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Crystal structure prediction at finite temperatures

npj Computational Materials 2023年第1期9卷 306-313页

作者： ivan a.kruglov Alexey V.Yanilkin Yana Propad Arslan B.Mazitov Pavel Rachitskii Artem R.Oganov Moscow Institute of Physics and Technology 9 Institutsky laneDolgoprudny 141700Russia Dukhov Research Institute of Automatics(VNIIA) Moscow 127055Russia Emerging Technologies Research Center XPANCEODubai Investment Park FirstDubaiUnited Arab Emirates Skolkovo Institute of Science and Technology Skolkovo Innovation Center30-1 Bolshoy blvdMoscow 121205Russia.

Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was consideredintractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solvedthis problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensivecalculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum ofthe enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchableto zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction atfinite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with aforcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomicpotential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initioaccuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalentsolid (WB), an Earth-forming silicate MgSiO_(3) (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO_(2).

关键词： prediction structure Crystal

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Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence

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Science China Materials 2024年第12期67卷 3941-3947页

作者： ivan a.kruglov Liudmila A.Bereznikova Congwei Xie Dongdong Chu Ke Li Evgenii Tikhonov Abudukadi Tudi Arslan Mazitov Min Zhang Shilie Pan Zhihua Yang Research Center for Crystal Materials State Key Laboratory of Functional Materials and Devices for Special Environmental ConditionsXinjiang Key Laboratory of Functional Crystal MaterialsXinjiang Technical Institute of Physics and ChemistryChinese Academy of SciencesUrumqi 830011China Moscow Institute of Physics and Technology 9 Institutsky LaneDolgoprudny 141700Russia Emerging Technologies Research Center XPANCEOInternet CityEmmay TowerDubaiUnited Arab Emirates

Finding crystals with high birefringence(Δn),especially in deep-ultraviolet(DUV)regions,is important for developing polarization devices such as optical fiber *** materials are usually discovered using experimental techniques,which are costly and inefficient for a large-scale ***,we collected a database of crystal structures and their optical properties and trained atomistic line graph neural network to predict theirΔ*** estimate the level of confidence of the trained model on new data,D-optimality criterion was *** trained graph neural network,we searched for novel materials with highΔn in the Materials Project database and discovered two new DUV birefringent candidates:NaYCO_(3)F_(2) and SClO_(2)F,with highΔn values of 0.202 and 0.101 at 1064 nm,*** analysis reveals that strongly anisotropic units with various anions andπ-conjugated planar groups are beneficial for highΔn.

关键词： machine learning birefringence optical materials D-optimality

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Publisher Correction:Crystal structure prediction at finite temperatures

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npj Computational Materials 2023年第1期9卷 214-214页

作者： ivan a.kruglov Alexey V.Yanilkin Yana Propad Arslan B.Mazitov Pavel Rachitskii Artem R.Oganov Moscow Institute of Physics and Technology 9 Institutsky lane Dolgoprudny 141700 Russia Dukhov Research Institute of Automatics (VNIIA) Moscow 127055 Russia Emerging Technologies Research Center XPANCEO Dubai Investment Park First Dubai United Arab Emirates Skolkovo Institute of Science and Technology Skolkovo Innovation Center 30-1 Bolshoy blvd Moscow 121205 Russia

Correction to:npj Computational Materials https://***/10.1038/s41524-023-01120-6,published online 21 October 2023 Current caption to Fig.4 reads“P–T phase diagram of MgSiO_(3)”.The correct caption should be“P–T p... 详细信息

关键词： prediction diagram Crystal

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Exploring van der Waals materials with high anisotropy:geometrical and optical approaches

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Light(Science & Applications) 2024年第4期13卷 645-653页

作者： Aleksandr S.Slavich Georgy A.Ermolaev Mikhail K.Tatmyshevskiy Adilet N.Toksumakov Olga G.Matveeva Dmitriy V.Grudinin Kirill V.Voronin Arslan Mazitov Konstantin V.Kravtsov Alexander V.Syuy Dmitry M.Tsymbarenko Mikhail S.Mironov Sergey M.Novikov ivan kruglov Davit A.Ghazaryan Andrey A.Vyshnevyy Aleksey V.Arsenin Valentyn S.Volkov Kostya S.Novoselov Moscow Center for Advanced Studies Kulakova str.20Moscow 123592Russia. Emerging Technologies Research Center XPANCEOInternet CityEmmay TowerDubaiUnited Arab Emirates. Donostia International Physics Center(DIPC) Donostia/San-Sebastián 20018Spain. Institute of Materials Ecole Polytechnique Federale de Lausanne1015 LausanneSwitzerland. Department of Chemistry Lomonosov Moscow State UniversityMoscow 119991Russia. Laboratory of Advanced Functional Materials Yerevan State UniversityYerevan 0025Armenia. National Graphene Institute(NGI) University of ManchesterManchester M139PLUK. Department of Materials Science and Engineering National University of SingaporeSingapore 03-09 EASingapore. Institute for Functional Intelligent Materials National University of Singapore117544 SingaporeSingapore

The emergence of van der Waals(vdW)materials resulted in the discovery of their high optical,mechanical,and electronic anisotropic properties,immediately enabling countless novel phenomena and *** success inspired an intensive search for the highest possible anisotropic properties among vdW ***,the identification of the most promising among the huge family of vdW materials is a challenging quest requiring innovative ***,we suggest an easy-to-use method for such a survey based on the crystallographic geometrical perspective of vdW materials followed by their optical *** our approach,we found As2S3 as a highly anisotropic vdW *** demonstrates high in-plane optical anisotropy that is~20%larger than for rutile and over two times as large as calcite,high refractive index,and transparency in the visible range,overcoming the century-long record set by *** these benefits,As2S3 opens a pathway towards nextgeneration nanophotonics as demonstrated by an ultrathin true zero-order quarter-wave plate that combines classical and the Fabry–Pérot optical phase ***,our approach provides an effective and easy-to-use method to find vdW materials with the utmost anisotropic properties.

关键词： anisotropy anisotropic high

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非线性光学数据库:理论预测助力材料的快速发现

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Science China Materials 2023年第11期66卷 4473-4479页

作者：谢聪伟吉洪诺夫·叶夫格尼储冬冬吴梦凡克鲁格洛夫·伊万潘世烈杨志华 Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special EnvironmentsXinjiang Technical Institute of Physics&ChemistryCASUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

现代激光技术迫切需要能够通过二次谐波产生相干光的非线性光学材料.然而,只有很少一部分非中心对称晶体材料的非线性光学性质被实验或者理论研究,针对高性能非线性光学晶体材料的探索仍非常有限.本工作建立了迄今为止最大的计算非线性... 详细信息

现代激光技术迫切需要能够通过二次谐波产生相干光的非线性光学材料.然而,只有很少一部分非中心对称晶体材料的非线性光学性质被实验或者理论研究,针对高性能非线性光学晶体材料的探索仍非常有限.本工作建立了迄今为止最大的计算非线性光学晶体材料数据库,其中包含2354个非中心对称晶体结构,以及它们的倍频系数和带隙.本文提供了理论数据的计算细节和其与实验数据的对比验证,以及数据库的详细描述.该数据库的重要特点之一是它包含大量通过进化算法搜索发现的新的热力学稳定和亚稳定结构,这为寻找具有更好性能的新型非线性光学材料提供了可能性.

关键词： materials database high-throughput computation second harmonic generation response NLO materials data-mining

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