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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.

MATERIALS FOR HUMANITY (MH 21)

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

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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.

technical paper

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.

Facile Library Construction of Transition Metal Dichalcogenide (TMD) Quantum Dots (QDs) for Biomedical Application

Rational, data-driven materials discovery has the potential to make research and development efforts far faster and cheaper. In such a paradigm, computer models trained to find patterns in massive chemical datasets would rapidly scan compositions and systematically identify attractive candidates. Here, we present several examples of our work on developing machine learning (ML) and deep learning methods capable of creating predictive models using a diverse range of materials data. As input training data, we demonstrate ML on both large computational datasets of DFT calculations, as implemented in the Open Quantum Materials Database (oqmd.org), and also experimental databases of materials properties. We construct ML models using a large and chemically diverse list of attributes, which we demonstrate can be used as an effective tool to automatically learn intuitive design rules, predict diverse properties of crystalline and amorphous materials, such as formation energy, specific volume, band gap energy, and glass-forming ability, and accelerate combinatorial searches.

Using Artificial Intelligence to Discover New Materials

no abstract, Symp Q, 8 July, 2021. 10:00 A.M.

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

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