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Magnetic multilayer films are known to host a variety of novel magnetic configurations such as chiral domain walls and topological magnetic skyrmions with potential nano-electronic applications. However, characterizing these material systems typically involves a combination of several magnetometry, microscopy, and spectroscopy experiments, and some of these techniques can be time-consuming and expensive. Therefore, to rapidly characterize these material systems, it is crucial to maximize the information extracted from results of experiments that are more accessible, such as domain images obtained from magnetic force microscopy. We show that deep convolutional neural networks can extract magnetic parameters such as the exchange interaction, Dzyaloshinskii-Moriya interaction, and uniaxial anisotropy from images of domain configurations. Experimentally realistic training and validation data were generated through micromagnetic simulations. The trained models were consistently able to reach R2 values greater than 0.7 on validation data. By inspecting the intermediate feature maps of the neural network, we find that the network is able to learn features such as domain boundaries. Testing the models on actual experimental data yield values that were consistent with our knowledge of the material systems. Our work thus demonstrates the utility of developing machine models trained on simulation data as a means to accelerate the characterization of magnetic systems.

MATERIALS FOR HUMANITY (MH 21)

Estimation of magnetic parameters from domain images with convolutional neural networks

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MH 21 is arranged around 6 themes that focus attention on pressing needs that should be addressed to enhance the well-being of communities and provide a forum for presenting breakthrough discoveries.

Demand for Artificial Intelligence (AI) has been growing due to their usefulness in commercial electronics and in tackling global issues, such as a pandemic. However, conventional von Neumann devices that use AI require extensive programming and power. Thus, significant attention is dedicated to the brain-inspired neuromorphic computing (NC) as a more power-efficient alternative and a suitable platform for AI. NC involves a network of interconnected synapses and neurons that mimic their biological counterparts. In this work, we present a DW-based artificial neuron that automatically resets without applying current.
We simulated a synthetic antiferromagnetic (SAF) DW device where the free layer (CoFeB) is driven by spin-transfer-torque (STT), achieving the integrate function. In the absence of current, the antiferromagnetic coupling (AFC) between the free and pinned layers pushes the DW back towards the starting position. This happens automatically, and is indicative of the leakage function. The integrate and leakage functions are also achieved experimentally, suggesting that an SAF-based neuron is viable for NC.

Synthetic antiferromagnetic domain wall device as artificial neuron for neuromorphic computing

Descriptor based predictive model can be efficient in identifying an optimal carbon-based catalyst for oxygen evolution reaction (OER). Here, we correlate the O atom adsorption strength with the OER activity of graphene nanoribbon systems and define the energy parameters to identify the overpotential (ɳ). The π electron-based descriptor can predict the catalytic activity of the graphene surfaces. Machine learning algorithms like Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Support Vector Regression (SVR) are trained on the data generated by density functional theory to predict the energy parameter (ΔGO-ΔGOH) and overpotential. An optimal active site for OER using proposed SVR model is identified with the overpotential (0.29 V) and then validate through DFT calculations. Also, to generalize the study we used SVR model on nitrogen doped graphene system to predict the site-specific activity towards OER. Such a combined approach can be extended to estimate the site-specific OER activity of different carbon catalysts at a dramatically reduced computational cost.

Energy Parameter and Electronic Descriptor for Carbon Based Catalyst Predicted using QM/ML

Either experimental trial and error or density functional theory (DFT) calculations for screening electrocatalysts remain comparatively expensive and scale poorly with system size. Here, we develop a simple but accurate binding-energy prediction machine-learning model to accelerate the hydrogen evolution reaction (HER) catalyst screening. Based on around 20 000 DFT-calculated HER binding-energy datasets, we develop a simple machine-learning model with only four features based on the optimized finger mapping method. Our method predicts H binding energies with a mean absolute error (MAE) of 0.14 eV. It is much lower than that from the regression of the Voronoi tessellations-based crystal structure modeling method (0.27 eV) and crystal graph convolutional neural networks (0.19 eV), which requires hundred features or expensive training cost. Moreover, for a small dataset, our explainable model has superior performance to the deep neural network models.

