The work function is the key surface property that determines how much energy is required for an electron to escape the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. Here, we present a high-throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that we created from 3,716 bulk materials, including up to ternary compounds. The number of materials for which we calculated surface properties surpasses the previously largest database, the Materials Project, by a factor of $\sim$27. On the tail ends of the work function distribution we identify 34 and 56 surfaces with an ultra-low (<2 eV) and ultra-high (>7 eV) work function, respectively. Further, we discover that the $(100)$-Ba-O surface of BaMoO$_3$ and the $(001)$-F surface of Ag$_2$F have record-low (1.25 eV) and record-high (9.06 eV) steady-state work functions without requiring coatings, respectively. Based on this database we develop a physics-based approach to featurize surfaces and use supervised machine learning to predict the work function. We find that physical choice of features improves prediction performance far more than choice of model. Our random forest model achieves a mean absolute test error of 0.09 eV, which is more than 6 times better than the baseline and comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function ($\sim 10^5$ faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion, electronic applications, and contacts in 2-dimensional devices.
We performed ab-initio driven density functional theory-based high throughput computations to search for materials with low thermal conductivity and high thermal transport anisotropy. We shortlisted a pool of 429 stable ternary semiconductors from the Materials Project and obtained phonon thermal conductivity by solving the Boltzmann transport equation on 225 materials. We found the lowest thermal conductivity of 0.16 W/m-K in SbRbK 2 and 40 materials with a thermal conductivity lower than 1 W/m-K at 300 K. For anisotropic thermal transport, we have identified six materials with anisotropy lar…
Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datas…
Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface, and nano-particle properties. The practical realization of these opportunities requires systematic generation and classification of the relevant computational data by high-throughput methods. In this paper we present Aflow (Automatic Flow), a software framework for high-throughput calculation of crystal structure properties of alloys, intermetallics and inorganic compounds…
Recently, Sten Haastrup, Mikkel Strange, Mohnish Pandey, Thorsten Deilmann, Per S Schmidt, Nicki F Hinsche, Morten N Gjerding, Daniele Torelli, Peter M Larsen, Anders C Riis-Jensen, Jakob Gath, Karsten W Jacobsen, Jens Jrgen Mortensen, Thomas Olsen and Kristian S Thygesen [2D Mater. 5 (2018) 042002] introduced the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Unfortunately, the work conta…
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 material…
The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns
State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院金属基复合材料国家重点实验室）
Recent advances in two-dimensional (2D) magnetism have heightened interest in layered magnetic materials due to their potential for spintronics. In particular, layered semiconducting antiferromagnets exhibit intriguing low-dimensional semiconducting behavior with both charge and spin as carrier controls. However, synthesis of these compounds is challenging and remains rare. Here, we conducted firstprinciples based high-throughput search to screen potentially stable mixed metal phosphorous trichalcogenides (MM'P2X6, where M and M' are transition metals and X is a chalcogenide) that have a wide …
Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded …
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting…
The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graph-like representations of crystal structures ("crystal graphs"). Here, we develop an improved variant of the CGCNN model (iCGCNN) that outperforms the original by incorporating information of the Voronoi tessellated crystal structure, explicit 3-body correlations of neighboring constituent atoms, and an optimized chemical representation of interatomic bonds in the crystal graphs. We demonstrate the accu…
High-Throughput Discovery of Semimetallic Borophenes with Diverse Dirac States Via Transferable Tight-Binding Approach
School of Physics and Optoelectronics, Xiangtan University（湘潭大学物理与光电工程学院）；Hunan Provincial Key Laboratory of Computational Condensed Matter Physics and Quantum Materials Engineering（湖南省计算凝聚态物理与量子材料工程重点实验室）
Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here we present an integrated approach using a set of high-throughput alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys were evaluated for t…
High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structura…
Despite their extraordinary properties, electrides are still a relatively unexplored class of materials with only a few compounds grown experimentally. Especially for layered electrides, the current researches mainly focus on several isostructures of Ca2N with similar interlayer two-dimensional (2D) anionic electrons. An extensive screening for different layered electrides is still missing. Here, by screening materials with anionic electrons for the structures in Materials Project, we uncover 12 existing materials as new layered electrides. Remarkably, these layered electrides demonstrate comp…
For decades of catalysis research, the d-band center theory that correlates the d-band center and the adsorbate binding energy has successfully enabled the accelerated discovery of novel catalyst materials. Recent studies indicate that, on top of the d-band center value, the full consideration of the d-band shapes describing higher moments of the d-band as well as sp-band properties can help better capturing surface reactivity. However, the density-of-states (DOS) patterns themselves have never been used as a descriptor in combined computational-experimental studies. Here, we propose the full …
In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery, for example, it has alleviated an otherwise extremely complex and tedious process and has resulted in the development of several new drugs. Over the last decade, these automation techniques have begun being adapted in the chemical and materials community as well with the goal of exploring chemical space and pursuing the discovery and design of novel compounds …
We present a novel methodology to identify topologically non-trivial materials based on band inversion induced by spin-orbit coupling (SOC) effect. Specifically, we compare the density functional theory (DFT) based wavefunctions with and without spin-orbit coupling and compute the spin-orbit-spillage as a measure of band-inversion. Due to its ease of calculation, without any need for symmetry analysis or dense k-point interpolation, the spillage is an excellent tool for identifying topologically non-trivial materials. Out of 30000 materials available in the JARVIS-DFT database, we applied this…
Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening
Soochow University（苏州大学）；AI for Science Institute（人工智能科学研究院）；Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University（香港理工大学智能穿戴系统研究所）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院生物医学工程系）
The discovery of metals as catalytic centers for nitrogen reduction reactions has stimulated great enthusiasm for single-atom catalysts. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the high throughout first principles calculations, the doping engineering can effectively regulate the NRR performance of b-Sb monolayer. Especially, the origin of activated N2 is revealed from the perspective of the electronic structure of the active center. Among the 24 transition metal dopants, Re@Sb and Tc@Sb showed the best NRR catalytic…
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in part due to the advances in computational and experimental methods in obtaining thermal properties of materials. In this paper, we provide a current overview of some of the recent work and highlight the challenges and opportunities that are ahead of us in this field. In particular, we focus on the use of machine learning and high-throughput methods for scree…
Peking University（北京大学）；Eastern Institute of Technology（东方理工大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）；The University of Toledo（托莱多大学）；WuXi AppTec（药明康德）；Peng Cheng Laboratory（鹏城实验室）；Southern University of Science and Technology（南方科技大学）
As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for polarity measurement. However, current TLC analysis presents several problems, including the need for a large number of attempts to obtain suitable conditions, as well as irreproducibility due to non-standardization. Herein, we describe an automated experiment system for TLC analysis. This system is designed to conduct TLC analysis automatically, facilitating h…
We introduce the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Material properties are systematically calculated by state-of-the art density functional theory and many-body perturbation theory (G$_0\!$W$\!_0$ and the Bethe-Salpeter Equation for $\sim$200 materials) following a semi-automated workflow for maximal consistency and transparency. The C2DB is fully open and can be browsed onlin…
Ultra-high temperature ceramics, UHTCs, are a group of materials with high technological interest because their use in extreme environments. However, their characterization at high temperatures represents the main obstacle for their fast development. Obstacles are found from a experimental point of view, where only few laboratories around the world have the resources to test these materials under extreme conditions, and also from a theoretical point of view, where actual methods are extremely expensive. Here, a new theoretical high-throughput framework for the prediction of the thermoelastic p…
The rational design of two-dimensional piezoelectric materials has recently garnered great interest due to their increasing use in technological applications, including sensor technology, actuating devices, energy harvesting, and medical applications. Several materials possessing high piezoelectric response have been reported so far, but a high-throughput first-principles approach to estimate the piezoelectric potential of layered materials has not been performed yet. In this study, we systematically investigated the piezoelectric ($e_{11}$, $d_{11}$) and elastic (C$_{11}$ and C$_{12}$) proper…
High-Throughput Discovery of Chiral Carbon-Boron Semimetals with Weyl Topology and Phonon-Mediated Superconductivity
University of Science and Technology of China（中国科学技术大学）；Wuhan Institute of Technology（武汉工程大学）；Hefei University of Technology（合肥工业大学）；Hanhai Lingchao Intelligent Computing (Shanghai) Technology Services Co., Ltd.（瀚海凌潮智能计算（上海）科技有限公司）
Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial x-ray diffraction datasets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of x-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their c…
Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor
Northwestern Polytechnical University（西北工业大学）；CRISMAT, CNRS, Normandie Univ, ENSICAEN, UNICAEN（诺曼底大学，卡昂国立高等工程学院，法国国家科学研究中心材料研究化学实验室）；Thayer School of Engineering, Dartmouth College（达特茅斯学院塔克工程学院）；Institute Laue Langevin（朗之万研究所）；FZU - Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）；ShanghaiTech Laboratory for Topological Physics & School of Physical Science and Technology, ShanghaiTech University（上海科技大学物理科学与技术学院拓扑物理实验室）；Université de Lorraine, CNRS, IJL（洛林大学，法国国家科学研究中心，界面与层析实验室）
A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards…
Using finite-temperature phonon calculations and machine-learning methods, we calculate the mechanical stability of about 400 semiconducting oxides and fluorides with cubic perovskite structures at 0 K, 300 K and 1000 K. We find 92 mechanically stable compounds at high temperatures -- including 36 not mentioned in the literature so far -- for which we calculate the thermal conductivity. We demonstrate that the thermal conductivity is generally smaller in fluorides than in oxides, largely due to a lower ionic charge, and describe simple structural descriptors that are correlated with its magnit…
The discovery and optimization of phase-change and shape memory alloys remain a tedious and expensive process. Here a simple computational method is proposed to determine the ideal phase-change material for a given alloy composed of three elements. Using first-principles calculations, within a high-throughput framework, the ideal composition of a phase-change material between any two assumed phases can be determined. This ideal composition minimizes the interface strain during the structural transformation. Then one can target this ideal composition experimentally to produce compounds with low…
Transparent oxides are essential building blocks to many technologies, ranging from components in transparent electronics, transparent conductors, to absorbers and protection layers in photovoltaics and photoelectrochemical devices. However, thus far, it has been difficult to develop p-type oxides with wide band gap and high hole mobility; current state-of-art transparent p-type oxides have hole mobility in the range of < 10 cm$^2$/Vs, much lower than their n-type counterparts. Using high-throughput computational screening to guide the discovery of novel oxides with wide band gap and high hole…
