jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Léo Laugier | Daniil Bash | Jose Recatala | Hong Kuan Ng | Savitha Ramasamy | Chuan-Sheng Foo | Vijay Chandrasekhar | Kedar Hippalgaonkar
Source: arXiv (Cornell University) (preprint, 2018-11-15)
Field: Materials Science — Materials Chemistry

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties. The dataset for the CGCNN is independent of Density Functional Theory (DFT) and only relies on the crystal and atomic information, while that for the FCNN is based on a rich attribute list mined from Materialsproject.org. The results show that the optimized FCNN is three layer deep and is able to predict the scattering-time independent thermoelectric powerfactor much better than the CGCNN (or XGBoost), suggesting that bonding and density of states descriptors informed from materials science knowledge obtained partially from DFT are vital to predict functional properties. Thermoelectric effect ; Density functional theory ; Convolutional neural network ; Crystal (programming language) ; Graph ; Computer science ; Graph theory ; Computation ; Materials science ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Mathematics ; Physics ; Combinatorics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Tomohiko Konno | Hodaka Kurokawa | Fuyuki Nabeshima | Yuki Sakishita | Ryō Ogawa | Iwao Hosako | Atsutaka Maeda
Source: arXiv (Cornell University) (article, 2018-12-03)
Field: Materials Science — Materials Chemistry

Deep Learning Model for Finding New Superconductors Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report the first deep learning model for finding new superconductors. We introduced the method named "reading periodic table" which represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors that are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in a database of superconductors. We also introduced the method named "garbage-in" to create synthetic data of non-superconductors that do not exist. Non-superconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and non-superconductors. We obtained three remarkable results. The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor CaBi2 and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available from the link https://github.com/tomo835g/Deep-Learning-to-find-Superconductors. Superconductivity ; Periodic table ; Deep learning ; Condensed matter physics ; Artificial intelligence ; Intuition ; Physics ; Computer science ; Quantum mechanics ; Psychology ; Cognitive science Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Surface and Thin Film Phenomena
Authors: Shan Zhu | Jiajun Li | Liying Ma | Chunnian He | Enzuo Liu | Fang He | Chunsheng Shi | Naiqin Zhao
Source: ChemRxiv (preprint, 2018-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Enabled Capacitance Prediction for Carbon-Based Supercapacitors Carbon is the most widely used electrode for the supercapacitors. To predict the capacitance of carbon-based supercapacitors, this work applies three machine learning (ML) methods, including linear regression, Lasso and artificial neural network. For training the ML process, we extracted data from hundreds of published papers. Moreover, five variables were selected to figure out their impact on capacitance, including specific surface area, calculated pore size, ID/IG ratio, N-doping level and voltage window. By evaluated with the real data, all of three methods achieve acceptable prediction results, and ANN exhibits the best performance. More importantly, this work shows the potential of ML in material science and advanced applications. Supercapacitor ; Capacitance ; Artificial neural network ; Lasso (programming language) ; Carbon fibers ; Work (physics) ; Voltage ; Materials science ; Computer science ; Linear regression ; Artificial intelligence ; Machine learning ; Electrode ; Algorithm ; Electrical engineering ; Engineering ; Mechanical engineering ; Chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Christopher Rose | Sherief Reda | Brenda M. Rubenstein | Jacob K. Rosenstein
Source:  (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Computing with Chemicals: Perceptrons Using Mixtures of Small Molecules Computation that can exploit the Avogadrian numbers of molecules in heterogeneous solutions, and the even larger number of potential interactions among these molecules, is a tantalizing dream. However, the lack of precise specificity/control of chemical interactions can be at odds with the dream. In this paper, we show how relatively simple chemistry can be used to produce a ubiquitous computational primitive (the multiply-accumulate or MAC operation) that forms the basis for a single-layer neural network called a perceptron. A chemical perceptron can be realized using distinct mixtures as inputs and different reagents as operations to produce the results of the perceptron MAC operation, that can be read out perhaps using simple indicators such as pH or fluorescence. With a moderately large chemical library, the number of potential inputs can be Avogadrian so that reagent addition implicitly performs a concomitantly large number of MAC operations in parallel. Perceptron ; Computer science ; Simple (philosophy) ; Artificial neural network ; Reagent ; Computation ; Exploit ; Artificial intelligence ; Biological system ; Theoretical computer science ; Chemistry ; Algorithm ; Organic chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Computational Drug Discovery Methods
Authors: William F. Schneider | Hua Guo
Source: The Journal of Physical Chemistry Letters (article, 2018-02-01)
Field: Materials Science — Materials Chemistry

