jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonathan Schmidt | Noah Hoffmann | Hai‐Chen Wang | Pedro Borlido | Pedro J. M. A. Carriço | Tiago F. T. Cerqueira | Silvana Botti | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2022-10-02)
Field: Materials Science — Materials Chemistry

Large-scale machine-learning-assisted exploration of the whole materials space Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to perform machine-learning assisted high-throughput materials searches including 2500 binary and ternary structure prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and ~150000 compounds with a distance of less than 50 meV/atom from the hull. Combining again machine learning and ab-initio methods, we finally evaluate the discovered materials for applications as superconductors, superhard materials, and we look for candidates with large gap deformation potentials, finding several compounds with extreme values of these properties. Ternary operation ; Chemical stability ; Convex hull ; Chemical space ; Ab initio ; Stability (learning theory) ; Computer science ; Binary number ; Machine learning ; Generalization ; Artificial intelligence ; Materials science ; Regular polygon ; Mathematics ; Physics ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Ross Drummond | Matthew C. Turner | Stephen Duncan
Source: IEEE Transactions on Neural Networks and Learning Systems (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Reduced-Order Neural Network Synthesis With Robustness Guarantees In the wake of the explosive growth in smartphones and cyber-physical systems, there has been an accelerating shift in how data are generated away from centralized data toward on-device-generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware-limited, devices to improve user privacy, reduce latency, and be more energy efficient. However, our understanding of how these device-orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input-output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semidefinite program that minimizes the worst case approximation error with respect to the larger network. Worst case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g., pruning, is the inclusion of the worst case approximation error directly within the training cost function, which should add robustness to out-of-sample data points. Numerical examples highlight the potential of the proposed approach. The overriding goal of this article is to generalize recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases. Robustness (evolution) ; Computer science ; Artificial neural network ; Function approximation ; Approximation error ; Algorithm ; Machine learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Taruto Atsumi | Kosei Sato | Yudai Yamaguchi | Masato Hamaie | Risa Yasuda | Naoto Tanibata | Hayami Takeda | Masanobu Nakayama | Masayuki Karasuyama | Ichiro Takeuchi
Source: physica status solidi (b) (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids Materials informatics has attracted significant attention toward the efficient discovery and development of new functional materials. Machine‐learning regression techniques have often been used to establish a link between properties. In this respect, precise and rational regression is contingent on the choice of descriptors used to represent both composition and structural information. The usage of structure‐derived descriptors restricts the prediction range to the registered materials in the crystal structure database owing to the structural information requirements. Conversely, machine‐learning regression based only on compositional descriptors is free from this restriction, despite the fact that the prediction performance may diminish. Herein, their prediction performance is improved using compositional descriptors with histograms, and detailed surveys are performed on their ability to extrapolate. The proposed model achieves a high prediction accuracy based on the area under the receiver operating characteristic (ROC) curve (specifically, AUC &gt; 0.9). Stability (learning theory) ; Machine learning ; Computer science ; Predictive modelling ; Artificial intelligence ; Regression ; Data mining ; Materials informatics ; Receiver operating characteristic ; Range (aeronautics) ; Materials science ; Health informatics ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Haoyu Yin | Yizhe Chen | Xiaonan Wang
Source: Matter (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Deep learning-based density functionals empower AI for materials Density functional theory ; Computer science ; Process (computing) ; Characterization (materials science) ; Economic shortage ; Artificial intelligence ; Experimental data ; Deep learning ; Machine learning ; Artificial neural network ; Mathematics ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Kangming Li | Brian DeCost | Kamal Choudhary | Michael T. Greenwood | Jason Hattrick‐Simpers
Source: arXiv (Cornell University) (preprint, 2022-10-24)
