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Formidable progress has been recently achieved in the fabrication and characterization of disordered low dimensional materials with unprecedented properties. For instance, particular forms of disordered graphene (reduced graphene oxides) and other two-dimensional materials, obtained by chemical exfoliation techniques, have been found suitable to improve the performances of composite materials, with applications in energy, textiles, solar cells, thermoplastics etc. In parallel, catalytic vapor deposition methods have been used intensively to upscale the growth of two-dimensional materials at wafer size, with resulting material morphologies ranging from polycrystalline to fully amorphous phases. Recent reports on wafer-scale synthesis of ultrathin layered amorphous carbon and boron nitride, structurally dominated by sp2 hybridization but lacking crystalline orders, have shown superior performances of those materials as coating and interfacial components, providing a new platform for properties reinforcement in the industrial context.

Here, we present our computational strategies to achieve predictive modelling in this context. For the “computationally-assisted” material synthesis, we use machine learning to generate atomic interaction potential in an arbitrary disordered local environment and obtain realistic large-scale material configurations through classical molecular dynamics, while generic tight-binding models are generated based on resulting spatial atomic distribution and optimized DFT-inspired Hamiltonian parameters. Next, we employ efficient linear scaling quantum transport [1] (see also www.lsquant.org) to simulate structural models containing up to a billion atoms, hence demonstrating unprecedented supremacy to match predictive modelling with experimental scales, and therefore constituting decision-making tools for material design and industrial performances achievement. We illustrate our strategy on various examples including the (thermal) reduction process of graphene-oxides for recovering higher crystallinity, the correlation between the degree of disorder and resulting physical properties in polycrystalline materials [2], as well as amorphous phases of sp2 carbon members [3] and boron-nitride [4]. The obtained results provide scaling laws and guiding rules for researchers and engineers to target the proper material growth and integration conditions for reaching the upper limit of a given application objective, such as flexible conductors, spin channel, ultrathin heat spreaders, energy harvesters, insulating thermal barriers, or ultralow dielectric coefficient materials.
[1] Z. Fan et al. S. Roche, Physics Reports 903, 1-69 (2021)
[2] AW Cummings et al. Advanced Materials 26 (30), 5079-5094 (2014)
[3] A Antidormi, L Colombo, S Roche, 2D Materials 8 (1), 015028
[4] S. Hong, et al. A. Antidormi, S. Roche, M. Chhowalla, H.J. Shin &amp; H.S. Shin, Nature 582, 511–514 (2020)

MATERIALS FOR HUMANITY (MH 21)

Multiscale Computational Methods for Exploring Electrical and Thermal properties of highly disordered Composites Materials: From Machine-learning based Molecular Dynamics to Linear Scaling Transport Methods

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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.

Formidable progress has been recently achieved in the fabrication and characterization of disordered low dimensional materials with unprecedented properties. For instance, particular forms of disordered graphene (reduced graphene oxides) and other two-dimensional materials, obtained by chemical exfoliation techniques, have been found suitable to improve the performances of composite materials, with applications in energy, textiles, solar cells, thermoplastics etc. In parallel, catalytic vapor deposition methods have been used intensively to upscale the growth of two-dimensional materials at wafer size, with resulting material morphologies ranging from polycrystalline to fully amorphous phases. Recent reports on wafer-scale synthesis of ultrathin layered amorphous carbon and boron nitride, structurally dominated by sp2 hybridization but lacking crystalline orders, have shown superior performances of those materials as coating and interfacial components, providing a new platform for properties reinforcement in the industrial context.

Here, we present our computational strategies to achieve predictive modelling in this context. For the “computationally-assisted” material synthesis, we use machine learning to generate atomic interaction potential in an arbitrary disordered local environment and obtain realistic large-scale material configurations through classical molecular dynamics, while generic tight-binding models are generated based on resulting spatial atomic distribution and optimized DFT-inspired Hamiltonian parameters. Next, we employ efficient linear scaling quantum transport [1] (see also www.lsquant.org) to simulate structural models containing up to a billion atoms, hence demonstrating unprecedented supremacy to match predictive modelling with experimental scales, and therefore constituting decision-making tools for material design and industrial performances achievement. We illustrate our strategy on various examples including the (thermal) reduction process of graphene-oxides for recovering higher crystallinity, the correlation between the degree of disorder and resulting physical properties in polycrystalline materials [2], as well as amorphous phases of sp2 carbon members [3] and boron-nitride [4]. The obtained results provide scaling laws and guiding rules for researchers and engineers to target the proper material growth and integration conditions for reaching the upper limit of a given application objective, such as flexible conductors, spin channel, ultrathin heat spreaders, energy harvesters, insulating thermal barriers, or ultralow dielectric coefficient materials.
[1] Z. Fan et al. S. Roche, Physics Reports 903, 1-69 (2021)
[2] AW Cummings et al. Advanced Materials 26 (30), 5079-5094 (2014)
[3] A Antidormi, L Colombo, S Roche, 2D Materials 8 (1), 015028
[4] S. Hong, et al. A. Antidormi, S. Roche, M. Chhowalla, H.J. Shin & H.S. Shin, Nature 582, 511–514 (2020)