Machine Learning ADVERTISEMENT RETURN TO ISSUEEditorialNEXTMachine LearningWilliam F. Schneider and Hua GuoCite this: J. Phys. Chem. Lett. 2018, 9, 3, 569Publication Date (Web):February 1, 2018Publication History Published online1 February 2018Published inissue 1 February 2018https://pubs.acs.org/doi/10.1021/acs.jpclett.8b00009https://doi.org/10.1021/acs.jpclett.8b00009editorialACS PublicationsCopyright © 2018 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views3175Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (253 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Algorithms,Machine learning,Neural networks,Physical chemistry,Potential energy Get e-Alerts Computer science ; Psychology ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Matthew A. Brown | Gerton Lunter
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-06-20)
Field: Materials Science — Materials Chemistry

An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs Abstract Motivation Convolutional neural networks (CNNs) have been trememdously successful in many contexts, particularly where training data is abundant and signal-to-noise ratios are large. However, when predicting noisily observed biological phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the model. Results Here we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of MC dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhibits exact reverse-complement symmetry and is more resistant to overtraining. We find that this model has increased power and generalizability, resulting in significantly better predictive accuracy compared to standard CNN implementations and state-of-art deep-learning-based motif finders. We use our network to predict recombination hotspots from sequence, and identify high-resolution binding motifs for the recombination-initiation protein PRDM9, which were recently validated by high-resolution assays. The network achieves a predictive accuracy comparable to that attainable by a direct assay of the H3K4me3 histone mark, a proxy for PRDM9 binding. Availability https://github.com/luntergroup/EquivariantNetworks Contact richard.brown@well.ox.ac.uk , gerton.lunter@well.ox.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. Computer science ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Bayesian probability ; Dropout (neural networks) ; Deep learning ; Algorithm Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Hai Guo | Jingying Zhao | Xiaoniu Li
Source: International Journal of Materials and Product Technology (article, 2018-12-17)
Field: Materials Science — Materials Chemistry

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees Nanocomposite coating is a coating made of particles whose sizes are of nanoscale. The microhardness of the coating is an importance parameter. Currently, experimental method is mainly adopted in the coating's microhardness and performance research, with high research cost and long time period. In this paper, the content of the nano-particles in the plating liquid, current density, duty ratio, addition of additives and ultrasonic power are set as inputs; the micro hardness of the nanocomposite coating is set as output. Extremely randomised trees (ERT) are used to establish a strong prediction model. The prediction performance is the ERT model is superior to that of the single models such as linear regression, back-propagation neural network and radial basis function neural network, etc. and other ensemble learning methods. ERT model can be used for predicting the microhardness of nanocomposite coating, providing an efficient and highly reliable method for new material performance prediction. Indentation hardness ; Coating ; Materials science ; Nanocomposite ; Composite material ; Artificial neural network ; Microstructure ; Computer science ; Machine learning Corrosion Behavior and Inhibition
Authors: Chuanshuai Zhu | Ruike Yang | Bao Chai | Qun Wei | Dongyun Zhang
Source: Journal of Physics and Chemistry of Solids (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Accelerating Pmn21-BAlNP properties prediction by machine learning based on first-principles calculation CASTEP ; Shear modulus ; Bulk modulus ; Lattice (music) ; Stability (learning theory) ; Thermodynamics ; Machine learning ; Mathematics ; Algorithm ; Artificial intelligence ; Materials science ; Density functional theory ; Chemistry ; Computer science ; Physics ; Computational chemistry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Keisuke Takahashi
Source: ChemPhysChem (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Uncovering Periodicity and Hidden Trends Responsible for Predicting the Magnetic Moment of Body Centered Cubic Crystal Prediction of the magnetic moment of binary body centered cubic (BCC) is explored in terms of first principle calculations and data science. A dataset of 1,541 binary BCC materials constructed by first principle calculations is implemented for data mining. Descriptors for determining the magnetic moment are explored using machine learning, where classification and regression models are both implemented. Data mining reveals that two descriptors are responsible for classifying whether the materials have zero or nonzero magnetic moments and can also classify which groups of magnetic moments they belong to (μ<sub>B</sub> <1, 1≤μ<sub>B</sub> <2, or 2≤μ<sub>B</sub> <3) where the average scores produced in cross validation indicate 80 % and 91 % accuracy, respectively. Furthermore, the direct prediction of magnetic moments is performed using a regression model where eight descriptors are revealed with an average score of 74 % accuracy. The inverse problem - from a given magnetic moment to corresponding material - is successfully addressed where the stability of the predicted materials are confirmed by further first principle calculations. Thus, descriptors for the magnetic moment in BCC materials are revealed and can be seen as the base descriptor set for the magnetic moments of further complex materials. Magnetic moment ; Moment (physics) ; Binary number ; Stability (learning theory) ; Inverse ; Magnetic susceptibility ; Condensed matter physics ; Materials science ; Physics ; Mathematics ; Computer science ; Machine learning ; Geometry ; Classical mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Fei Wang | Tiark Rompf
Source: arXiv (Cornell University) (preprint, 2018-02-14)
Field: Materials Science — Materials Chemistry

From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning. Researchers have proposed different avenues such as neural machine translation for proof synthesis, vectorization of symbols and expressions for representing symbolic patterns, and coupling of neural back-ends for dimensionality reduction with symbolic front-ends for decision making. However, these initial explorations are still only point solutions, and bear other shortcomings such as lack of correctness guarantees. In this paper, we present our approach of casting symbolic reasoning as games, and directly harnessing the power of deep reinforcement learning in the style of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT) problem as showcase, we demonstrate the feasibility of our method, and the advantages of modularity, efficiency, and correctness guarantees. Computer science ; Correctness ; Heuristics ; Artificial intelligence ; Reinforcement learning ; Artificial neural network ; Satisfiability modulo theories ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Software Engineering Research ; Reinforcement Learning in Robotics
Authors: Thomas Akdeniz | Daniel J. Lizotte | Nasser Mohieddin Abukhdeir
Source: Nanotechnology (article, 2018-11-23)
Field: Materials Science — Materials Chemistry

A generalized shapelet-based method for analysis of nanostructured surface imaging The determination of quantitative structure-property relations is a vital but challenging task for nanostructured materials research due to the presence of large-scale spatially varying patterns resulting from nanoscale processes such as self-assembly and nano-lithography. Focusing on nanostructured surfaces, recent advances have been made in automated quantification methods for orientational and translational order using shapelet functions, originally developed for analysis of images of galaxies, as a reduced-basis for surface pattern structure. In this work, a method combining shapelet functions and machine learning is developed and applied to a representative set of images of self-assembled surfaces from experimental characterization techniques including scanning electron miscroscopy, atomic force microscopy and transmission electron microscopy. The method is shown to be computationally efficient and able to quantify salient pattern features including deformation, defects, and grain boundaries from a broad range of patterns typical of self-assembly processes. Materials science ; Nanoscopic scale ; Lithography ; Characterization (materials science) ; Nanotechnology ; Transmission electron microscopy ; Electron tomography ; Scanning transmission electron microscopy ; Optoelectronics Block Copolymer Self-Assembly ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Roger A. Chevalier
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-07-10)
Field: Chemistry — Electrochemistry

Penetration Assessment of Dietary Supplements and Drugs through the Blood-Brain Barrier for Potential Treatment of Parkinson ‘s Disease Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder, affecting 2% of the population over the age of 60. To date, there is no disease modifying drugs to prevent dopaminergic neuron loss and abnormal protein deposition in the brains. There is a strong demand for neuroprotective therapies to prevent or slow down dopaminergic neuron degeneration. An essential prerequisite for a compound designed to affect the central nervous system (CNS) is a satisfactory transport through the blood brain barrier (BBB). Numerous posts on the net suggest that both repositioned drugs molecules and active molecules present in dietary supplements may slow down PD’s progression. The logBB is an index of BBB permeability. Starting from quantitative and qualitative permeability data, this study tries to predict logBB values from various physicochemical properties of molecules, including, among others, molecular size, polar surface area (PSA) and logP values. Quantitative logBB models were implemented using MLP (multiple linear regression), PLS (Partial Least Square), AAKR (Auto Associative Kernel Regression) and ECM (Evolving Cluster Method). Qualitative models were carried out with SVM (Support Vector Method). The paper estimates the BBB permeability of 39 molecules potentially able to slow down PD’s progression and compares the performances of qualitative and quantitative machine learning method used. For information, the current study also gives a short overview of the state of the art on the potential impact of dietary supplements on PD. Dopaminergic ; Parkinson's disease ; Disease ; Blood–brain barrier ; Population ; Neuroscience ; Medicine ; Pharmacology ; Chemistry ; Internal medicine ; Central nervous system ; Psychology ; Dopamine ; Environmental health Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advancements in Transdermal Drug Delivery
Authors: Andrew A. Protter | Vernon T. Phan | Traci Mizuno | Kevin Kim | Brent Louie | Sidney P. Elmer | Damir Herman | Johan A. Grahnen | Pek Yee Lum
Source: Journal of Clinical Oncology (article, 2018-05-20)
Field: Chemistry — Organic Chemistry

Novel small molecules induced ROS accumulation and antitumor activity based on an oxidative stress gene signature. e14575 Background: Reactive oxygen species (ROS) homeostasis is crucial for cell survival. Drugs that possess anti-tumor effects by suppressing ROS metabolism in cancer cells have potential as a new class of cancer therapeutics. Methods: A computational engine (SMarTR) comprising an ensemble of machine learning algorithms was used to predict compounds with anti-tumor activity against a panel of human cancers. A chemical moiety was selected and novel, synthetic derivatives were characterized for viability effects. A gene expression sensitivity signature (GESS) was derived, validated using anti-tumor responses in culture and then used to predict tumors most likely to respond to these novel compounds across 2532 different human tumor samples. Two compounds from this series, AU-535 and AU-583, were analyzed further for effects on ROS accumulation, DNA damage, and apoptosis in cancer cells. Results: AU-535 and AU-583 achieved significant antitumor effects in multiple myeloma, prostate cancer, triple negative breast (TNBC) and ovarian cancer cell lines, inducing ROS accumulation, apoptosis (Annexin V staining) and DNA damage (nuclear gH2AX). The GESS profile indicated cell response to these compounds was dependent on expression of genes associated with ROS metabolism. Consistently, AU-535 treatment induced ROS and increased expression of genes related ROS response and metabolism. Importantly, GESS predicts 26.6% (388/1457) of multiple myeloma, 28.5% (215/755) of ovarian, 25.2 % (27/107) of prostate and 56.8% (121/213) of TNBC tumors would respond to agents with this pharmacology. AU-535 and AU-583 showed favorable pharmacokinetic profiles, with intrinsic clearance values of 62 and 11.1 uL/min/mg, respectively, with human liver microsomes, and 48.1% and 42.6%, respectively, oral bioavailability in in rats. Conclusions: Two novel small molecules with anti-cancer activities associated with ROS metabolism have been characterized in cancer cell models. A gene expression signature associated with response has been derived and predicts favorable responses in patients with multiple cancer types. Oxidative stress ; Medicine ; Gene ; Gene signature ; Reactive oxygen species ; Cancer research ; Signature (topology) ; Small molecule ; Cell biology ; Gene expression ; Genetics ; Biology ; Internal medicine Synthesis and biological activity ; ATP Synthase and ATPases Research