jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Douglas M. Reitz | Estela Blaisten‐Barojas
Source: Scientific Reports (article, 2019-01-24)
Field: Materials Science — Materials Chemistry

Simulating the NaK Eutectic Alloy with Monte Carlo and Machine Learning Combining atomistic simulations and machine learning techniques can expedite significantly the materials discovery process. We present an application of such methodological combination for the prediction of the melting transition and amorphous-solid behavior of the NaK alloy at the eutectic concentration. We show that efficient prediction of these properties is possible via machine learning methods trained on the topological local structural properties. The configurations resulting from Monte Carlo annealing of the NaK eutectic alloy are analyzed with topological attributes based on the Voronoi tessellation and using expectation-maximization clustering and Random Forest classification. We show that the Voronoi topological fingerprints make an accurate and fast prediction of the alloy thermal behavior by cataloguing the atomic configurations into three distinct phases: liquid, amorphous solid, and crystalline solid. Melting is found at 230 K by the sharp split of configurations classified as crystalline solid and as liquid. With the proposed metrics, an arrest-motion temperature is identified at 130-140 K through a top down clustering of the atomic configurations catalogued as amorphous solid. This statistical learning paradigm is not restricted to eutectic alloys or thermodynamics, extends the utility of topological attributes in a significant way, and harnesses the discovery of new material properties. Eutectic system ; Amorphous solid ; Voronoi diagram ; Monte Carlo method ; Materials science ; Cluster analysis ; Topological data analysis ; Computer science ; Alloy ; Statistical physics ; Machine learning ; Topology (electrical circuits) ; Artificial intelligence ; Algorithm ; Physics ; Crystallography ; Chemistry ; Mathematics ; Metallurgy ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Seongok Ryu | Yongchan Kwon | Woo Youn Kim
Source: arXiv (Cornell University) (preprint, 2019-03-20)
Field: Materials Science — Materials Chemistry

Uncertainty quantification of molecular property prediction with Bayesian neural networks Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predictions arisen from insufficient quality and quantity of training data. Here, we show that Bayesian neural networks are useful to quantify the uncertainty of molecular property prediction with three numerical experiments. In particular, it enables us to decompose the predictive variance into the model- and data-driven uncertainties, which helps to elucidate the source of errors. In the logP predictions, we show that data noise affected the data-driven uncertainties more significantly than the model-driven ones. Based on this analysis, we were able to find unexpected errors in the Harvard Clean Energy Project dataset. Lastly, we show that the confidence of prediction is closely related to the predictive uncertainty by performing on bio-activity and toxicity classification problems. Computer science ; Artificial neural network ; Uncertainty quantification ; Machine learning ; Property (philosophy) ; Variance (accounting) ; Bayesian probability ; Artificial intelligence ; Noise (video) ; Deep neural networks ; Bayesian network ; Bayesian inference ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: In Ho Cho
Source: International Journal for Numerical Methods in Engineering (article, 2019-07-26)
Field: Materials Science — Materials Chemistry

A framework for self‐evolving computational material models inspired by deep learning Summary There exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering. Computer science ; Artificial intelligence ; Deep learning ; Similarity (geometry) ; Index (typography) ; Process (computing) ; Scheme (mathematics) ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: R. A. Mohamed
Source: Materials Research Express (article, 2019-06-07)
Field: Materials Science — Polymers and Plastics

Prediction of AC conductivity for organic semiconductors based on artificial neural network ANN model This article presents a theoretical study conducted to model the experimental data of alternating–current (AC) conductivity for different organic semiconductors using an artificial neural network (ANN) model. The experimental data were utilized as inputs to an ANN model. ANNs were constructed to obtain the best performance. Mean squared errors (MSE) were calculated and they were found to present small values indicating excellent matching between the ANN results and the targets. A new equation was obtained to describe the behavior of AC conductivity of organic semiconductors as a function of frequency and temperature. This equation was applied to eight different samples of organic semiconductors and excellent results were obtained. Simulation and prediction for known values of experimental data were carried out as a testing step and the results show excellent agreement with the target values. The prediction of values that were not involved in the experimental data range is the main aim of this research. The predictions of these unknown values were carried out successfully and provided excellent results. Thus, the equation obtained in this work could be considered as a generalized tool for predicting the AC conductivity for any other organic material. This article opens up a new field of collaboration between experimental and theoretical researchers. Artificial neural network ; Organic semiconductor ; Conductivity ; Experimental data ; Range (aeronautics) ; Semiconductor ; Materials science ; Mean squared error ; Biological system ; Machine learning ; Artificial intelligence ; Computer science ; Mathematics ; Statistics ; Chemistry ; Physical chemistry ; Composite material Conducting polymers and applications ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Katsuhiko Ishiguro | Shin‐ichi Maeda | Masanori Koyama
Source: arXiv (Cornell University) (preprint, 2019-02-04)
Field: Materials Science — Materials Chemistry