technical paper

It has been well known that various kinds of lattice defects are ubiquitous in real materials and they govern mechanical properties of materials based on pioneering studies. While removal of detrimental influence by lattice defects has been one of the principal aim of recent studies [1-2], it is reported that further exploitations of those lattice defects for other functional properties is possible, where lattice defects play yet another role or more to control or even create resultant materials properties [3-6].
Aiming at modifying or even creating new properties that single crystals cannot acquire, we have carried out modeling of lattice defects for atomistic simulations including molecular dynamics and ab initio calculations [7-12]. Then, we identify the origin of the resultant thermal transport properties, thereby exploiting lattice defects for advanced materials [13-15] in the framework of “crystal defect cores” [16].
In this presentation, findings upon modeling of lattice defects, resultant transport properties and underlying mechanisms behind the properties will be discussed.

References:
[1] S.F. Chichibu, et al., Nat. Mater. 5 (2006) 810–816.
[2] Z. Chen, et al., Nat. Commun. 8 (2017) 14101.
[3] Y. Takashima, et al., Nat. Commun. 2 (2011) 168.
[4] A. Nakamura, KM, et al., Nat. Mater. 2 (2003) 453–456.
[5] Y. Oshima, AN, KM, Science. 360 (2018) 772–774.
[6] K. Matsunaga, TY, AN, et al., Acta Mater. 195 (2020) 645–653.
[7] T. Oyama, MY, KM, et al., Phys. Rev. B, 71 (2005) 224105.
[8] M. Yoshiya and T. Oyama, J. Mater. Sci., 46 [12] (2011) 4176-4190.
[9] B. Feng, et al., Nat. Commun., 7 (2016) 11079.
[10] T. Yokoi and M. Yoshiya, Physica B, Condensed Matter, 532 (2018) 2-8.
[11] T. Yokoi, AN, KM, Phys. Rev. Mater., 4 (2020) 014605.
[12] T. Yokoi, AN, KM, et al., Phys. Chem. Chem. Phys., 23 (2021) 10118.
[13] S. Fujii, MY, et al., Sci. Rep., 8 (2018) 11152.
[14] S. Fujii, TY, MY, Acta Mater., 171 (2019) 154-162.
[15] W. Sekimoto, SF, MY, Scr. Mater., 202 (2021) 113991.
[16] https://www.core.mp.pse.nagoya-u.ac.jp/en/

Modeling of Lattice Defects and Resultant Thermal Transport Properties of Materials

N-type transparent conductors (TCs) are key materials in the modern optoelectronics industry. Despite years of research, the development of a high-performance p-type TC has lagged far behind that of its n-type counterparts, delaying the advent of “transparent electronics” based on transparent p-n junctions.[1] Here, we computationally investigate three layered oxychalcogenide structural motifs to try to predict new p-type TCs, namely the [Cu2Ch2][A3B2O5] (325), [Cu2Ch2][A4B2O6] (426) and [Cu2Ch2][A2BO2] (212) structural motifs. Specifically, we have used a materials informatics approach (SMACT) to screen through the search space using low-cost heuristic tools.[2] This reduces the potential combinations from 1800 to a more computationally tractable 228, which then undergo DFT calculations to assess thermodynamic and dynamic stability. In this talk, I will present an update on how our search has predicted >50 novel semiconductors with potential applications ranging from TCs[3,] to photocatalysts,[4], solar absorbers and thermoelectrics.

[1] A. Walsh and J.-S. Park, The Holey Grail of Transparent Electroncs, Matter, 3, 604 (2020)
[2] D. W. Davies, K. T. Butler, A. J. Jackson, A. Morris, J. M. Frost, J. M. Skelton and A. Walsh, Computational Screening of All Stoichiometric Inorganic Materials, Chem, 1 617 (2016)
[3] B.A.D. Williamson, G.J. Limburn, G.W. Watson, G. Hyett, and D.O. Scanlon. Computationally Driven Discovery of Layered Quinary Oxychalcogenides: Potential p-Type Transparent Conductors?
[4] G. J. Limburn, M. J.P. Stephens, B. A. D. Williamson, A. Iborra-Torres, D. O. Scanlon and G. Hyett. Photocatalytic, structural and optical properties of mixed anion solid solutions Ba3Sc2−xInxO5Cu2S2 and Ba3In2O5Cu2S2−ySey, Journal of Materials Chemistry A, 8, 19887 (2020).


