jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hind Lafridi | A. Oussa | Hsaine Zgou | Mohammed Bouachrıne
Source: Moroccan Journal of chemistry (article, 2020-04-23)
Field: Chemistry — Organic Chemistry

QSAR modeling of antiradical properties of phenolic compounds using DFT calculations This paper describes a quantitative structure – activity relationship study of the antiradical properties of 31 flavonoids belonging to different groups such as: flavonols, flavonones, dihydroflavonols and biflavonones. Using density functional theory (DFT) calculations, some structural characteristics such as frontier molecular orbitals, molecular descriptors, have been studied. To gain insights into the chemical structure and property of the studied compounds, many types of descriptors are generated by using DFT/B3LYP 6-31G(d,p) and other software. Also, The Principle Component Analysis (PCA), Multiple Linear and Nonlinear Regression (MLR and MNLR), and Artificial Neural Network (ANN) have been investigated to select the descriptors, and to generate the correlation models that relate the structural feature to the biological activity. The statistical results of the MLR, MNLR, and ANN indicate that the determination coefficient R2 were 0.811, 0.646, 0.982, respectively. A good correlation coefficient is obtained, and the antiradical activities of these compounds are well predicted. These models are expected to be useful for screening of polyphenolic antioxidants. Quantitative structure–activity relationship ; Chemistry ; Molecular descriptor ; Density functional theory ; Flavonols ; Linear regression ; Correlation coefficient ; Computational chemistry ; Biological system ; Principal component analysis ; Polyphenol ; Stereochemistry ; Organic chemistry ; Artificial intelligence ; Machine learning ; Computer science ; Antioxidant Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Phytochemicals and Antioxidant Activities
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wavefunction theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in a MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening. Artificial intelligence ; Machine learning ; Computer science ; Transferability ; Leverage (statistics) ; Robustness (evolution) ; Classifier (UML) ; Workflow ; Training set ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Samik Bose | Suman Chakrabarty | Debashree Ghosh
Source: ACS Omega (article, 2020-03-24)
Field: Materials Science — Materials Chemistry

Support Vector Regression-Based Monte Carlo Simulation of Flexible Water Clusters Molecular simulations based on classical force fields are computationally efficient but lack accuracy due to the empirical formulation of non-bonded interactions. Quantum mechanical (QM) methods, albeit accurate, have inhibitory computational costs for large molecules and clusters. Hence, to overcome the bottleneck, machine learning (ML)-based methods have been employed in the recent years. We had earlier reported a combined scheme of many-body expansion (MBE) and ML to predict the interaction energies of rigid water clusters. In this work, we proceed toward building a flexible water model using the ML-MBE scheme. This ML-MBE scheme has an error of <1% for interaction energy prediction in comparison to the parent QM method for flexible water decamers. Machine learning-based Monte Carlo simulations (MLMC) are performed with this water model, and the structural properties of these configurations are compared with those obtained from ab initio molecular dynamics (AIMD) and the TIP3P classical force field. The radial distribution functions, tetrahedral order parameters, and number of hydrogen bonds in AIMD and MLMC have a similar qualitative and quantitative trend, whereas the classical force fields show a significant deviation. Monte Carlo method ; Force field (fiction) ; Bottleneck ; Molecular dynamics ; Statistical physics ; Ab initio ; Computer science ; Chemistry ; Computational chemistry ; Algorithm ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Thomas C. Nicholas | Andrew L. Goodwin | Volker L. Deringer
Source: Chemical Science (preprint, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining Much of our understanding of complex structures is based on simplification: for example, metal-organic frameworks are often discussed in the context of "nodes" and "linkers", allowing for a qualitative comparison with simpler inorganic structures. Here we show how such an understanding can be obtained in a systematic and quantitative framework, combining atom-density based similarity (kernel) functions and unsupervised machine learning with the long-standing idea of "coarse-graining" atomic structure. We demonstrate how the latter enables a comparison of vastly different chemical systems, and we use it to create a unified, two-dimensional structure map of experimentally known tetrahedral AB<sub>2</sub> networks - including clathrate hydrates, zeolitic imidazolate frameworks (ZIFs), and diverse inorganic phases. The structural relationships that emerge can then be linked to microscopic properties of interest, which we exemplify for structural heterogeneity and tetrahedral density. Granularity ; Tetrahedron ; Zeolitic imidazolate framework ; Computer science ; Kernel (algebra) ; Context (archaeology) ; Metal-organic framework ; Similarity (geometry) ; Nanotechnology ; Materials science ; Biological system ; Chemical physics ; Statistical physics ; Artificial intelligence ; Chemistry ; Physics ; Crystallography ; Mathematics ; Image (mathematics) ; Geography ; Pure mathematics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Crystal Structures and Properties ; X-ray Diffraction in Crystallography
Authors: Daniil Bash | Yongqiang Cai | Vijila Chellappan | Swee Liang Wong | Yang Xu | Pawan Kumar | Jin Da Tan | Anas Abutaha | Jayce Jian Wei Cheng | Yee‐Fun Lim | Siyu Tian | Zekun Ren | Wai Kuan Wong | Flore Mekki‐Berrada | Jatin Kumar | Saif A. Khan | Qianxiao Li | Tonio Buonassisi | Kedar Hippalgaonkar
Source: ChemRxiv (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity &lt;p&gt;Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.&lt;/p&gt; Carbon nanotube ; Materials science ; Conductivity ; Throughput ; Resampling ; Electrical resistivity and conductivity ; Computer science ; Characterization (materials science) ; High fidelity ; Composite number ; Thin film ; Nanotechnology ; Algorithm ; Artificial intelligence ; Machine learning ; Composite material ; Physics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification ; Adversarial Robustness in Machine Learning
Authors: Steven W. Cranford
Source: Matter (article, 2020-04-01)
Field: Materials Science — Biomaterials

