are encoded in the amorphous and crystalline phases of GST that feature a difference in the electrical resistivity by about three orders of magnitude. Readout of the memory consists of the measurement of the resistance at low bias while the set/reset processes consist of a reversible transformation between the crystalline and amorphous phases induced by Joule heating.
Atomistic simulations based on density functional theory (DFT) have provided useful insights on the structural and functional properties of phase change materials over the years. However, several key issues such as the effect of confinement and nanostructuring on the crystallization kinetics, just to name a few, are presently beyond the reach of DFT simulations. A route to overcome the limitations in system size and time scale and enlarge the scope of DFT methods is the exploitation of machine learning techniques trained on a DFT database to generate interatomic potentials for large scale molecular dynamics simulations. The first example of the application of such an approach to the study of phase change compounds dates to 2012 when we devised an interatomic potential for GeTe [2] within the neural network (NN) scheme proposed by Behler and Parrinello [3]. The NN potential was then used to address several issues such as the crystallization in bulk and nanowires, and the thermal conductivity and aging of the amorphous phase [4].
In this talk, we report on the generation of an interatomic potential for the Ge2Sb2Te5 compound within the NN framework implemented in the DeePMD-kit package [6]. The interatomic potential allows simulating several tens of thousands of atoms for tens of ns at a modest computational cost. The validation of the potential and its application to the study of the crystallization kinetics of the amorphous phase will be discussed.
[1] W. Zhang, R. Mazzarello, M. Wuttig, E. Ma, Nat. Rev. Mater. 4, 150 (2019) [2] G. C. Sosso, G. Miceli, S. Caravati, J. Behler, and M. Bernasconi, Phys. Rev. B 85, 174103 (2012). [3] J. Behler and M. Parrinello, Phys. Rev. Lett. 98, 146401 (2007). [4] G. C. Sosso and M. Bernasconi, MRS Bulletin 44, 705 (2019). [6] H. Wang, L. Zhang, J. Han, and W. E, Comp. Phys. Commun. 228, 178 (2018); L. Zhang, J. Han, H. Wang, R. Car, W. E, Phys. Rev. Lett. Phys. Rev. Lett. 120, 143001 (2018). [7] O. Abou El Kheir, L. Bonati, M. Parrinello, M. Bernasconi, arXiv: 304.03109; npj Comp. Mater., in press.Friday, January 19th, 2024, Lloyd Building Viz Room, 3pm
Computational Materials Discovery for Carbon Dioxide Capture Applications
Mathias Steiner (IBM Research Brazil)
Artificial intelligence (AI) is aiding the discovery of sustainable materials in every step along the computational workflow. Machine learning (ML) supports the automated extraction of data from the literature, the creation of large simulation data sets, the generative design of new materials, as well as the computational validation of discovery outcomes. In this talk, I will present our team’s research in the computational discovery of polymers [1,2] and nanopores [3,4,5] for carbon dioxide capture applications. I will discuss some of challenges in the AI/ML design of complex materials and provide examples of how discovery outcomes could be computationally validated, prior to lab synthesis and characterization. In view of global challenges such as climate change, open-science strategies with publicly shared data and models are needed for accelerating computational materials discovery.
[1] Giro, R., Hsu, H., Kishimoto, A. et al. AI powered, automated discovery of polymer membranes for carbon capture. npj Comput Mater 9, 133 (2023). https://doi.org/10.1038/s41524-023-01088-3. [2] Ferrari, B.S., Manica, M., Giro, R. et al. Predicting polymerization reactions via transfer learning using chemical language models. Preprint (2023). https://arxiv.org/abs/2310.11423. [3] Oliveira, F.L., Cleeton, C., Neumann Barros Ferreira, R. et al. CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks. Sci Data 10, 230 (2023). https://doi.org/10.1038/s41597-023-02116-z. [4] Zheng, B., Lopes Oliveira, F., Neumann Barros Ferreira, R. et al. Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2’s Chemisorption and Diffusion in Mg-MOF-74. ACS Nano 17 (6), 5579-5587 (2023). https://doi.org/10.1021/acsnano.2c11102.[5] Cipcigan, F., Booth, J., Neumann Barros Ferreira, R. et al. Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GflowNets. Preprint (2023). https://arxiv.org/abs/2310.07671.
Old Seminars
Friday, March 9th, 2018, Lloyd Building Viz Room, 3pm
From High-precision Imaging to High-performance Computing: Leveraging ADF- STEM Atom-counting and DFT for Catalyst Nano-metrology
Lewys Jones (School of Physics and CRANN, TCD)
Z-contrast imaging in the scanning transmission electron microscope (STEM) is a powerful tool to image precious metal heterogeneous catalysts at the atomic scale. When the annular dark-field (ADF) images from the STEM are quantified onto an absolute scale, it has been shown that it is possible to count the number of atoms in individual atomic columns of metallic nanoparticles and to estimate their three-dimensional structure [1]. In recent years further progress has been made in identifying the possible sources of error in the recording and analysis of quantitative annular dark-field (ADF STEM) images [2], in experiment-design, and in verifying the metrology by tomographic techniques. Of these developments, the move to fast multi-frame image-acquisition and -averaging has enabled the correction of experimental scanning-distortions, reductions in electron beam-damage of samples, and improvements in signal-noise ratio (SNR) [3]. Very recently, a new ADF image analysis best-practice, melding the benefits of both reference-simulation and unbiased statistical interpretation based analysis methods, has produced an atom counting method with even greater robustness [4,5]. Exploiting these recent technical developments, we obtain optimised raw data which is fed into high- throughput image processing tools revealing particle size, atom-counts etc. Unfortunately, our increased analysis throughput merely shifts the investigation bottleneck from data-processing to interpretation. To remedy this, we have developed a computationally-efficient genetic-algorithm based structure solving code (requiring a few tens of CPU hours per structure on a standard desktop PC) to retrieve likely low- energy 3D particle structures which match the experimental observations.
