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In this project, two-dimensional (2D) materials are classified into the categories 2D magnetic materials and 2D non-magnetic materials. The 2D magnetic materials are further classified into two categories, namely 2D ferromagnetic materials and 2D anti-ferromagnetic materials. It is important to identify 2D ferromagnetic materials because they are used in various important applications such as data storage mechanisms and spintronic devices. Raw data from the 2D material databases such as 2DMatpedia was used in the project. Classical machine learning involving algorithms such as decision trees and support vector machines were used to classify the data obtained from the above databases into the respective categories anti-ferromagnetic, ferromagnetic and non-magnetic materials. Techniques such as deep learning and convolutional neural networks were also employed to classify the data. The best results for classification of the two-dimensional data into magnetic and non-magnetic materials were obtained for the classical machine learning algorithm, gradient boost algorithm with an accuracy of 93.37%. The most important features of the materials for determining the magnetic properties were band gap, number of magnetic atoms, maximum ionic character, presence of chromium and presence of manganese in that order. The magnetic materials classified were further classified into ferromagnetic and anti-ferromagnetic materials. The best accuracy for this process was obtained to be 94.44% using the classical machine learning algorithm, random forest classifier. The average ionic character, maximum average ionic character, yang-omega, and band-center were the key features for determining whether a material is ferromagnetic or anti-ferromagnetic. A web interface was deployed for the model and can be found here: https://twodferromagnetism-model.herokuapp.com. The code used for obtaining the results can be found here: https://github.com/RiyaBOT/ME4101A-ShethRiyaNimish.

MATERIALS FOR HUMANITY (MH 21)

Exploring two-dimensional ferromagnetic materials by machine learning

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Many industries use deep learning algorithms to assist in the research and development of products. Therefore, we focus on using deep learning to help predict the parameters of new compounds and to help determine whether the compound is stable.
In this project, we explore the establishment of AI models through different algorithms for machine learning and deep learning. Then, we obtain the important features in the prediction process from the machine learning part, so as to provide a reference for actual research work. The accuracy of the model we get is analyzed and compared to determine the final algorithm and build the model. Using this model, we can predict the formation energy and energy above hull of compounds to determine whether they can exist stably. Finally, we integrate functions into a GUI, so that researchers can use this GUI to make stable predictions of any unknown new compounds easily and conveniently. Throughout the project, the accuracy of the model and the GUI have been continuously improved by us. And show these results in the following part.

Deep learning for accurate predictions of alloy stability

The setting up of the magnetic topological materials database provides the possibility of exploring in this field through the machine learning method. Here, we trained a support vector classification (SVC) model to predict the topological state of the magnetic materials. There are 551 of materials in the topological magnetic materials database, and for each material, the topological state is different for different U values as the date are the DFT results. So, different U values should be different data and finally, 1319 of data are used for the model training. Although the strongly imbalance of the data (only 87 of data are topological insulators (TIs)), the trained model still can reach high precision (0.99) for trivial materials, which means that almost all the un-trivial magnetic materials can be screened by our model. Then, the SVC model is used to predict the topological state of the magnetic materials in MAGNDATA database and select 65 materials as TIs and 15 materials as enforced semimetals (ESs) for all the four times prediction. Topological quantum chemistry method then used to test the topological state of the randomly selected 11 of the un-trivial materials and indicate that 5 of them are ES and 1 is TI. Then first principle method is applied to further inspect the topological state of the only TI. The atom unfilled orbitals and valence electrons are deemed as import features by our model, which is in consistence with the physical analysis results. This SVC model could help screen topological materials from large amount of magnetic materials.

Machine Learning Method to Fastly Screen Topological Magnetic Materials

Two-dimensional topological superconductivity candidate in a van der Waals layered material
Jing-Yang You ,1 Bo Gu ,2 Gang Su,2 and Yuan Ping Feng1,3,*
1Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117551
2Kavli Institute for Theoretical Sciences, and CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100190, China
3Centre for Advanced 2D Materials, National University of Singapore, 6 Science Drive 2, Singapore 117546