Nature MADE: A Simple Guide to Biological Design Rules Surprise ; DECIPHER ; Simple (philosophy) ; Biomimetics ; Architecture ; Epistemology ; Computer science ; Cognitive science ; Nanotechnology ; Artificial intelligence ; Sociology ; Psychology ; Visual arts ; Biology ; Communication ; Art ; Philosophy Calcium Carbonate Crystallization and Inhibition ; Nanotechnology research and applications
Authors: Jennifer Carter | Amit K. Verma | Nishan M. Senanayake
Source: MRS Advances (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers Process (computing) ; Curriculum ; Openness to experience ; Experiential learning ; Workforce ; Engineering ethics ; Workflow ; Fluency ; Materials science ; Engineering design process ; Engineering education ; Computer science ; Data science ; Engineering management ; Mechanical engineering ; Engineering ; Mathematics education ; Pedagogy Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced Materials Characterization Techniques
Authors: Ivan S. Novikov | Blazej Grabowski | Fritz Körmann | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2020-12-23)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potentials reproduce vibrational and magnetic degrees of freedom We present the magnetic Moment Tensor Potentials (mMTPs), a new generation of machine-learning interatomic potentials, accurately reproducing both vibrational and magnetic degrees of freedom as provided, e.g., from first-principles calculations. The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space. The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron, with applications to phonon calculations for different magnetic states, and molecular dynamics simulations with fluctuating magnetic moments. Magnetic moment ; Degrees of freedom (physics and chemistry) ; Tensor (intrinsic definition) ; Molecular dynamics ; Physics ; Spin (aerodynamics) ; Space (punctuation) ; Condensed matter physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Jing Luo | Yong Xin | Zhengcheng Zhou | Yichao Zhu | Xu Guo
Source: Journal of Nuclear Materials (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

Attempts on representing sink strengths with machine learning formulations and the long-term role of crystalline interfaces in the development of irradiation-induced bubbles Sink (geography) ; Partial differential equation ; Computer science ; Nonlinear system ; Boundary value problem ; Mechanics ; Reaction–diffusion system ; Statistical physics ; Materials science ; Applied mathematics ; Mathematics ; Mathematical analysis ; Physics Nuclear materials and radiation effects ; Concrete and Cement Materials Research ; CO2 Sequestration and Geologic Interactions
Authors: Iztok Peruš | Heinz Palkowski | G. Kugler | Milan Terčelj
Source: Journal of Materials Research and Technology (article, 2020-09-30)
Field: Materials Science — Materials Chemistry