Here we present results from a pure platinum nanoparticle sample supported on a 3D amorphous carbon used in the cathode of hydrogen fuel cells to aid the oxygen reduction reaction (ORR). Experimentally observed structures with fewer than 600 atoms were further used as inputs for full molecular dynamics (MD) and density functional theory (DFT) calculations using the DL_POLY4 and ONETEP codes respectively. These calculations reveal the effect of surface atomic-roughness on the local electronic density, the Smoluchowski effect. These results predict that adatoms present strongly over-binding sites and would lead to a form of “topographic-poisoning”. Using these DFT calculations we can predict the oxygen binding energy of various surface sites as a function of coordination-number, or particle size or crystallographic facet for example, and even to speculate about the chemical activity of members of the experimental ensemble.
A striking conclusion from this work is the need to shift our focus from obtaining and analysing singular beautiful images, to the collective analysis of large numbers of low SNR images from ensembles of particles; then to use these data to explore cohorts of likely candidate structures. Efforts are now underway to generalise this new approach to larger particles, different structures and to whole ensemble measurements; at which point comparative chemical activity studies could be pursued. [6]
[2] L. Jones, IOP Conf. Ser. Mater. Sci. Eng., 109:1, 12008, (2016).
[3] L. Jones et al., Adv. Struct. Chem. Imaging, 1:1, (2015) p. 8.
[4] A. De Backer et al., Ultramicroscopy, 171, (2016) p. 104–116.
[5] A. De wael et al., Ultramicroscopy, 41:1, (2017), p. 81–94.
[6] The research was supported by the European Union under Grant Agreement 312483 – ESTEEM2 and EPSRC grant code, EP/K040375/1, for the ‘South of England Analytical Electron Microscope’.
Friday, January 26th, 2018, Lloyd Building Viz Room, 3pm
Towards an automated generation of ab initio-accurate Force Fields for thermal properties calculations
Alessandro Lunghi (Computational Spintronics Group, School of Physics, TCD)
DFT based computational methods are the method of choice when an accurate estimation of energies and forces is required. This level of accuracy, however, comes with a severe computational price that limits size and time-scales of possible investigations. Many interesting properties, such as phonon thermal conductivity, would require incredible efforts to be computed by DFT and the current state of the art in the field is limited to the study of few-atoms unit cell systems. A commonly employed strategy to overcome DFT limitations is to employ analytical expressions for the potential energy surface (PES), i.e. force fields. In recent years “machine learning” oriented force fields have been proposed with the promise of combining DFT accuracy with a higher throughput. Here I will present a method to obtain ab initio accurate force fields employing a SNAP potential. Major challenges in developing an automated generation of potentials will be illustrated together with results for selected 2D materials.
Friday, January 19th, 2018, Lloyd Building Viz Room, 3pm
Towards a parameter-free theory for electrochemical process at the nano-scale
Ashwinee Kumar (Computational Spintronics Group, School of Physics, TCD)
The electrified interfaces are very complex systems with a large variety of interactions from short range to long range. There are ionic and covalent bonds, hydrogen bonds, van-der- Waals interactions. Beside these different other phenomenon occurs at electrified interface, such as charge transfer, mass transfer, bond formation and bond breaking. Describing all this requires complex models, thus a realistic description of electrified interfaces are still missing.
In this work we make a step beyond the state of the art in the description of the electrified interface, extending previous schemes towards a more realistic dynamical picture of the equilibrium double layer under bias, which will be able to describe surface density redistribution and the effect of the strong fluctuation in the electric field at interface. We develop a model of a Pt-water solution half-cell where we explicitly describe water solution and metal electrode from first principle. The introduction of an excess of anions or cations in solution is used to control the charge on the electrode – in this way addressing the effect of an applied bias. In our model the separation between the two electrode surfaces (d ∼ 40 Å) and the cell cross section ( ∼ 286 A2) is adequate to achieve a realistic description of this interface, but small enough to accumulate sufficient statistics. Interface capacitance will be calculated a posteriori, by studying the relation between potential drop and surface charge to this end different interfaces will be aligned using the bulk potential of the solvent. In this way we will be able to describe the double layer of this interface for the first time in a realistic way. Then we try to compare the given statistics with the smaller system. CP2K code has been used to implement Born-Oppenheimer Molecular Dynamics(BOMD).