Field: Materials Science — Materials Chemistry

A critical examination of robustness and generalizability of machine learning prediction of materials properties Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score may not imply good generalization of performance. Here we show that ML models trained on the Materials Project 2018 (MP18) dataset can have severely degraded prediction performance on new compounds in the Materials Project 2021 (MP21) dataset. We document performance degradation in graph neural networks and traditional descriptor-based ML models for both quantitative and qualitative predictions. We find the source of the predictive degradation is due to the distribution shift between the MP18 and MP21 versions. This is revealed by the uniform manifold approximation and projection (UMAP) of the feature space. We then show that the performance degradation issue can be foreseen using a few simple tools. Firstly, the UMAP can be used to investigate the connectivity and relative proximity of the training and test data within feature space. Secondly, the disagreement between multiple ML models on the test data can illuminate out-of-distribution samples. We demonstrate that the simple yet efficient UMAP-guided and query-by-committee acquisition strategies can greatly improve prediction accuracy through adding only 1~\% of the test data. We believe this work provides valuable insights for building materials databases and ML models that enable better prediction robustness and generalizability. Generalizability theory ; Robustness (evolution) ; Computer science ; Machine learning ; Artificial intelligence ; Benchmark (surveying) ; Artificial neural network ; Feature engineering ; Data mining ; Feature vector ; Test data ; Deep learning ; Statistics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: A A Thanush | P Chitra | J Kasinath | Ritesh Prakash
Source: IOP Conference Series Materials Science and Engineering (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Atmospheric corrosion rate prediction of low-alloy steel using machine learning models Abstract Corrosion mitigation is one of the indispensable needs in many industries and is currently being pursued by various methods like surface modification, corrosion inhibitor addition, and cathodic protection systems. Corrosion rate prediction can help in designing alloys with an optimized composition of materials such that it has a lower corrosion rate in the environment where they are exposed. Corrosion rate prediction can also help the manufacturers to plan the replacement of the sample used in advance. Machine learning, which is the science of making machines learn without being explicitly programmed and without using pre-determined equations, can help overcome challenges in predicting corrosion of various materials under a variety of environmental conditions. In this paper, three machine learning algorithms namely Support Vector Regression, Multiple Linear Regression, and Random Forest Regression are used to develop a Hybrid model to predict the corrosion rate of materials. Feature reduction is performed after feature importance calculation using Random Forest Regression model. The accuracy of the developed models were calculated using r 2 scores as an evaluation metric for different train-test split ratios. The input data for various conditions such as open, sheltered, coastal. Etc. are fed to the model and the performance was evaluated. The results show that the proposed Hybrid model outperforms the other baseline approaches with an accuracy of 91.46%, for predicting corrosion rate of materials. Corrosion ; Random forest ; Support vector machine ; Predictive modelling ; Metric (unit) ; Linear regression ; Computer science ; Cathodic protection ; Regression analysis ; Machine learning ; Regression ; Kriging ; Feature (linguistics) ; Artificial intelligence ; Process engineering ; Materials science ; Metallurgy ; Engineering ; Mathematics ; Statistics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Eng Hock Lee | Wei Jiang | Hussain Alsalman | Tony Low | Vladimir Cherkassky
Source: Physical Review Materials (article, 2022-04-14)
Field: Materials Science — Materials Chemistry

Methodological framework for materials discovery using machine learning Traditionally, materials discovery has been guided by basic physical rules, and such rules embody the basic understanding of the physical characteristics of interest of the material. However, the discovery of physical rules remains a challenging task due to the inherent difficulty in recognizing patterns in the high-dimensional and highly nonuniform distributed materials space. The standard data analytics approach using machine learning (ML) may fall short in producing meaningful results due to fundamental differences between the underlying assumptions and goals of ML vs materials discovery. ML is mainly focused on estimating complex black-box predictive models (that are nonlinear and multivariate), whereas in materials discovery, the goal is to come up with