Quantifying complex influences of chemical composition and soaking conditions for increasing the hot workability of M2 high-speed steel by using the alternative approach The conditions for increasing the hot workability and extending the temperature range for the safe hot working of M2 high-speed steel (HSS) were studied and revealed. This was enabled by combination of two approaches, i.e. results obtained by an analysis of so individual as well as spatial influences of chemical elements on the hot workability using a conditional average estimator neural networks in combination with the results obtained from hot-compression tests that revealed the appropriate soaking conditions. The Latin Hypercube Sampling technique was used to model the uncertainty of the collected data used in the analysis. The obtained results reveal new, surprisingly complex, typically spatial and (highly) non-linear relationships between the chemical elements and the hot workability of M2 HSS, i.e. common mutual influence of carbon, carbide-forming elements as well as elements, i.e. Si, Mn and Co, which indirectly influence the formation of carbides. Further also new allowed upper limits for contents of some harmful elements like S, P, Al, Sb, Cu, Sn, As, Ni, etc. at which transition from higher to lower workability takes place were revealed. Finally, by applying a specially developed procedure for hot-compression tests the appropriate soaking time and temperature were assessed. New findings explain and considerably improve the intrinsic hot workability and extend the temperature range for safe hot working at its upper and lower limits. Materials science ; Latin hypercube sampling ; Carbide ; High-speed steel ; Hot working ; Estimator ; Chemical composition ; Metallurgy ; Compression (physics) ; Composite material ; Thermodynamics ; Microstructure ; Statistics ; Mathematics ; Monte Carlo method Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Qinghua Wang | Feng Xu | Bo Yang | Lijie Feng | Dedong Huang | Chen Wang | Bin Wu
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Geometry Optimization of Sheet Specimen for the Measurement Accuracy Improvement in the Hopkinson Bar Based on Intelligent Algorithm At present, the split Hopkinson tensile bar (SHTB) is widely used to determine the dynamic tensile properties of materials under high strain rates, in which sheet specimen with dogbone-shaped structure is commonly adopted. However, the geometry dimensions of the specimen used in different literatures vary widely and no uniform criterion has been formulated. In order to obtain the optimal specimen geometry associated with the best measurement accuracy in SHTB experiments, the specimen geometry influence on the measurement accuracy of SHTB experiments is investigated by using the finite element (FE) method, and several key indicators which can characterize the measurement accuracy of specimen are proposed based on simulation analysis. Orthogonal tests are designed to generate training samples for BP (back propagation) neural network, and the complex and highly nonlinear mapping between the structure parameters and measurement accuracy indicators of specimen is fitted by BP and then utilized for the fitness function design of genetic algorithm (GA). Finally, the optimal geometry as well dimensions of the SHTB sheet specimen are determined using GA. Meantime, the finite element simulations are carried out in further to verify the effectiveness of the optimized geometry of specimen. The results of this investigation will provide a recommendation for specimen geometry design and a basis for data reliability analysis in SHTB experiments. Bar (unit) ; Finite element method ; Genetic algorithm ; Split-Hopkinson pressure bar ; Algorithm ; Materials science ; Sheet metal ; Artificial neural network ; Computer science ; Geometry ; Structural engineering ; Mathematics ; Composite material ; Engineering ; Artificial intelligence ; Strain rate ; Machine learning High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Energetic Materials and Combustion
Authors: Huimin Zhu | Wei Liu | Qi Yin | Siwei Li | Changyong Wang | Lun Song | Xin Qiao
Source: Science China Technological Sciences (article, 2020-09-07)
Field: Materials Science — Biomaterials

Silk fibroin sponge combined with cell-derived ECM for tissue-engineered 3D functional neural tissues Fibroin ; Decellularization ; Extracellular matrix ; Sponge ; Scaffold ; Tissue engineering ; Chemistry ; Cell biology ; Neural tissue engineering ; Matrix (chemical analysis) ; Biomedical engineering ; Materials science ; Regeneration (biology) ; SILK ; Biology Silk-based biomaterials and applications ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: B. S. Keerthi Gowda | G. L. Easwara Prasad | V. Ramachandran
Source: SHILAP Revista de lepidopterología (article, 2020-07-01)
Field: Materials Science — Polymers and Plastics

Prediction of Mechanical Strength Attributes of Coir/Sisal Polyester Natural Composites by ANN Coir and Sisal are agriculture wastes that are effectively and financially accessible in the distinctive piece of Karnataka and other various states of republic India. These are generally treated as bio-compostable material by the customary horticulture/agriculture professionals. Aftereffects of past research related to fabrication, testing, analysis and design of conventional (synthetic fiber reinforced) composite materials portray that, strength to weight proportion is the basic criteria for a tailored design of composite materials. Viable utilizations of low-density reinforcing materials as the constituent materials of composites demonstrate great strength to weight ratio. Hence, 2 mm, 3 mm, 4 mm, 5 mm and 6 mm thick composite panels made up of 10 mm long coir/sisal fiber fortified in a polyester matrix of coupons are utilized for the experimentation process. The present study exhibits that the feed-forward Artificial Neural Network (ANN) model developed to predict the mechanical properties of coir/sisal polyester composite could be the acceptable mathematical tool for the prediction of mechanical properties of treated and untreated, arbitrarily oriented coir/sisal fiber strengthened polyester composite instead of the complicated experimental procedure. It exhibits that where traditional technique feels hard to estimate mechanical properties of coir/sisal fiber fortified polyester composite materials, the ANN model supports to foresee it. ANN approach avoids remembrance of equations and generalizes the problem domain and reduces the human error. SISAL ; Coir ; Composite material ; Materials science ; Polyester Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Agricultural Engineering and Mechanization
Authors: Peter de B. Harrington
Source: Journal of Chemometrics (article, 2020-02-23)
Field: Chemistry — Analytical Chemistry

Enhanced zippy restricted Boltzmann machine for feature expansion and improved classification of analytical data Abstract Restricted Boltzmann machines (RBMs) are components found in many deep learning algorithms. RBMs originally were designed for binary image data. Some advances in RBM algorithms have been made so that they may accept real‐valued inputs that are typical for analytical chemistry measurements. However, these algorithms are difficult to train and require fine‐tuning of the parameters. The RBM algorithm was modified to furnish the enhanced zippy RBM (EZRBM) that trains reliably and robustly with respect to the parameters. In addition, feature augmentation (ie, fusing the RBM linear inputs and nonlinear outputs) improves the classification rate while reducing the dependence of the RBM training parameters. Two different classifiers were used, the support vector classifier (SVC) and super partial least squares–discriminant analysis (sPLS‐DA), to evaluate the performance. Classifiers built from the EZRBM outputs performed better than those built from the continuous RBM and the Gaussian RBM (GRBM) outputs when validated using 100 bootstraps with two Latin partitions. Three datasets were used. The first was an overdetermined set of eight fatty acid concentrations for 572 olive oils from nine regions of Italy. The second was 75 UV spectra of 15 Cannabis extracts with 101 measurements made from 200 to 400 nm. The third set comprised 60 proton nuclear magnetic resonance (NMR) spectra of 12 tea extracts that had 1000 chemical shift measurements from 0.5 to 7.0 ppm. In every evaluation, the augmented EZRBM had better classification performance than the classifiers without the RBM. The classifiers built with EZRBM outperformed the other RBM algorithms except for a single instance. Recently, RBMs have been considered a transform into a dual feature space. Boltzmann machine ; Restricted Boltzmann machine ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Computer science ; Linear discriminant analysis ; Classifier (UML) ; Feature (linguistics) ; Binary number ; Deep learning ; Algorithm ; Mathematics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Rhys E. A. Goodall | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2020-04-20)
Field: Materials Science — Materials Chemistry

Machine learnt approximations to the bridge function yield improved\n closures for the Ornstein-Zernike equation A key challenge for soft materials design and coarse-graining simulations is\ndetermining interaction potentials between components that give rise to desired\ncondensed-phase structures. In theory, the Ornstein-Zernike equation provides\nan elegant framework for solving this inverse problem. Pioneering work in\nliquid state theory derived analytical closures for the framework. However,\nthese analytical closures are approximations, valid only for specific classes\nof interaction potentials. In this work, we combine the physics of liquid state\ntheory with machine learning to infer a closure directly from simulation data.\nThe resulting closure is more accurate than commonly used closures across a\nbroad range of interaction potentials. We show for two examples of a\nprototypical inverse design problem, fitting a coarse-grained simulation\npotential, that our approach leads to improved one-step inversion.\n Ornstein–Zernike equation ; Closure (psychology) ; Zernike polynomials ; Inverse ; Statistical physics ; Computer science ; Inversion (geology) ; Range (aeronautics) ; Function (biology) ; Applied mathematics ; Mathematics ; Integral equation ; Physics ; Mathematical analysis ; Geometry ; Engineering ; Quantum mechanics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Theoretical and Computational Physics