Two-dimensional (2D) topological superconductors are highly desired because they not only offer opportunities for exploring novel exotic quantum physics but also possess potential applications in quantum computation. However, there are few reports about 2D superconductors, let alone topological superconductors. Here, we find a 2D monolayer W2N3, which can be exfoliated from its real van der Waals bulk material with much lower exfoliation energy than MoS2, to be a topological metal with exotic topological states at different energy levels. Owing to the Van Hove singularities, the density of states near the Fermi level are high, making the monolayer a compensate metal. Moreover, the monolayer W2N3 is unveiled to be a superconductor with the superconducting transition temperature TC∼22 K and a superconducting gap of about 5 meV based on the anisotropic Migdal-Eliashberg formalism, arising from the strong electron-phonon coupling around the point, and the 2D superconductor is phonon mediated and fits the BCS mechanism with an Ising-type pairing. Because of the strong electron and lattice coupling, the monolayer displays a non-Fermi liquid behavior in its normal states at temperatures lower than 80 K, where the specific heat exhibits T3 behavior and the Wiedemann-Franz law is dramatically violated. Our findings not only provide a platform to study the emergent phenomena in 2D topological superconductors, but also open a door to discover more 2D high-temperature topological superconductors in van der Waals materials.

Reference:
[1] J-Y. You, B Gu, G. Su, Yuan Ping Feng, Physical Review B 103, 104503 (2021).

Two-dimensional topological superconductivity candidate in a van der Waals layered material

Two-dimensional (2D) Janus MoSSe have great potential for photocatalytic water splitting due to their novel properties induced by the unique out-of-plane asymmetric structures.1-3 Doping is an effective way to modulating the photocatalytic properties. The intrinsic defects are usually unavoidable when introducing extrinsic defects like transitional metal adatoms. However, to our knowledge, few researchers have investigated the synergistic effects on the photocatalytic properties when vacancies and adsorbed transition metals coexist on the MoSSe surface. Here, we systematically investigate the geometric, electronic, and optical properties of 2D Janus MoSSe with titanium doping and vacancies to explore their synergistic effects on photocatalytic activity. 
We find that there is effective attraction between the substituted or adsorbed Ti atoms and S/Se vacancies. The Ti adatoms dramatically extend the light absorption range to the infrared region. The S/Se vacancies coexisting with Ti adatoms will modulate the transition of photoexcited electrons, thereby enhancing sunlight absorption as shown in Figure 1. The Ti adatoms either existing alone or coexisting with vacancies introduce smaller lattice distortion compared with substituted Ti atoms, and these Ti adatoms induce smaller effective mass of charge carriers. The configuration of S vacancy coexisting with Ti adatoms on the Se surface exhibits the most significant synergistic effects and the best overall photocatalytic performance. 
Our work reveals the mechanisms and effects induced by doping and vacancies coexisting in 2D Janus MoSSe and also proposes a new practical strategy to improve the performance of 2D photocatalysts.


Effects of Vacancy and Ti Doping in 2D Janus MoSSe on Photocatalysis

We present a quantum-mechanical study of a B2-phase of Fe70Al30 alloy
with and without antiphase boundaries (APBs) with the {001} crystallographic
orientation of APB interfaces. Our motivation is to explain experimental
findings demonstrating a significantly higher magnetic flux density from 
A2-phase interlayers at the thermally-induced APBs in this material
and suggesting that the ferromagnetism is stabilized by the disorder in
the A2 phase. Our investigation of sharp APBs (without any A2-phase interlayer) 
indicates that they have moderate APB energies (~0.1 J/m2) and cannot explain the 
experimentally detected increase in the ferromagnetism because they 
often induce a ferro-to-ferrimagnetic transition. When studying
thermal APBs, we introduce a few atomic layers of A2 phase of Fe70Al30 into
the interface of sharp APBs. The averaged computed magnetic moment of Fe atoms
in the whole B2/A2 nanocomposite is then increased by 11.5% w.r.t. the B2 phase.
The A2 phase itself (treated separately as a bulk) has the total magnetic moment
even higher, by 17.5%, and this increase also applies if the A2 phase at APBs
is sufficiently thick (the experimental value is 2–3 nm). We link the changes
in the magnetism to the facts that (i) the Al atoms in the first nearest
neighbor (1NN) shell of Fe atoms nonlinearly reduce their magnetic moments
and (ii) there are on average less Al atoms in the 1NN shell of Fe atoms
in the A2 phase. These effects synergically combine with the influence
of APBs which provide local atomic configurations not existing in
an APB-free bulk. The identified mechanism of increasing the magnetic
properties by introducing APBs with disordered phases can be used as
a new designing principle when developing new magnetic materials.

Enhancing magnetism in Fe-Al alloys by increasing chemical disorder

Solid -state conductors have attracted wide attention due to their benefit of enhanced safety and capacity with application in batteries. Our computational tools--bond valence pathway analyzer (BVPA) have been reported to provide an automated pathway analysis method for large database of solid Ionics and a dopant predictor to easily monitor the site substitution of the lattice[1]. In this work, local coordination environment characterization and classification has been introduced to further understand the interstitial site and atomic sites in the crystal structure and to improve the predicted stability of modified lattice. The local coordination number evaluation is implemented based bond valence theory [2] with proofed credibility after benchmarked with most reported methods[3]. Over 35000 atomic sites have been analysed from over 7000 high quality structure obtained from ICSD database. Machine learning has been applied to divide the local environment into over 60 defined polyhedral environments. The polyhedral environments are further explored to understand the interstitial sites in ionic pathway.

Coordination environment analysis for interstitial sites in solid-state ionic conductors.

Grain boundaries (GBs) widely exist in two-dimensional (2D) and three-dimensional (3D) materials in experiment, which significantly affect the thermoelectric performance because of the scattering effect on the transport of both electron and phonon. Motivated by the research progress in 3D SnSe [Zhao et al. Science, 2018, 360, 778], we have systematically studied the GBs in a SnSe monolayer including their stable geometric configurations, the effect of GBs on the thermoelectric properties, and the strategies to improve the performance by using first principles calculations combined with semiclassical Boltzmann theory. We find that the GBs increase the potential energy barrier of carriers and decrease the valley degeneracy of the conducting bands, leading to the reduction of Seebeck coefficient, as compared to that of the pristine SnSe sheet. We further demonstrate that, the trapping gap states are effectively eliminated or reduced by doping germanium or silicon, leading to the enhanced electrical conductivity and reduced thermal conductivity, thus the thermoelectric performance of the SnSe sheet with GBs is synergistically enhanced. These findings shed lights on developing practical strategies for modulating the thermoelectric performance of 2D polycrystalline sheets.

The effect of grain boundary on the thermoelectric performance of 2D materials: a case study of monolayer SnSe

Thermoelectric materials convert heat to electric power, and their performance quantified by the figure of merit, zT = (α2σ/κ)T. The data-driven screening combined with high-throughput calculations efficiently helps to find novel thermoelectric materials. The transport descriptors are one of the key factors to discover efficient thermoelectrics from the materials database. In our study, using a data-driven screening, we selected few potential candidates with trigonal ABX2 family and calculate the charge transport properties from first principles. Our results suggest that carrier scattering by ionized impurities and polar optical phonons are dominate in the electronic transport contrary to the often assumed acoustic-phonon-dominated scattering. We have derived new transport descriptors for the carrier mobility and the thermoelectric power factor. And along with thermal conductivity calculations using Peierls and interbranch coherent contributions, we conclude that the selected trigonal ABX2 family as potential thermoelectrics.

Data Driven Thermoelectrics: Electronic transport descriptors

Transition metal dichalcogenides exhibit polymorphs, such as 2H (trigonal coordination) and 1T (octahedral coordination) phases, which tends to have semiconducting and metallic nature, respectively. The distorted octahedral structures (Metastable 1T′-phase) show semi-metallic nature have immense interest for various applications in spintronics, energy harvesting and quantum devices etc. However, high-quality metastable 1T′-TMD synthesis without intercalation and external bias remains a great challenge. We have reported a general synthetic method for the large-scale preparation of metastable 1T′-phase TMDs and revealed the existence of superconductivity at TC = 8.6 K without any external gate bias, intercalation and pressure in 1T′-WS2. And have shown the thickness dependent and 2D-superconductivity features in our 1T′-WS2.

Low temperature electrical transport of metastable 1T′-WS2

Metal additive manufacturing is an invaluable part of robust and on-demand supply chains of the future. With its ability to consolidate material to high density into geometrically complex structures, selective laser melting (SLM) is a powerful tool for the fabrication of high strength, lightweight components. A limited pool of qualified alloys remains to be an impediment to greater adoption. Martensitic steels are a class of alloys that see widespread use in industries for tooling and highly stressed load bearing parts. However, their presence in the literature remains sparse in comparison to several workhorse steel alloys. In this work, we discuss the print optimization for this class of steels, including optimization of laser power, scanning speed, hatch distance and platform heating temperature. We investigate the general microstructural features of these steels, which is dominated by post-solidification displacive martensitic transformation and its response to cyclic thermal cycles in SLM. We demonstrate that martensitic high strength steels can be processed by SLM to have excellent mechanical properties in the as-printed state. Greater understanding of the SLM processing of this class of steels will enable the fabrication of stronger and lighter parts for increasingly demanding applications.

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Deep learning for accurate predictions of alloy stability

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