Computational Prediction of Novel Quinary Layered Oxychalcogenides

There is an ever-increasing need for new magnetic materials. When developing them, it is advantageous to improve our understanding of mechanisms that are decisive for the existing materials. Iron and iron-based materials are prototype magnetic systems and the question is whether their properties have already reached their limits or can be further improved beyond the current horizons. We have employed quantum mechanical calculations to shed a new light on relations between the extended defects and magnetic properties in Fe-Al-based materials. We show that there is a very strong competition between the thermodynamic stability, that favors Al atoms becoming the first nearest neighbors of Fe atom, and iron magnetic moment that is reduced by this trend [1-3]. We analyze how extended defects, in particular antiphase boundaries, alter local atomic configurations of Fe atoms and, consequently, increase the magnetic moment. The increase can be as massive as by 140% in off-stoichiometric Fe2AlTi intermetallic com-pound with the Heusler L21 structure [4]. We suggest that defects can be used as designing tool when developing new magnetic materials. Additional studies of point defects are under way [5]. 

References
[1] I. Miháliková et al., Nanomaterials 8 (2018) 1059; doi:10.3390/nano8121059.
[2] I. Miháliková et al., Materials 12 (2019) 1430; doi:10.3390/ma12091430. 
[3] M. Friák et al., Nanomaterials 10 (2020) 044, doi:10.3390/nano10010044.
[4] M. Friák et al., Crystals 9 (2019) 299; doi:10.3390/cryst9060299.
[5] P. Šesták et al., manuscript in preparation (2021). 



Designing improved magnetic materials by exploiting extended defects

Transforming the abundant atmospheric nitrogen (N2) into ammonia (NH3), a widely used industrial feedstock, through a green and sustainable process has gained an increasing interest for both commercial and environmental needs. The current industrial route for the production of NH3 is based on the Haber-Bosch (H-B) process, which is conducted under high temperature (~500℃) and pressure (~200 atm) conditions and consumes about 2% of the world’s energy per year. Thus, it is of paramount significance to search for energy-efficient and environmentally friendly techniques to replace the H-B process. The electrochemical nitrogen reduction reaction (eNRR), which takes place at mild conditions and can be driven by renewable energies such as solar and wind, constitutes a desirable alternative. However, the development of low-cost and high-efficient electrocatalysts for eNRR has remained elusive up to now. In this work, we conduct a comprehensive study to unravel the reaction mechanisms of N2 fixation on SAC-Nb2CN2 and molybdenum nitride by using density-functional-theory (DFT) calculations. 
For SAC-Nb2CN2, we screen the transition metals (TM), including 26 elements, supported on two-dimensional (2D) Nb2CN2 (TM-Nb2CN2) for their applications into electrochemical reduction of N2 (NRR) based on first-principles calculations. We show that most SACs can bind with Nb2CN2 strongly through a TM-N3 configuration. We find that Mn-Nb2CN2 is a promising candidate for the N2 reduction reaction (NRR), with a low overpotential of 0.51 V through the distal mechanism. Importantly, TM-Nb2CN2 presents high selectivity to NRR by blocking the hydrogen adsorption and preventing the hydrogen evolution reaction (HER). 
For molybdenum nitride, the activity and selectivity of eNRR on pristine (001) and (110) Mo5N6 surfaces as well as few specific numbers of heteroatom anchored N-terminated surfaces are all evaluated and compared. We find that the Mo and N atoms on the pristine Mo5N6 surface are both active for eNRR, while follow different pathways in mechanism. Moreover, the eNRR catalytic performance of Mo5N6 could be further boost by specific metal atoms anchoring, such as single atom, metal dimer and heterodiatom pair. Finally, a full map of eNRR mechanism on pristine and metal atom-decorated Mo5N6 surfaces are illustrated. 
Our work not only provides a fundamental understanding of eNRR mechanism on TMNs based materials, but also offers powerful strategies towards the rational design of efficient NRR electrocatalysts.

Design of Catalysts for Water Splitting and N2/CO2 Reduction

Starting from bcc/fcc unit cells for iron ground/excited states, mostly we use the minimum unit cell for the target crystal from the well-known materials database with long history. We have extended the standard unit cell for Wurtzite (B, Al, In)xGa1-xN to 2x2x2 supercell etc., and found that previously used unit cells are NOT the ones for the best ground state atomic structures. We found that there are energetically more favorable complex atomic structures only possible to be explained by introducing larger number of atoms in larger unit cells. For quite long time ago, double Perovskite structure (deformed unit cells) has found to be better than the single unit call, which can be explained by introducing the φ4 potential minimum, better than the simple harmonicφ2 one. This is not the only one case, but there are many such cases which are related to widely used materials, some of them have been known to explain the experimental observations.
In this presentation, our recent work will be introduced on a necessary larger unit cell for Wurtzite (B, Al, In)xGa1-xN system, which has been know experimentally to explain the favorable growth direction known as pulling effect in epitaxial layer fabrication, which now is cleared to be related to the lager unit cell we have theoretically introduced. Minority elements are presented by two atoms in the simulation cell and the energetics of these spatial configurations are analyzed to estimate the preferable group-III sites of boron, aluminum and indium atoms. Boron atom energetically prefers to occupy in a collinear orientation along the c-axis [0001], which we proved to be related to the experimentally known “pulling effect”.
More recently clarified phenomena will be presented.



Your Unit Cell is Really the Unit？

In the past several decades, the materials innovation has been relying on the case-by-case based trial-and-error method, which is quite effective but less efficient. It has been suggested that high-throughput experiments and calculations can significantly speed up the material innovation process. In this talk, we report an accelerated discovery of various catalysts by using first-principles calculations and semi-automated high-throughput calculation framework. For example, from several thousand two-dimensional materials, we identify 9 two-dimensional materials with active basal planes for catalysing hydrogen evolution reaction. Moreover, among various transition metal single atoms, we predict that Mo single atom anchored on the MoS2 monolayer might have promising performance for nitrogen reduction reaction. All these results show the promise of using high-throughput first-principles calculations to speed up the innovation of novel functional materials.

High-Throughput First-Principles Calculations Accelerated Materials Discovery

Two-dimensional (2D) transition metal carbides Mn+1Xn labeled as MXene derived from ternary transition metal carbides MAX (Mn+1AXn) phases attract increasing attention due to their promising applications as Li-Ion battery anodes, hybrid electro-chemical capacitors and electronic devices, etc. Experimentally, most MXenes are synthesized by chemical etching the “A” layers from MAX phases. So far, MXene is the largest family of 2D materials as there are over 70 MAX phases available, and clearly, the conventional route of synthesis-property characterization to obtain desirable MXense is time consuming and cost demanding. This talk will highlight our recent work on the prediction and design of MXenes with desired properties by means of ab initio calculations and ab initio molecular dynamics simulations. On the other hand, inspired by the chemical bonding characters of MAX phases and the way of synthesizing MXenes, we further predicted a group of 2D transition metal borides labeled as MBenes from their corresponding layered ternary phases. The theoretical predictions were confirmed by experiments. MBenes also show promise in the application of Li ion batteries and electrocatalysis, etc. Finally, the results of this talk highlight the power of ab initio calculations in accelerating the discovery of novel 2D materials and in the design of desired properties as well.

Two-Dimensional Transition Metal Carbides and Borides

Nature produces a variety of materials with many functions, often out of simple and abundant materials, and at low energy. Such systems - examples of which include silk, bone, nacre or diatoms - provide broad inspiration for engineering. Here we explore the translation of biological composites to engineering applications, using a variety of tools including molecular modeling, AI and machine learning, and experimental synthesis and characterization. We review a series of studies focused on the mechanical behavior of materials, especially fracture, and how these phenomena can be modeled using a combination of molecular dynamics and machine learning. We also present various case studies of hierarchical material optimization using genetic algorithms, applied to 3D printed composites, and protein design.

Computational material synthesis: Atomistic and molecular dynamics, composites, and bio-inspired AI

Laser-powder bed fusion (L-PBF) of alloys often leads to large, elongated grains, which are the cause for anisotropic mechanical properties and, sometimes, solidification cracking. While grain refinement has been used before in the design of Al alloys for AM in order to avoid these issues, it is much less common in steels. Here, we report that Ti(O,N) is an effective inoculant for ferritic steels, owing to the Baker-Nutting crystallographic orientation relationship and matching lattice constants. We show that this inoculating phase can be added to steel powder ex situ as TiN micro-powder, but that it also can be formed in sub-µm-size in the melt by reaction of the alloying element Ti with nitrogen from the process atmosphere. Thereby, the refining effect can be “switched” by the choice of process gas. By both, in-situ synthesis and ex-situ addition, ultrafine grains with an average diameter just larger than 1 µm and a completely random texture are achieved. We show that this refinement method is effective in several ferritic steels (Fe-Ti, Fe-Al, Fe-Cr) and discuss the required amount of Ti addition. The addition of 5wt.-%Ti to Fe-19wt.-%Cr leads to a doubling of the yield strength and UTS, which we argue is mostly due to the grain refinement by Ti(O,N).

Ultrafine-grained ferritic steels by L-PBF via ex-situ addition or in-situ synthesis of inoculants

Porosity in laser powder bed fusion is a concern due to the influence of such defects on mechanical properties of parts. Different mechanisms lead to different types and shapes of pores, in different quantities and it is important to be cognizant of these differences, for best approaches to minimize their formation and even remove them by different methods. Results are presented showing how this can be done for different types of pores in the laser powder bed fusion of metals.

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