interpretable data-driven physical rules. Here, we attempt to tackle this problem by proposing a robust data analytics framework that allows us to derive basic physical rules from data. We introduce the concept of global and local modeling, utilizing both supervised and unsupervised learning, for highly nonuniformly distributed materials data. To enhance the model interpretation, we also introduce a model-independent interpretation technique to assist human experts in extracting useful physical rules. The proposed framework for extracting data-derived physical rules at the global and local level is illustrated using two case studies: (1) classification of van der Waals (vdW) and non-vdW (nvdW) materials and (2) classification of wide bandgap and non-wide bandgap vdW materials. Computer science ; Knowledge extraction ; Machine learning ; Analytics ; Interpretation (philosophy) ; Artificial intelligence ; Task (project management) ; Data science ; Black box ; Data analysis ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Wei Li | Lian-Chun Long | Jingyi Liu | Yang Yang
Source: Acta Physica Sinica (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Classification of magnetic ground states and prediction of magnetic moments of inorganic magnetic materials based on machine learning Magnetic materials are important basic materials in the information age. Different magnetic ground states are the prerequisite for the wide application of magnetic materials, among which the ferromagnetic ground state is a key requirement for future high-performance magnetic materials. In this paper, machine learning is used to study the classification of ferromagnetic, antiferromagnetic, ferrimagnetic and paramagnetic ground states of inorganic magnetic materials and the prediction of magnetic moments of inorganic ferromagnetic materials. We obtain 98888 inorganic magnetic materials data from the Materials Project database, containing material ids, chemical formulae, CIF files, magnetic ground states and magnetic moments, and extract 582 elemental and structural features for the inorganic magnetic materials by using Matminer. We design a two-step feature selection method. In the first step, RFECV is used to evaluate material features one by one to remove redundant features without degrading the model accuracy. In the second step, we rank the material features to further refine and select the most important material features for the model, and 20 material features are selected for the classification of magnetic ground states and the prediction of magnetic moments, respectively. Among the selected material features, it is found that the electronegativity, the atomic own magnetic moment and the number of unfilled electrons in the atomic peripheral orbitals all make important contributions to the classification of magnetic ground states and the prediction of magnetic moments. We build a magnetic ground state classification model and a magnetic moment prediction model by using the random forest, and quantitatively evaluate the machine learning models by using the 10-fold cross-validation approach, and the results show that the constructed machine learning models has sufficient accuracy and generalization capability. In the test set, the magnetic ground state classification model has an accuracy of 85.23%, a precision of 85.18%, a recall of 85.04%, and an F1 score of 85.24%; the magnetic moment prediction model has a goodness-of-fit of 91.58% and an average absolute error of 0.098 μ&lt;sub&gt;B&lt;/sub&gt; per atom. This study provides a new method and choice for high-throughput classification and screening of magnetic ground states of inorganic magnetic materials and predicting the magnetic moment of ferromagnetic materials. Magnetic moment ; Paramagnetism ; Ferrimagnetism ; Ground state ; Condensed matter physics ; Ferromagnetism ; Materials science ; Magnetic susceptibility ; Electronegativity ; Artificial intelligence ; Magnetization ; Computer science ; Physics ; Magnetic field ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Alexander E. Mayer | Polina N. Mayer | M V Lekanov | Boris A. Panchenko
Source: Metals (article, 2022-12-15)
Field: Materials Science — Materials Chemistry

Incipience of Plastic Flow in Aluminum with Nanopores: Molecular Dynamics and Machine-Learning-Based Description Incipience of plastic flow in nanoporous metals under tension is an important point for the development of mechanical models of dynamic (spall) fracture. Here we study axisymmetric deformation with tension of nanoporous aluminum with different shapes and sizes of nanopores by means of molecular dynamics (MD) simulations. Random deformation paths explore a sector of tensile loading in the deformation space. The obtained MD data are used to train an artificial neural network (ANN), which approximates both an elastic stress–strain relationship in the form of tensor equation of state and a nucleation strain distance function. This ANN allows us to describe the elastic stage of deformation and the transition to the plastic flow, while the following plastic deformation and growth of pores are described by means of a kinetic model of plasticity and fracture. The parameters of this plasticity and fracture model are identified by the statistical Bayesian approach, using MD curves as the training data set. The present research uses a machine-learning-based approximation of MD data to propose a possible framework for construction of mechanical models of spall fracture in metals. Plasticity ; Materials science ; Spall ; Deformation (meteorology) ; Mechanics ; Fracture (geology) ; Composite material ; Physics Microstructure and mechanical properties ; Nanopore and Nanochannel Transport Studies ; High-Velocity Impact and Material Behavior