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GNNs developed for molecular analysis, however, do not fit well on the training set, and their performance does not scale well with the complexity of the network. In this paper, we propose an auxiliary module to be attached to a GNN that can boost the representation power of the model without hindering with the original GNN architecture. Our auxiliary module can be attached to a wide variety of GNNs, including those that are used commonly in biochemical applications. With our auxiliary architecture, the performances of many GNNs used in practice improve more consistently, achieving the state-of-the-art performance on popular molecular graph datasets. Graph ; Computer science ; Voltage graph ; Null graph ; Power graph analysis ; Boosting (machine learning) ; Theoretical computer science ; Line graph ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Tang-Shi Yao | Cenyao Tang | Meng Yang | Kejia Zhu | Dayu Yan | Changjiang Yi | Zili Feng | Hechang Lei | Cheng-He Li | Le Wang | Lei Wang | Youguo Shi | Yujie Sun | Hong Ding
Source: Chinese Physics Letters (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Instruct Single Crystal Growth by Flux Method* Growth of high-quality single crystals is of great significance for research of condensed matter physics. The exploration of suitable growing conditions for single crystals is expensive and time-consuming, especially for ternary compounds because of the lack of ternary phase diagram. Here we use machine learning (ML) trained on our experimental data to predict and instruct the growth. Four kinds of ML methods, including support vector machine (SVM), decision tree, random forest and gradient boosting decision tree, are adopted. The SVM method is relatively stable and works well, with an accuracy of 81% in predicting experimental results. By comparison, the accuracy of laboratory reaches 36%. The decision tree model is also used to reveal which features will take critical roles in growing processes. Decision tree ; Support vector machine ; Ternary operation ; Computer science ; Gradient boosting ; Machine learning ; Artificial intelligence ; Boosting (machine learning) ; Tree (set theory) ; Tree diagram ; Random forest ; Phase diagram ; Data mining ; Phase (matter) ; Mathematics ; Physics ; Combinatorics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions
Authors: Taher Hajilounezhad | Zakariya A. Oraibi | Ramakrishna Surya | Filiz Bunyak | Matthew R. Maschmann | Prasad Calyam | Kannappan Palaniappan
Source:  (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning The parameter space of CNT forest synthesis is vast and multidimensional, making experimental and/or numerical exploration of the synthesis prohibitive. We propose a more practical approach to explore the synthesis-process relationships of CNT forests using machine learning (ML) algorithms to infer the underlying complex physical processes. Currently, no such ML model linking CNT forest morphology to synthesis parameters has been demonstrated. In the current work, we use a physics-based numerical model to generate CNT forest morphology images with known synthesis parameters to train such a ML algorithm. The CNT forest synthesis variables of CNT diameter and CNT number densities are varied to generate a total of 12 distinct CNT forest classes. Images of the resultant CNT forests at different time steps during the growth and self-assembly process are then used as the training dataset. Based on the CNT forest structural morphology, multiple single and combined histogram-based texture descriptors are used as features to build a random forest (RF) classifier to predict class labels based on correlation of CNT forest physical attributes with the growth parameters. The machine learning model achieved an accuracy of up to 83.5% on predicting the synthesis conditions of CNT number density and diameter. These results are the first step towards rapidly characterizing CNT forest attributes using machine learning. Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and properties. Carbon nanotube ; Computer science ; Nanotube ; Nanotechnology ; Materials science ; Artificial intelligence Carbon Nanotubes in Composites ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Philippe Schwaller | Daniel Probst | Alain C. Vaucher | Vishnu H Nair | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2019-12-25)
Field: Materials Science — Materials Chemistry

Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. The classification process is a tedious task, requiring first an accurate mapping of the reaction (atom mapping) followed by the identification of the corresponding reaction class template. In this work, we present two transformer-based models that infer reaction classes from the SMILES representation of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We study the incorrect predictions of the models and show that they reveal different biases and mistakes in the underlying data set. Using the embeddings of our classification model, we introduce reaction fingerprints that do not require knowing the reaction center or distinguishing between reactants and reagents. This conversion from chemical reactions to feature vectors enables efficient clustering and similarity search in the reaction space. We compare the reaction clustering for combinations of self-supervised, supervised, and molecular shingle-based reaction representations. Cluster analysis ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Chemical space ; Artificial neural network ; Similarity (geometry) ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Taha Roodbar Shojaei | Mohamad Amran Mohd Salleh | Hossein Mobli | Mortaza Aghbashlo | Meisam Tabatabaei
Source: Journal of Materials Science Materials in Electronics (article, 2019-01-02)
Field: Materials Science — Materials Chemistry

Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots Quenching (fluorescence) ; Nanoparticle ; Materials science ; Artificial neural network ; Quantum dot ; Carbon fibers ; Chemical engineering ; Nanotechnology ; Biological system ; Computer science ; Composite material ; Artificial intelligence ; Composite number ; Fluorescence Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zhen Song | Quanlin Liu
Source: The Journal of Physical Chemistry C (article, 2019-12-06)
Field: Materials Science — Materials Chemistry

Structural Indicator to Characterize the Crystal-Field Splitting of Ce<sup>3+</sup> in Garnets The structure–property relationship is a hot research topic in both chemistry and material science. A structural descriptor has significant applications in machine learning and high-throughput screening for rapid estimations of material properties. In this work, we report a simple structural indicator to characterize the crystal-field splitting of Ce3+ ions in the garnet structure. The indicator is the interplanar distance of the octahedron, which is calculated as 2/√3 | x + y + z | · L, where L stands for the cell parameter and (x, y, z) are the oxygen coordinates in a specific form. The indicator value is correlated to the crystal-field splitting of Ce3+ in the lanthanide aluminum/gallium garnets and is able to reproduce the reverse garnet effect. By inspecting the polyhedral competition effect in the garnet structure, this indicator is found to be correlated to the tetragonal distortion of the dodecahedron, which determines the crystal-field splitting. Tetragonal crystal system ; Dodecahedron ; Octahedron ; Gallium ; Crystal field theory ; Crystal structure ; Crystal (programming language) ; Crystallography ; Lanthanide ; Field (mathematics) ; Ion ; Materials science ; Chemistry ; Mathematics ; Computer science ; Metallurgy Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Solid-state spectroscopy and crystallography
Authors: Gayatri Viswanathan | Anton O. Oliynyk | Erin Antono | Julia Ling | Bryce Meredig | Jakoah Brgoch
Source: Inorganic Chemistry (article, 2019-07-03)
Field: Materials Science — Materials Chemistry

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always straightforward. Methods for simplifying and rationalizing the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure determination. In this work, we propose a new method that uses single-crystal data to determine the crystal structures of inorganic, extended solids called "single-crystal automated refinement" (SCAR). The approach was developed using data mining and machine learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to determine two newly synthesized and previously unreported phases, ZrAu<sub>0.5</sub>Os<sub>0.5</sub> and Nd<sub>4</sub>Mn<sub>2</sub>AuGe<sub>4</sub>. The structure solutions are found to be comparable with those produced by manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thus verified as being a fast and reliable assistant in determining even complex single-crystal diffraction data for extended inorganic solids. Chemistry ; Crystal structure ; Crystal (programming language) ; Crystal structure prediction ; Algorithm ; Crystallography ; Single crystal ; Set (abstract data type) ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Sida Huang | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Chemical Physics (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Ultrasmall Au clusters supported on pristine and defected CeO2: Structure and stability The atomistic simulation of supported metal catalysts has long been challenging due to the increased complexity of dual components. In order to determine the metal/support interface, efficient theoretical tools to map out the potential energy surface (PES) are generally required. This work represents the first attempt to apply the recently developed SSW-NN method, stochastic surface walking (SSW) global optimization based on global neural network potential (G-NN), to explore the PES of a highly controversial supported metal catalyst, Au/CeO<sub>2</sub>, system. By establishing the ternary Au-Ce-O G-NN potential based on first principles global dataset, we have searched for the global minima for a series of Au/CeO<sub>2</sub> systems. The segregation and diffusion pathway for Au clusters on CeO<sub>2</sub>(111) are then explored by using enhanced molecular dynamics. Our results show that the ultrasmall cationic Au clusters, e.g., Au<sub>4</sub>O<sub>2</sub>, attaching to surface structural defects are the only stable structural pattern and the other clusters on different CeO<sub>2</sub> surfaces all have a strong energy preference to grow into a bulky Au metal. Despite the thermodynamics tendency of sintering, Au clusters on CeO<sub>2</sub> have a high kinetics barrier (>1.4 eV) in segregation and diffusion. The high thermodynamics stability of ultrasmall cationic Au clusters and the high kinetics stability for Au clusters on CeO<sub>2</sub> are thus the origin for the high activity of Au/CeO<sub>2</sub> catalysts in a range of low temperature catalytic reactions. We demonstrate that the global PES exploration is critical for understanding the morphology and kinetics of metal clusters on oxide support, which now can be realized via the SSW-NN method. Ternary operation ; Catalysis ; Cationic polymerization ; Kinetics ; Materials science ; Molecular dynamics ; Chemical physics ; Surface diffusion ; Metal ; Potential energy surface ; Cluster (spacecraft) ; Diffusion ; Work (physics) ; Thermodynamics ; Chemistry ; Physical chemistry ; Computational chemistry ; Adsorption ; Molecule ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions