arXiv subjects

Xing-Yu Ma

Publications and source records attributed to Xing-Yu Ma.

12 recordsLinked to original sources

arXiv2023

Inverse Lithography Physics-informed Deep Neural Level Set for Mask Optimization

As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high printability in the lithography process. Recently, level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution, showcasing its powerful pattern fidelity, especially in advanced process. However, massive computational time consumption of ILT limits its applicability to mainly correcting partial layers and hotspot regions. Deep learning (DL) methods have shown great potential in accelerating ILT. However, lack of domain knowledge of inverse lithography limits the ability of DL-based algorithms in process window (PW) enhancement and etc. In this paper, we propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization. This approach utilizes level set based-ILT as a layer within the DL framework and iteratively conducts mask prediction and correction to significantly enhance printability and PW in comparison with results from pure DL and ILT. With this approach, computation time is reduced by a few orders of magnitude versus ILT. By gearing up DL with knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.

eess.IV↗

arXiv2023

Controllable spin splitting in 2D Ferroelectric few-layer ${$γ}$-GeSe

$γ$-GeSe is a new type of layered bulk material that was recently successfully synthesized. By means of density functional theory first-principles calculations, we systematically studied the physical properties of two-dimensional (2D) few-layer $γ$-GeSe. It is found that few-layer $γ$-GeSe are semiconductors with band gaps decreasing with increasing layer number; and 2D $γ$-GeSe with layer number $n \geq 2$ are ferroelectric with rather low transition barriers, consistent with the sliding ferroelectric mechanism. Particularly, spin-orbit coupling induced spin splitting is observed at the top of the valence band, which can be switched by the ferroelectric reversal; furthermore, their negative piezoelectricity also enables the regulation of spin splitting by strain. Finally, excellent optical absorption was also revealed. These intriguing properties make 2D few-layer $γ$-GeSe promising in spintronic and optoelectric applications.

cond-mat.mtrl-sci↗

arXiv2022

Large kagome family candidates with topological superconductivity and charge density waves

A group of newly discovered non-magnetic metal kagome structures AV3Sb5 (A=K, Rb, Cs) have aroused widespread interest in experiment and theory due to their unusual charge density wave (CDW) and intertwined superconductivity. However, they all possess weak electron-phonon coupling (EPC) and low superconducting transition temperature. Here, we performed high-throughput first-principles calculations on novel kagome candidates with AV3Sb5 prototype structure, and proposed 24 dynamically stable novel kagome metals. The calculation based on Bardeen-Cooper-Schrieffer theory shows that most of these metals are superconductors with much stronger EPC than the reported AV3Sb5 materials, and their superconducting transition temperatures Tc is between 0.3 and 5.0K. Additionally, several compounds, such as KZr3Pb5 with the highest Tc, are identified as Z2 topological metals with clear Dirac cone topological surface states near Fermi level. NaZr3As5 is shown to have possible CDW phases. Our results provide rich platforms for exploring various new physics with the prototype kagome structure, in which the coexistence of superconductivity and nontrivial topological nature provides promising insights on the discovery of topological superconductors.

cond-mat.supr-con↗

arXiv2021

High-Efficient ab initio Bayesian Active Learning Method and Applications in Prediction of Two-dimensional Functional Materials

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and unbalanced distribution of target property. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate prediction of desired functional materials with the ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3,119) of two-dimensional hexagonal binary compounds with unbalanced materials property, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of possible candidates in comparison to the random search. This approach shows enormous advantages for the cases with unbalanced distributions of target property. It can be readily applied to seek for a broad range of advanced materials.

cond-mat.mtrl-sci↗

arXiv2021

The atlas of ferroicity in two dimensional MGeX3 family: room-temperature ferromagnetic half metals and unexpected ferroelectricity and ferroelasticity

Two-dimensional (2D) ferromagnetic and ferroelectric materials attract unprecedented attention due to the spontaneous-symmetry-breaking induced novel properties and multifarious potential applications. Here we systematically investigate a large family (148) of 2D MGeX3 (M = metal elements, X = O/S/Se/Te) by means of the high-throughput first-principles calculations, and focus on their possible ferroic properties including ferromagnetism, ferroelectricity, and ferroelasticity. We discover eight stable 2D ferromagnets including five semiconductors and three half-metals, 21 2D antiferromagnets, and 11 stable 2D ferroelectric semiconductors including two multiferroic materials. Particularly, MnGeSe3 and MnGeTe3 are predicted to be room-temperature 2D ferromagnetic half metals with Tc of 490 and 308 K, respectively. It is probably for the first time that ferroelectricity is uncovered in 2D MGeX3 family, which derives from the spontaneous symmetry breaking induced by unexpected displacements of Ge-Ge atomic pairs, and we also reveal that the electric polarizations are in proportion to the ratio of electronegativity of X and M atoms, and IVB group metal elements are highly favored for 2D ferroelectricity. Magnetic tunnel junction and water-splitting photocatalyst based on 2D ferroic MGeX3 are proposed as examples of wide potential applications. The atlas of ferroicity in 2D MGeX3 materials will spur great interest in experimental studies and would lead to diverse applications.

cond-mat.mtrl-sci↗

arXiv2021

Calculated Effects of Vacancy and Ti-doping in 2D Janus MoSSe for Photocatalysis

Two-dimensional (2D) Janus transitional metal dichalcogenides (TMDCs) have great potential for photocatalytic water splitting due to their novel properties induced by the unique out-of-plane asymmetric structures. 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 are effective attractions between the substituted or adsorbed Ti atoms and S/Se vacancies. The Ti adatoms dramatically extend the light absorption range to infrared region. The S/Se vacancies coexisting with Ti adatoms will modulate the transition of photo-excited electrons, thereby enhancing the sunlight absorption. The Ti adatoms either existing alone or coexisting with vacancies introduce smaller lattice distortion compared to substituted Ti atoms and these Ti adatoms induce smaller effective mass of charge carriers. The configuration of S vacancy coexisting with Ti adatoms on Se-surface exhibits the most significant synergistic effects and best overall photocatalytic performance. Our work reveals the mechanism and effects induced by doping and vacancies coexisting in 2D Janus TMDCs, also propose a new practical strategy to improve the performance of 2D photocatalysts.

cond-mat.mtrl-sci↗

arXiv2020

Kagome quantum anomalous Hall effect with high Chern number and large band gap

Due to the potential applications in the low-power-consumption spintronic devices, the quantum anomalous Hall effect (QAHE) has attracted tremendous attention in past decades. However, up to now, QAHE was only observed experimentally in topological insulators with Chern numbers C= 1 and 2 at very low temperatures. Here, we propose three novel two-dimensional stable kagome ferromagnets Co3Pb3S2, Co3Pb3Se2and Co3Sn3Se2that can realize QAHE with high Chern number of |C|=3. Monolayers Co3Pb3S2, Co3Pb3Se2 and Co3Sn3Se2 possess the large band gap of 70, 77 and 63 meV with Curie temperature TC of 51, 42 and 46 K, respectively. By constructing a heterostructure Co3Sn3Se2/MoS2, its TC is enhanced to 60 K and the band gap keeps about 60 meV due to the tensile strain of 2% at the interface. For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level. Our results provide new topological nontrivial systems of kagome ferromagnetic monolayers and heterostructrues possessing QAHE with high Chern number |C| = 3 and large band gaps.

cond-mat.mtrl-sci↗

arXiv2020

Voting Data-Driven Regression Learning for Discovery of Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By an unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables to make precise predictions for targeted functional materials.

cond-mat.mtrl-sci↗

arXiv2020

Large Family of Two-Dimensional Ferroelectric Metals Discovered via Machine Learning

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of 'ferroelectric metal', however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2,964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.

cond-mat.mtrl-sci↗

arXiv2020

Toward understanding physical origin of 2175Å extinction bump in interstellar medium

The 2175 Å ultraviolet (UV) extinction bump in interstellar medium (ISM) of the Milky Way was discovered in 1965. After intensive exploration of more than a half century, however, its exact origin still remains a big conundrum that is being debated. Here we propose a mixture model by which the extinction bump in ISM is argued possibly relevant to the clusters of hydrogenated T-carbon (HTC) molecules (C40H16) that have intrinsically a sharp absorption peak at the wavelength 2175 Å. By linearly combining the calculated absorption spectra of HTC mixtures, graphite, MgSiO3 and Fe2SiO4, we show that the UV extinction curves of optional six stars can be nicely fitted. This present work poses an alternative explanation toward understanding the physical origin of the 2175 Å extinction bump in ISM of the Milky Way.

astro-ph.GA↗

arXiv2019

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning

Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive dataset generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5,000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.

cond-mat.mtrl-sci↗

arXiv2019

Carboneyane: A nodal line topological carbon with sp-sp2-sp3 chemical bonds

A structurally stable carbon allotrope with plentiful topological properties is predicted by means of first-principles calculations. This novel carbon allotrope possesses the simple space group C2/m, and contains simultaneously sp, sp2 and sp3 hybridized bonds in one structure, which is thus coined as carboneyane. The calculations on geometrical, vibrational, and electronic properties reveal that carboneyane, with good ductility and a much lower density 1.43 g/cm3, is a topological metal with a pair of nodal lines traversing the whole Brillouin zone, such that they can only be annihilated in a pair when symmetry is preserved. The symmetry and topological protections of the nodal lines as well as the associated surface states are discussed. By comparing its x-ray diffraction pattern with experimental results, we find that three peaks of carboneyane meet with the detonation soot. On account of the fluffy structure, carboneyane is shown to have potential applications in areas of storage, adsorption and electrode materials.

cond-mat.mtrl-sci↗