Machine learning driven synthesis of few-layered WTe2

Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T' few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.

[1]  Cuntai Guan,et al.  Machine Learning Driven Synthesis of Carbon Dots with Enhanced Quantum Yields. , 2020, ACS nano.

[2]  F. Zhang,et al.  Electric-field induced structural transition in vertical MoTe2- and Mo1–xWxTe2-based resistive memories , 2018, Nature Materials.

[3]  Ji Feng,et al.  On the Quantum Spin Hall Gap of Monolayer 1T′‐WTe2 , 2016, Advanced materials.

[4]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[5]  Juqing Liu,et al.  A Universal Method for Preparation of Noble Metal Nanoparticle‐Decorated Transition Metal Dichalcogenide Nanobelts , 2014, Advanced materials.

[6]  M. Osada,et al.  Vapour–liquid–solid growth of monolayer MoS2 nanoribbons , 2018, Nature Materials.

[7]  Jianbin Xu,et al.  A Simple Method for Synthesis of High‐Quality Millimeter‐Scale 1T′ Transition‐Metal Telluride and Near‐Field Nanooptical Properties , 2017, Advanced materials.

[8]  Hai Xu,et al.  Controlled Growth of 1D MoSe2 Nanoribbons with Spatially Modulated Edge States. , 2017, Nano letters.

[9]  Giuseppe Iannaccone,et al.  Quantum engineering of transistors based on 2D materials heterostructures , 2018, Nature Nanotechnology.

[10]  Chuanghan Hsu,et al.  A library of atomically thin metal chalcogenides , 2018, Nature.

[11]  Yung Woo Park,et al.  Large-area synthesis of high-quality monolayer 1T’-WTe2 flakes , 2017, 2d materials.

[12]  Jakoah Brgoch,et al.  Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC. , 2017, Journal of the American Chemical Society.

[13]  Seong Kwang Hong,et al.  Machine learning-based self-powered acoustic sensor for speaker recognition , 2018, Nano Energy.

[14]  Feng Lin,et al.  Machine Learning Directed Search for Ultraincompressible, Superhard Materials. , 2018, Journal of the American Chemical Society.

[15]  K. Zhou,et al.  Phase‐Controlled Synthesis of Monolayer Ternary Telluride with a Random Local Displacement of Tellurium Atoms , 2019, Advanced materials.

[16]  Gwo-Ching Wang,et al.  Diffusion-Controlled Epitaxy of Large Area Coalesced WSe2 Monolayers on Sapphire. , 2018, Nano letters.

[17]  T. Buonassisi,et al.  Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges. , 2019, 1901.05801.

[18]  Anubhav Jain,et al.  Best practices in machine learning for chemistry , 2021, Nature Chemistry.

[19]  Jinlan Wang,et al.  Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning , 2018, Nature Communications.

[20]  Hyeonsik Cheong,et al.  Electrically Robust Single‐Crystalline WTe2 Nanobelts for Nanoscale Electrical Interconnects , 2018, Advanced science.

[21]  Robert Vajtai,et al.  Tellurium-Assisted Low-Temperature Synthesis of MoS2 and WS2 Monolayers. , 2015, ACS nano.

[22]  Hyunsoo Yang,et al.  Room‐Temperature Nanoseconds Spin Relaxation in WTe2 and MoTe2 Thin Films , 2018, Advanced science.

[23]  Di Wu,et al.  Imaging quantum spin Hall edges in monolayer WTe2 , 2018, Science Advances.

[24]  D. Cahill,et al.  Direct Synthesis of Large‐Scale WTe2 Thin Films with Low Thermal Conductivity , 2017 .

[25]  Suyeon Cho,et al.  Bandgap opening in few-layered monoclinic MoTe2 , 2015, Nature Physics.

[26]  Thomas J. Kempa,et al.  Substrate-directed synthesis of MoS2 nanocrystals with tunable dimensionality and optical properties , 2019, Nature Nanotechnology.

[27]  Zaiyao Fei,et al.  Edge conduction in monolayer $WTe_{2}$ , 2016, 1610.07924.