Binding-energy prediction machine-learning models for accelerating HER catalyst screening

Defects including the pores and cracks significantly degrade the properties of as-built parts. The molten pool flow, particularly the keyhole effect, plays a key role in the defects that are difficult to observe through experiments in the selective laser melting process. In this study, we derived a new formulation of evaporation for metal alloys from thermodynamics considering the chemical compositions, and the formation of the keyhole was simulated using the Finite Volume Method (FVM) with the Volume of Fraction method to track the gas-liquid interface, where the multi-reflection of the laser within the keyhole and powder bed was incorporated using a ray-tracing algorithm. The geometry features of the simulated keyhole and melting pool showed a good agreement with the experimental observation by ultrahigh-speed X-ray imaging in Argonne National Lab. To understand the physical mechanisms of the keyhole, the laser energy distribution on the keyhole surface, and the driving forces (e.g. Marangoni force, recoil pressure) on the keyhole surface were further analyzed. The laser scanning simulations on a bare plate under external magnetic fields indicate that the Lorentz force can smooth the fluid flow field in the molten pool and increase the ratio of equiaxed grains in the solidication front.

Evaporation Model for Keyhole Dynamics During Additive Manufacturing of Metal

Historically, students of materials science and engineering were introduced to crystal chemical principles through examining physical models of simple crystals (typically metals or binary salts), latterly augmented by 3D visualisation of structures (the persistent interest in perovskites comes to mind). Both approaches have their strengths and weaknesses. Physical models cater well to tactile learners but are limited to simple lattices. Commonly, the concept of close packing is illustrated with large balls to demonstrate 2-layer hexagonal close packing (hcp) and 3-layer cubic close packing (ccp) with smaller balls occupying interstitial cavities (1). Such models are appreciated by students and feedback indicates improved appreciation of octahedral and tetrahedral co-ordination. A limitation of this approach is that interstitial sites are ‘hidden’ amongst the large balls, although this can be overcome through the use of transparent spheres (2). Ball and stick models remove some of these limitations, but are more appropriate for molecular chemistry, as they become rather intricate when dealing with ‘infinite’ lattices of even moderate complexity.

Computer-generated structure representations have the advantage of being readily scalable to larger classes, inexpensive and applicable to complex structures. Their use can also be integrated with crystal database extraction and introducing the basics of crystallographic information files (CIFs). However, the very ease of producing beautiful and dynamic drawings, especially with those packages that automatically locate and display the polyhedra can lead to a black-box exercise with poor educational outcomes. Nonetheless, such packages are de rigueur in crystal chemical research and students should be proficient in their use and the interpretation of topological relationships.

Precision polyhedral models bridge the gap between classical constructions from spheres or balls-and-sticks and contemporary computed representations. First, more complex structures can be assembled from polyhedral groupings. Second, the different topological connectivity between polyhedra (corner-, edge- face-sharing) becomes evident, as do changes in symmetry through displacive topological transformations. Third, the Wyckoff positions extracted from the CIFs can be readily identified, and finally, the physical models bear a striking resemblance to the 3D visualizations.

This video poster demonstrates the process and the value of exploiting close-packed kernels to explore the breadth of connectivity in several important structures. Using hemi-cuboctahedra the ccp kernel (a cuboctahedron) and the hcp kernel (a twinned cuboctahedron) are constructed. CIF for rutile (hcp), wurtzite (hcp), rhenia (ccp) and sphalerite (ccp) are drawn using 3D visualisation tools. Students then construct the polyhedral representations of these structures around the kernels.

Unfortunately, research concerning the synergistic benefits of the combined manipulation of physical and virtual models is sparse, but the anecdotal evidence is that a deeper appreciation of crystal chemical concepts results (3). No doubt students with different learning-strengths will benefit more-or-less from these complementary approaches, and for this reason, team learning where abstract thinkers produce the 3D visualisation and hands-on practitioners build the physical model can be especially instructive.

(1) Ma et al., Using Magnet-Embedded Silicone Balls to Construct Sable Models for Close-Packed Crystal structures, J. Che. Educ., 97, 4063-4068 (2020).
(2) Lenzer et al, Making it Clear: Exploring Crystal Structures by Constructing and Comparing See-Through Models. J. Chem. Educ. 2019, 96, 8, 1630–1639 (2019)
(3) Kelly, I Comparison of Virtual Models and Hands-on Models for Teaching Crystallography, (2001) Master Thesis, Purdue University


Precision Polyhedral Models for Crystal Chemical Instruction: Integrating Crystal Structure Databases and Enhancing the Appreciation of 3D Visualisation

ATLAS is a platform jointly developed by Nanyang Technological University (NTU) and Ludwig Maximilian University of Munich (LMU). It aims to facilitate global students and lifelong learners to have more flexible and personalized learning experiences. Its current largest deployment is an international graduate degree program in Materials Science, NTU. The initial-deployed courses were launched in 2016. The platform has delivered 14 courses to approximately 800 global graduate students during these years. Some of the features of ATLAS include dynamic reasoning maps, clickable transcript linked with video, diverse assessments including instant auto-marking MCQs, peer-review essay, and peer-review presentation. In the future, ATLAS is planned to facilitate cross referencing among courses, in order to provide more opportunities for personalized learning. Bases on students’ feedback, the flexibility, clear structure, and sufficient explanation of the concepts made them enjoy the courses. Further developments of ATLAS will enhance platform stability, formalize training of faculty in learning design and course conversion, regularize course operation and maintenance support, and improve the interaction between faculty and students by deploying hybrid learning and live tutorial sessions.

Advanced Teaching, Learning, and Application System (ATLAS): a Tool for Graduate Student Training for Materials Science and Engineering

n this presentation, I will introduce the future development of chemical recycling as an initiative to solve the plastic problem.　In particular, I will introduce the outline of the actual chemical recycling process and explain the necessity of fusion of the arterial industry and the venous industry for the future based on the case of the technological development.

Integration of arterial and venous industries & promotion of chemical (feedstock) recycling

Sustainable nanomaterials, such as cellulose nanocrystals (CNCs) are rod-like nanoparticles obtained by sulfuric acid hydrolysis of cellulose fibres. Several properties of CNCs, such as its availability, low cost, high mechanical strength, large number of surface functional groups, high surface area per volume and aspect-ratio have led to an increasing interest in using them for adsorption and controlled release applications. Pristine CNCs were incorporated into hydrogel beads in order to eliminate the need for centrifugation. CNCs used in these hydrogel beads were also functionalized and the adsorption and controlled release characteristics were examined. CNCs incorporated alginate hydrogel bead systems possessed attractive adsorption properties both in batch and fixed bed column adsorption systems. These hydrogel beads could be regenerated and reused if necessary. Additionally, cellulose nanofibers (CNFs) were combined with CNCs and pulp fibers to produce filtration membrane suitable for the removal or organic and metallic contaminants. These sustainable nanomaterials possess attractive characteristics for the removal of various types of contaminants in wastewater systems. In addition, these nanomaterials can be modified and functionalized for selective adsorption of heavy metals and also to decontaminate water by incorporating anti-bacterial properties to these nanomaterials.

Sustainable Nanomaterials in Environmental Management Systems

Additive manufacturing is currently paving its way to industrial applications at high pace. While in medical applications there is already a widespread use of AM for customized solutions, the strongest innovation boost in AM is coming from aviation industry, followed by the energy sector, automotive industry, space and toolmaking industry. 
Relying on a strong academic and industrial background in advanced manufacturing technologies, Europe is a strong player in the development of AM machines, processes, materials and components. Major national and European R&D programs as well as company in-house activities have led to successful implementation of AM for functional parts made from polymers, ceramics and metals. 
The aim of this lecture is to highlight selected European AM research initiatives and their research foci, covering the different classes of materials and a wide range of AM processes. Major emphasis is placed on challenges towards industrial implementation, including quality assurance and process certification, high performance materials and structures, multi-material and multi-functional solutions, testing and characterization as well as standardization actions.


Recent Developments in Additive Manufacturing in Europe

Transition metal dichalcogenides (TMDs) QDs are fundamentally interesting because of the stronger quantum size effect with decreased lateral dimensions. 1-3 Here we demonstrate that biomineralization assisted bottomup strategy can be broadly applied for synthesizing a wide library of TMD QDs, Though this method, we found that increased sulfur defects correlated well with enhanced fluorescence and increased oxidative stress generation from MoS2 QDs, leading to cell imaging, and enhancing photodynamic effect. Further, we also developed other TMD QDs based nanoagents for photothermal assisted chemodynamic therapy of tumor and ptotoactivated antibacterial. Thus, our facile strategy shows the potential to motivate future design and optimization of many other TMDs QDs for biomedical applications.

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