jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu-Hsuan Liao | Yu-Ning Yu | Maysam Abbod | Chung-Hung Shih | Jiann-Shing Shieh
Source: Sensors and Materials (article, 2020-07-17)
Field: Chemistry — Analytical Chemistry

Using Artificial Neural Network to Predict a Variety of Pathogenic Microorganisms In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients' data are used to construct the ANN model. Then, another two patients' data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future. Artificial neural network ; Variety (cybernetics) ; Microorganism ; Artificial intelligence ; Computer science ; Biochemical engineering ; Biology ; Engineering ; Bacteria Spectroscopy and Chemometric Analyses
Authors: Yuzhi Xu | Cheng‐Wei Ju | Bo Li | Qiu-Shi Ma | Lianjie Zhang | Junwu Chen
Source: ChemRxiv (preprint, 2020-09-04)
Field: Materials Science — Materials Chemistry

Segment Descriptor Enabling Prediction of Electronic Properties and Photocatalytic Hydrogen Evolution Rate of Alternating Conjugated Copolymers Based on Machine Learning Alternating conjugated copolymers have been regarded as promising candidates for photocatalytic hydrogen evolution due to the adjustability of their molecular structures and electronic properties. In this work, machine learning (ML) models with molecular fingerprint of segment descriptors (SD) have been successfully constructed to promote the accurate and universal prediction of electronic properties such as electron affinity, ionization potential and optical bandgap. Moreover, without any experimental values, a high-performance prediction classifier model toward photocatalytic hydrogen production of alternating copolymers has been developed with high accuracy (real-test accuracy = 0.91). Consequently, our results demonstrate accurate regression and classification models to disclose valuable influencing factors concerning hydrogen evolution rate (HER) of alternating copolymers. Copolymer ; Conjugated system ; Photocatalysis ; Materials science ; Computer science ; Training set ; Hydrogen ; Artificial intelligence ; Machine learning ; Biological system ; Polymer ; Chemistry ; Catalysis ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Seyed Mohamad Moosavi | Aditya Nandy | Kevin Maik Jablonka | Daniele Ongari | Jon Paul Janet | Peter G. Boyd | Yongjin Lee | Berend Smit | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-05-07)
Field: Chemistry — Inorganic Chemistry

Understanding the Diversity of the Metal-Organic Framework Ecosystem By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. Chemical space ; Formalism (music) ; Computer science ; Diversity (politics) ; Data science ; Biochemical engineering ; Engineering ; Biology ; Bioinformatics ; Sociology Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Ryohto Sawada | Yuma Iwasaki | Masahiko Ishida
Source: Scientific Reports (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training We present semi-supervised information maximizing self-argument training (IMSAT), a neural network-based classification method that works without the preparation of labeled data. Semi-supervised IMSAT can amplify specific differences and avoid undesirable misclassification in accordance with the purpose. We demonstrate that semi-supervised IMSAT has a comparable performance with existing methods for semi-supervised learning of image classification and can also classify real experimental data (X-ray diffraction patterns and thermoelectric hysteresis curves) in the same way even though their shape and dimensions are different. Our algorithm will contribute to the automation of big data processing and artificial intelligence-driven material development. Computer science ; Artificial intelligence ; Artificial neural network ; Automation ; Machine learning ; Big data ; Property (philosophy) ; Argument (complex analysis) ; Supervised learning ; Data mining ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Tomohiko Konno
Source: Journal of the Physical Society of Japan (article, 2020-11-30)
Field: Materials Science — Materials Chemistry

Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer We verified that the deep learning method named reading periodic table introduced by ref. Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations. We then extended the method to learn the laws better by directly learning the cylindrical periodicity between the right- and left-most columns in the periodic table at the learning representation level, that is, by considering the left- and right-most columns to be adjacent to each other. Thus, while the original method handles the table as is, the extended method treats the periodic table as if its two edges are connected. This is achieved using novel layers named periodic convolution layers, which can handle inputs exhibiting periodicity and may be applied to other problems related to computer vision, time series, and so on for data that possess some periodicity. In the reading periodic table method, no material feature or descriptor is required as input. We demonstrated two types of deep learning estimation: methods to estimate the existence of a bandgap, and methods to estimate the value of the bandgap given when the existence of the bandgap in the materials is known. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train-test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate. The code and data are open. Convolution (computer science) ; Table (database) ; Periodic table ; Representation (politics) ; Deep learning ; Feature (linguistics) ; Code (set theory) ; Lookup table Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Quantum many-body systems
Authors: Jorge Arturo Flores-López | Leticia Flores-Pulido | Lidia Patricia Jaramillo-Quintero | Carolina Rocío Sánchez-Pérez
Source: Applied Sciences (article, 2020-07-23)
Field: Chemistry — Analytical Chemistry

Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel Electrophoresis allows us to identify the types of proteins present in food, DNA, tissues and more. With the help of the molecular marker their weight is known, these markers are applied within the one-dimensional gel, and their protein value is known by means of marks. In this research, the molecular marker is obtained and the wavelet transform (WT) is obtained, generating approximation coefficients, which were taken to determine a molecular weight using three classification paradigms. The first paradigm is an approach in content-based image retrieval (CBIR) which makes a detection of the molecular weight in electrophoresis samples. The second approach is a neural network, thus two models are employed: self-organization maps (SOM) and back propagation in a supervised and unsupervised way, respectively. The third approach is based in a J48 decision tree. A comparison is made between the three paradigms for molecular weight computation. Neural networks obtained an improvement in the precision compared versus the CBIR-WT. Five parametric statistics were generated from the wavelet approximation coefficients. The CBIR-WT, SOM, back propagation and J48 were decisive for the classification and calculation of the molecular weight of each protein stain in the one-dimensional electrophoresis gel. C4.5 algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Decision tree ; Artificial neural network ; Chemistry ; Support vector machine Spectroscopy and Chemometric Analyses ; Genetics, Bioinformatics, and Biomedical Research ; Gene expression and cancer classification
Authors: Shae-Lynn Lahey | Từ Nguyễn Thiên Phúc | Christopher N. Rowley
Source: ChemRxiv (preprint, 2020-11-02)
Field: Chemistry — Spectroscopy

Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments Many drug molecules contain biaryl fragments, resulting in a torsional barrier corresponding to rotation around the bond linking the aryls. The potential energy surfaces of these torsions vary significantly due to steric and electronic effects, ultimately affecting the relative stability of the molecular conformations in the protein-bound and solution states. Simulations of protein--ligand binding require accurate computational models to represent the intramolecular interactions to provide accurate predictions of the structure and dynamics of binding. In this paper, we compare four force fields (Generalized AMBER Force Field (GAFF), Open Force Field (OpenFF), CHARMM General Force Field (CGenFF), Optimized Potentials for Liquid Simulations (OPLS)) and two neural network potentials (ANI-2x, ANI-1ccx) in their ability to predict the torsional potential energy surfaces of 88 biaryls extracted from drug fragments. The mean of the absolute deviation over the full PES (MADF) and the mean absolute deviation of the torsion rotational barrier height (MADB) relative to high-level ab initio reference data was used as a measure of accuracy. In comparison to high-level ab-initio data, ANI-1ccx was most accurate for predicting the barrier height (MADF: 0.5~kcal/mol, MADB:~0.8~kcal/mol), followed closely by ANI-2x (MADF: 0.5~kcal/mol, MADB:~1.0~kcal/mol), then CGenFF (MADF: 0.8~kcal/mol, MADB: 1.3~kcal/mol), OpenFF (MADF: 1.5~kcal/mol, MADB: 1.4~kcal/mol), GAFF (MADF: 1.2~kcal/mol, MADB: 2.6~kcal/mol), and finally OPLS (MADF: 1.5~kcal/mol, MADB: 2.8~kcal/mol). Significantly, the NNPs are systematically more accurate and more reliable than any of the force fields. As a practical example, the neural network potential/molecular mechanics (NNP/MM) method was used to simulate the isomerization of ozanimod, a drug used for multiple sclerosis. Multi-nanosecond molecular dynamics (MD) simulations in an explicit aqueous solvent were performed, as well as umbrella sampling and adaptive biasing force enhanced sampling techniques. These theories predicted a rate of isomerization of $4.30 \times 10^{-1}$ ns$^{-1}$, which is consistent with direct MD simulations. Force field (fiction) ; Chemistry ; Steric effects ; Ab initio ; Potential energy surface ; Computational chemistry ; Intramolecular force ; Binding energy ; Molecule ; Molecular dynamics ; Potential energy ; Crystallography ; Thermodynamics ; Physical chemistry ; Stereochemistry ; Atomic physics ; Physics ; Organic chemistry Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Chemical Synthesis and Analysis
Authors: Alex Glushkovsky
Source: arXiv (Cornell University) (preprint, 2020-11-24)
Field: Materials Science — Materials Chemistry