Authors: Yong Yang | Han Jiang | Hua Zhai | Jiarui Chen | Qi Jiang | Shuyong Chen | Bao Li | Xin Cao
Source: Journal of Non-Crystalline Solids (article, 2022-09-29)
Field: Materials Science — Ceramics and Composites

Prediction and screening of glass properties based on high-throughput molecular dynamics simulations and machine learning Throughput ; Dynamics (music) ; Computer science ; High-throughput screening ; Molecular dynamics ; Artificial intelligence ; Machine learning ; Chemistry ; Bioinformatics ; Physics ; Biology ; Computational chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Cultural Heritage Materials Analysis
Authors: Dong Hyeon Mok | Jong Seung Kim | Seoin Back
Source: Chemistry of Materials (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Direction-Based Graph Representation to Accelerate Stable Catalyst Discovery To realize a renewable and sustainable energy cycle, there has been a lot of effort put into discovering catalysts with desired properties from a large chemical space. To achieve this goal, several screening strategies have been proposed, most of which require validation of thermodynamic stability and synthesizability of candidate materials via computationally intensive quantum chemistry or solid-state physics calculations. This problem can be overcome by reducing the number of calculations through machine learning methods, which predict target properties using unrelaxed crystal structures as inputs. However, numerical input representations of most of the previous models are based on either too specific (e.g., atomic coordinates) or too ambiguous (e.g., stoichiometry) information, practically inapplicable to energy prediction of unrelaxed initial structures. In this work, we develop a direction-based crystal graph convolutional neural network (D-CGCNN) with the highest accuracy toward formation energy predictions of the relaxed structures using the initial structures as inputs. By comparing with other approaches, we revealed correlations between crystal graph similarities and model performances, elucidating the origin of the improved accuracy of our model. We applied this model to the ongoing high-throughput virtual screening project, where the model discovered 1,725 stable materials from 15,318 unrelaxed structures by performing 3,966 structure optimizations (∼25%). Crystal structure prediction ; Computer science ; Graph ; Chemical space ; Representation (politics) ; Stability (learning theory) ; Theoretical computer science ; Crystal structure ; Statistical physics ; Machine learning ; Chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Hiroyuki Hayashi | Atsuto Seko | Isao Tanaka
Source: npj Computational Materials (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Recommender system for discovery of inorganic compounds Abstract A recommender system based on experimental databases is useful for the efficient discovery of inorganic compounds. Here, we review studies on the discovery of as-yet-unknown compounds using recommender systems. The first method used compositional descriptors made up of elemental features. Chemical compositions registered in the inorganic crystal structure database (ICSD) were supplied to machine learning for binary classification. The other method did not use any descriptors, but a tensor decomposition technique was adopted. The predictive performance for currently unknown chemically relevant compositions (CRCs) was determined by examining their presence in other databases. According to the recommendation, synthesis experiments of two pseudo-ternary compounds with currently unknown structures were successful. Finally, a synthesis-condition recommender system was constructed by machine learning of a parallel experimental data-set collected in-house using a polymerized complex method. Recommendation scores for unexperimented conditions were then evaluated. Synthesis experiments under the targeted conditions found two yet-unknown pseudo-binary oxides. Recommender system ; Computer science ; Binary number ; Set (abstract data type) ; Decomposition ; Ternary operation ; Binary classification ; Database ; Data mining ; Information retrieval ; Machine learning ; Chemistry ; Support vector machine ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications