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| 1 | Towards efficient data exchange and sharing for big-data driven materials science:metadata and data formats显示文摘With big-data driven materials research,the new paradigm of materials science,sharing and wide accessibility of data are becoming crucial aspects.Obviously,a prerequisite for data exchange and big-data analytics is standardization,which means using consistent and unique conventions for,e.g.,units,zero base lines,and file formats.There are two main strategies to achieve this goal.One accepts the heterogeneous nature of the community,which comprises scientists from physics,chemistry,bio-physics,and materials science,by complying with the diverse ecosystem of computer codes and thus develops“converters”for the input and output files of all important codes.These converters then translate the data of each code into a standardized,codeindependent format.The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs,outputs,and restart files,directly into the same code-independent format.In this perspective paper,we present both strategies and argue that they can and should be regarded as complementary,if not even synergetic.The represented appropriate format and conventions were agreed upon by two teams,the Electronic Structure Library(ESL)of the European Center for Atomic and Molecular Computations(CECAM)and the NOvel MAterials Discovery(NOMAD)Laboratory,a European Centre of Excellence(CoE).A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations. | Luca M.Ghiringhelli Christian Carbogno Sergey Levchenko Fawzi Mohamed Georg Huhs Martin Luders Micael Oliveira Matthias Scheffler | 2017 | npj Computational Materials2017，，1： | 5 |
| 2 | Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition显示文摘A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) compounds.Its aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation energy.Here,we present a summary of the top-three ranked ML approaches.The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials.The second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression model.The third-place model employed the smooth overlap of atomic position representation with a neural network.The Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance.Ensembling relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy. | Christopher Sutton Luca M.Ghiringhelli Takenori Yamamoto Yury Lysogorskiy Lars Blumenthal Thomas Hammerschmidt Jacek R.Golebiowski Xiangyue Liu Angelo Ziletti Matthias Scheffler | 2019 | npj Computational Materials2019，，1： | 2 |
| 3 | The NOMAD Artificial-Intelligence Toolkit:turning materials-science data into knowledge and understanding显示文摘We present the Novel-Materials-Discovery(NOMAD)Artificial-Intelligence(AI)Toolkit,a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable,accessible,interoperable,and reusable(FAIR)data.The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive,the largest database of materials-science data worldwide,as well as locally stored,users’owned data.The NOMAD Oasis,a local,stand-alone server can be also used to run the AI Toolkit.By using Jupyter notebooks that run in a web-browser,the NOMAD data can be queried and accessed;data mining,machine learning,and other AI techniques can be then applied to analyze them.This infrastructure brings the concept of reproducibility in materials science to the next level,by allowing researchers to share not only the data contributing to their scientific publications,but also all the developed methods and analytics tools.Besides reproducing published results,users of the NOMAD AI toolkit can modify the Jupyter notebooks toward their own research work. | Luigi Sbailò Ádám Fekete Luca M.Ghiringhelli Matthias Scheffler | 2022 | npj Computational Materials2022，，1： | 1 |
| 4 | Finding predictive models for singlet fission by machine learning显示文摘Singlet fission(SF),the conversion of one singlet exciton into two triplet excitons,could significantly enhance solar cell efficiency.Molecular crystals that undergo SF are scarce.Computational exploration may accelerate the discovery of SF materials.However,many-body perturbation theory(MBPT)calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening.We use the sure-independence-screening-and-sparsifying-operator(SISSO)machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons(PAH101).SISSO generates models by iteratively combining physical primary features.The best models are selected by linear regression with cross-validation.The SISSO models successfully predict the SF driving force with errors below 0.2 eV.Based on the cost,accuracy,and classification performance of SISSO models,we propose a hierarchical materials screening workflow.Three potential SF candidates are found in the PAH101 set. | Xingyu Liu Xiaopeng Wang Siyu Gao Vincent Chang Rithwik Tom Maituo Yu Luca M.Ghiringhelli Noa Marom | 2022 | npj Computational Materials2022，，1： | 1 |
| 5 | Numerical quality control for DFT-based materials databases显示文摘Electronic-structure theory is a strong pillar of materials science.Many different computer codes that employ different approaches are used by the community to solve various scientific problems.Still,the precision of different packages has only been scrutinized thoroughly not long ago,focusing on a specific task,namely selecting a popular density functional,and using unusually high,extremely precise numerical settings for investigating 71 monoatomic crystals^(1).Little is known,however,about method- and code-specific uncertainties that arise under numerical settings that are commonly used in practice.We shed light on this issue by investigating the deviations in total and relative energies as a function of computational parameters.Using typical settings for basis sets and k-grids,we compare results for 71 elemental^(1) and 63 binary solids obtained by three different electronic-structure codes that employ fundamentally different strategies.On the basis of the observed trends,we propose a simple,analytical model for the estimation of the errors associated with the basis-set incompleteness.We cross-validate this model using ternary systems obtained from the Novel Materials Discovery (NOMAD) Repository and discuss how our approach enables the comparison of the heterogeneous data present in computational materials databases. | Christian Carbogno Kristian Sommer Thygesen Björn Bieniek Claudia Draxl Luca M.Ghiringhelli Andris Gulans Oliver T.Hofmann Karsten W.Jacobsen Sven Lubeck Jens Jørgen Mortensen Mikkel Strange Elisabeth Wruss Matthias Scheffler | 2022 | npj Computational Materials2022，，1： | 0 |
| 6 | Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy显示文摘Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images. | Andreas Leitherer Byung Chul Yeo Christian H.Liebscher Luca M.Ghiringhelli | 2023 | npj Computational Materials2023，，1： | 0 |
| 7 | Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence显示文摘Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications,including superconductivity,catalysis,and thermoelectricity.Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data.For such applications,reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed.Here,we present a general,data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis.We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values.By extracting the most influential material properties from this model,we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials. | Thomas A.R.Purcel Matthias Scheffler Luca M.Ghiringhelli Christian Carbogno | 2023 | npj Computational Materials2023，，1： | 0 |
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