AI Discovering a Coordinate System of Chemical Elements: Dual Representation by Variational Autoencoders The periodic table is a fundamental representation of chemical elements that plays essential theoretical and practical roles. The research article discusses the experiences of unsupervised training of neural networks to represent elements on the 2D latent space based on their electron configurations. To emphasize chemical properties of the elements, the original data of electron configurations has been realigned towards valence orbitals. Recognizing seven shells and four subshells, the input data has been arranged as 7x4 images. Latent space representation has been performed using a convolutional beta variational autoencoder (beta-VAE). Despite discrete and sparse input data, the beta-VAE disentangles elements of different periods, blocks, groups, and types. The unsupervised representation of elements on the latent space reveals pairwise symmetries of periods and elements related to the invariance of quantum numbers of corresponding elements. In addition, it isolates outliers that turned out to be known cases of Madelung's rule violations for lanthanide and actinide elements. Considering the generative capabilities of beta-VAE, the supervised machine learning has been set to find out if there are insightful patterns distinguishing electron configurations between real elements and decoded artificial ones. Also, the article addresses the capability of dual representation by autoencoders. Conventionally, autoencoders represent observations of input data on the latent space. By transposing and duplicating original input data, it is possible to represent variables on the latent space which can lead to the discovery of meaningful patterns among input variables. Applying that unsupervised learning for transposed data of electron configurations, the order of input variables that has been arranged by the encoder on the latent space has turned out to exactly match the sequence of Madelung's rule. Autoencoder ; Computer science ; Representation (politics) ; Pattern recognition (psychology) ; Chemical space ; Artificial intelligence ; Latent variable ; Space (punctuation) ; Unsupervised learning ; Artificial neural network ; Chemistry Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Geochemistry and Geologic Mapping
Authors: Richard Archibald | Edmond Chow | E. D’Azevedo | Jack Dongarra | Markus Eisenbach | Rocco Febbo | Florent Lopez | Daniel Nichols | Stanimire Tomov | Kwai Wong | Junqi Yin
Source: arXiv (Cornell University) (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Integrating Deep Learning in Domain Sciences at Exascale This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications. Computer science ; Deep learning ; Workflow ; Modular design ; Supercomputer ; Artificial intelligence ; Asynchronous communication ; Domain (mathematical analysis) ; Asynchronous learning ; Computer architecture ; Scale (ratio) ; Distributed computing ; Data science ; Parallel computing ; Operating system ; Database ; Cooperative learning Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Reservoir Engineering and Simulation Methods
Authors: Kuo-Feng Lin | Chin-Chuan Hsieh | Shui-Chuan Hsin | Wen–Feng Hsieh
Source:  (article, 2020-02-26)
Field: Materials Science — Surfaces, Coatings and Films

Achieving high numerical aperture near-infrared imaging based on an ultrathin cylinder dielectric metalens Artificial subwavelength dielectric meta-lens (ML), realization of ultrathin and light-weight, provides a potential candidate to replace traditional bulky curved lens with high image quality. A ML with 1.5 mm in diameter having numerical aperture NA ~ 0.60 at the near-infrared wavelength λ = 0.94 μm was designed by finite-difference timedomain (FDTD) method with speeding up optimization of MLs’ scheme by deep neural network (DNN) model. Additionally, an ultra-thin high NA ML was achieved by cost effective semiconductor manufacturing technology. The fabricated ML can focus an incident light down to a spot as small as ~ 5.2 μm with high optical efficiency of ~88.4% (focusing efficiency achieved 23.7%). We also provided efficient MLs’ semiconductor manufacturing technology for the development of optical device in the near-infrared image technology. Materials science ; Numerical aperture ; Optics ; Finite-difference time-domain method ; Lens (geology) ; Optoelectronics ; Aperture (computer memory) ; Dielectric ; Infrared ; Wavelength ; Microwave ; Light-emitting diode ; Computer science ; Telecommunications ; Physics Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: N. N. Kiselyova | Victor Dudarev | V. V. Ryazanov | O. V. Sen’ko | A. A. Dokukin
Source: Inorganic Materials Applied Research (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Computer-Aided Design of Compounds with Crystal Structure of Melilites Compounds of compositions $${\text{A}}_{2}^{{ + 2}}{\text{B}}_{2}^{{ + 3}}$$ С+4О7 and А+2 $${\text{B}}_{{\text{2}}}^{{{\text{ + 2}}}}{\text{C}}_{2}^{{ + 4}}$$ О7 that are not yet obtained (A and B are cations of different elements; C is Si or Ge) with a melilite-type crystal structure are predicted and their crystal lattice parameters are evaluated. Predicting is based only on data on the properties of elements and simple oxides. The mean accuracy of predicting is at least 85%. The calculations are performed using scikit-learn system programs and an information analytical system based on machine learning approaches. Melilite ; Crystal structure ; Lattice (music) ; Crystal (programming language) ; Computer science ; Crystallography ; Materials science ; Physics ; Chemistry ; Programming language ; Metallurgy X-ray Diffraction in Crystallography ; Crystal Structures and Properties ; Machine Learning in Materials Science
Authors: Fei Fang | Peng Liu | Yang Zhao | Rajil Mehta | George C. Tseng | Parmjeet Randhawa | Kunhong Xiao
Source: medRxiv (preprint, 2020-05-14)
Field: Chemistry — Spectroscopy

Diagnosis of T-Cell-Mediated Kidney Rejection by Biopsy-Based Proteomics and Machine Learning Purpose: This study is aimed at developing a clinic-friendly proteomics protocol and a machine learning (ML)-based molecular diagnostic test for T-cell-mediated rejection (TCMR) using formalin-fixed, paraffin-embedded (FFPE) biopsies. Experimental design: Based on the procedures we reported for proteomic profiling of FFPE biopsies using Tandem Mass Tag (TMT)-based technology, a label-free-based quantitative proteomics protocol was developed as a more clinical-practical and cost-efficient molecular diagnostic test for renal transplant injection. This new protocol was applied to a set of FFPE biopsies from renal allograft injury patients and normal controls, including 5 TCMR, 5 polyomavirus BK nephropathy (BKPyVN) and 5 stable graft function (STA). Three different machine learning algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and random forests (RF), were tested to build a prediction model for TCMR. Results: About 750-1250 proteins were identified and quantified in each sample with high confidence using the label-free-based proteomics protocol. 178, 450 and 281 proteins were defined as differential expression (DE) proteins for TCMR vs STA, BKPyVN vs STA and TCMR vs BKPyVN, respectively. By comparing the quantitative data from the TMT- and label-free-based proteomics profiling, a classifier panel comprised of 234 DE proteins commonly quantified by two methods was generated to test different ML algorithms. Leave-one-out cross-validation result suggested that the RF-based model achieved the best prediction power for TCMR at both proteome and transcriptome level. Conclusions and clinical relevance: Proteomics profiling of FFPE biopsies using a platform integrated of label-free quantitative proteomics with ML-based predictive model can help to discover biomarker panels and provide clinical molecular diagnostic tests to enhance biopsy interpretation for renal allograft rejection. Proteomics ; Proteome ; Medicine ; Support vector machine ; Bioinformatics ; Computational biology ; Computer science ; Machine learning ; Artificial intelligence ; Biology Advanced Proteomics Techniques and Applications ; Renal Transplantation Outcomes and Treatments ; Hepatitis C virus research