[28]  Ryan P. Adams,et al.  Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. , 2016, Nature materials.

[29]  Yao Zhang,et al.  Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning , 2020, Nature Communications.

[30]  Brian L. DeCost,et al.  On-the-fly closed-loop materials discovery via Bayesian active learning , 2020, Nature Communications.

[31]  Mohammad Rashidi,et al.  Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning. , 2018, ACS nano.

[32]  Eric S. Toberer,et al.  Empirical modeling of dopability in diamond-like semiconductors , 2018, npj Computational Materials.

[33]  Jun Lou,et al.  Vertical and in-plane heterostructures from WS2/MoS2 monolayers. , 2014, Nature materials.

[34]  M. Hersam,et al.  2D materials for quantum information science , 2019, Nature Reviews Materials.

[35]  Weisheng Zhao,et al.  Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy , 2018, Nano Research.

[36]  Takashi Taniguchi,et al.  Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices , 2018, Nature Communications.

[37]  Tonio Buonassisi,et al.  Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing , 2018, Joule.

[38]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[39]  Shengbai Zhang,et al.  MoS2 nanoribbons: high stability and unusual electronic and magnetic properties. , 2008, Journal of the American Chemical Society.

[40]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[41]  Peng Yu,et al.  Large‐Area and High‐Quality 2D Transition Metal Telluride , 2016, Advanced materials.

[42]  Vladan Stevanović,et al.  Computationally guided discovery of thermoelectric materials , 2017 .

[43]  Brian M. Bersch,et al.  Tungsten Ditelluride: a layered semimetal , 2015, Scientific Reports.

[44]  P. Cui,et al.  Fabrication of MoSe2 nanoribbons via an unusual morphological phase transition , 2017, Nature Communications.

[45]  Lukas Novotny,et al.  One-dimensional edge contacts to a monolayer semiconductor. , 2019, Nano letters.

[46]  Priya Vashishta,et al.  Defect Healing in Layered Materials: A Machine Learning-Assisted Characterization of MoS2 Crystal Phases. , 2019, The journal of physical chemistry letters.

[47]  Takashi Taniguchi,et al.  Electrically switchable Berry curvature dipole in the monolayer topological insulator WTe2 , 2018, Nature Physics.

[48]  Ting Yu,et al.  Morphology evolution of MoS2: From monodisperse nanoparticles to self-assembled nanobelts , 2016 .

[49]  Pinshane Y. Huang,et al.  High-mobility three-atom-thick semiconducting films with wafer-scale homogeneity , 2015, Nature.

[50]  Zheng Liu,et al.  Machine learning-guided synthesis of advanced inorganic materials , 2019, ArXiv.

[51]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[52]  Roger G. Melko,et al.  Machine learning phases of matter , 2016, Nature Physics.

[53]  Zachary W. Ulissi,et al.  Accelerated discovery of CO2 electrocatalysts using active machine learning , 2020, Nature.

[54]  Shengbai Zhang,et al.  MoS 2 Nanoribbons : High Stability and Unusual Electronic and Magnetic Properties , 2008 .

[55]  Leroy Cronin,et al.  Controlling an organic synthesis robot with machine learning to search for new reactivity , 2018, Nature.

[56]  Yao Ding,et al.  Polymer-confined growth of perforated MoSe2 single-crystals on N-doped graphene toward enhanced hydrogen evolution. , 2017, Nanoscale.

[57]  S. M. Moosavi,et al.  The Role of Machine Learning in the Understanding and Design of Materials , 2020, Journal of the American Chemical Society.

[58]  M. Scheffler,et al.  Insightful classification of crystal structures using deep learning , 2017, Nature Communications.

[59]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[60]  Euijoon Yoon,et al.  Single‐Crystalline Nanobelts Composed of Transition Metal Ditellurides , 2018, Advanced materials.

[61]  Conor P. Cullen,et al.  Synthesis of tungsten ditelluride thin films and highly crystalline nanobelts from pre-deposited reactants , 2020, Tungsten.

[62]  Liang Feng,et al.  Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials , 2019, Science Advances.