jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louis R. Nemzer
Source: NSUWorks (Nova Southeastern University) (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Visualizing Amino Acid Substitutions in Physicochemical Space for Machine Learning Single amino acid substitutions are associated with certain inherited genetic disorders, such as sickle cell disease and hemochromatosis. To help sift through the large genomic databases now available, a new method of visualizing the physical and chemical changes as 3D vectors is proposed. In a principal component analysis space, the most important axes can be interpreted as "size," "hydrophobicity," and "charge." It can be shown that substitutions accessible by single nucleotide changes are strongly correlated with directions in this space, based on the codon position and mutation type. This illustrates the granular control available under the canonical genetic code for potentially beneficial mutations conferring a heterozygote advantage. This work can be applied to machine learning algorithms to better understand the underlying mechanisms behind these hereditary conditions. Other potential applications include the development of new therapeutics via rational protein design, as well as an improved theoretical basis for understanding directed and natural protein evolution. Space (punctuation) ; Computer science ; Chemical space ; Amino acid ; Computational biology ; Artificial intelligence ; Chemistry ; Biology ; Biochemistry ; Operating system Machine Learning in Materials Science
Authors: Jejoon Yeon | Sanjib C. Chowdhury | Chaitanya M. Daksha | Donato Belmonte | Adri C. T. van Duin | JOHN W. GILLESPIE
Source:  (article, 2021-09-20)
Field: Materials Science — Ceramics and Composites

PARAMETERIZATION OF REAXFF POTENTIAL OF MG/AL/SI/O INTERACTION AND INVESTIGATION OF MECHANICAL PROPERTIES FOR S-GLASS New ReaxFF parameters are developed for the description of Mg/Al/Si/O interaction for the Magnesium Aluminosilicate (MAS) glass structure. The training set contains energy curves from equation of state for various Mg/Al/Si/O crystals, valence angle and bond distance scan, and heat of formation for the Mg/Al/Si/O interactions. A semi-automated Genetic Algorithm assisted by Artificial Neural Network is applied for this parametrization. Validation efforts showed the current ReaxFF parameter set can describe the atomistic structure and property of tectosilicate MAS glass including S-glass. Estimated quasi-static modulus of S-glass structure matches well with experimental value. Analysis shows the key of high modulus of S-glass is numerous Mg-BO (Bridge Oxygen) interactions across the Mg-O-AlSi structure. In addition, atomistic origin of high ductility and progressive failure of S-glass is derived from the reconstruction of the atomic structure, forming Mg-BO-Si interactions that delays fracture formation. ReaxFF ; Materials science ; Modulus ; Aluminosilicate ; Ductility (Earth science) ; Composite material ; Molecular dynamics ; Thermodynamics ; Interatomic potential ; Computational chemistry ; Chemistry ; Creep ; Catalysis Advanced ceramic materials synthesis ; Glass properties and applications ; Advanced Surface Polishing Techniques
Authors: Fabio Calcinelli | Andreas Jeindl | Lukas Hörmann | Oliver T. Hofmann
Source: arXiv (Cornell University) (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Polymorphism-induced changes of interlayer electronic coupling at organic/inorganic interfaces The properties of a material depend on its structure, which for organic thin films often substantially differs from the bulk. A theoretical prediction of the most stable thin film structures through traditional first-principle modeling alone is unfeasible due to the combinatorial explosion in the number of polymorphs. Employing smart-data machine learning, we can now perform exhaustive structure searches for organic/inorganic interfaces requiring only a few hundred first-principles calculations. Here, we use this method to study the first two layers of benzoquinone on two different substrates, Ag(111) and graphene. Our results indicate that for the first layer, similar structures are favorable for both substrates. For the second layer, we find two distinct structures with significantly differing adsorption energies. Interestingly, graphene favors one class while Ag favors the other. To explain this behavior, we study the electronic properties of the substrates. This allows to explain the switch in stability, and its impact on the change in layer-to-layer charge transport. Materials science ; Adsorption ; Coupling (piping) ; Chemical physics ; Layer (electronics) ; Charge (physics) ; Thin film ; Graphene ; Polymorphism (computer science) ; Electronic structure ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Physical chemistry ; Composite material Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Surface Chemistry and Catalysis
Authors: Lorenzo Gigli | Max Veit | Michele Kotiuga | Giovanni Pizzi | Nicola Marzari | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2021-11-09)
Field: Materials Science — Materials Chemistry

Thermodynamics and dielectric response of $\text{BaTiO}_3$ by data-driven modeling Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning model that describes on the same footing structural, energetic and functional properties of barium titanate ($\text{BaTiO}_3$), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. These predictions allow us to assess the microscopic mechanism of the ferroelectric transition. The presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition, even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate, partly-ordered phases. Moreover, we thoroughly probe the static and dynamical behavior of $\text{BaTiO}_3$ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Finally, we apply the polarization model to calculate dielectric response properties of the material in a fully ab initio manner, again reproducing the correct qualitative experimental behaviour. Ferroelectricity ; Ab initio ; Dielectric ; Polarization (electrochemistry) ; Barium titanate ; Phase diagram ; Phase transition ; Condensed matter physics ; Statistical physics ; Physics ; Symmetry breaking ; Ab initio quantum chemistry methods ; Materials science ; Phase (matter) ; Quantum mechanics ; Chemistry ; Physical chemistry ; Molecule Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Scientific Research and Discoveries
Authors: Satish A Patel | Dharmendrasinh A. Baria
Source: INDIAN DRUGS (article, 2021-12-04)
Field: Chemistry — Analytical Chemistry

SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF PHENYLEPHRINE HYDROCHLORIDE AND NAPHAZOLINE HYDROCHLORIDE IN EYE DROPS BY CHEMOMETRIC TECHNIQUES AND ARTIFICIAL NEURAL NETWORK Three multivariate calibration-prediction techniques, partial least squares (PLS), principal component regression (PCR) and artifi cial neural networks (ANN), have been applied without separation in the spectrophotometric multi-component analysis of phenylephrine hydrochloride and naphazoline hydrochloride. A set of 25 synthetic mixtures of phenylephrine hydrochloride and naphazoline hydrochloride has been evaluated to determine the predictability of PLS, PCR and ANN. The absorbance data matrix was obtained by measuring zero-order absorbances between 230-300 nm at intervals of 3 nm. The suitability of the models was determined on the basis of root mean square error (RMSE), root mean squared cross validation error (RMSECV) and root mean squared prediction error (RMSEP) values of calibration and validation data. The results showed a very good correlation between true values and the predicted concentration values. Therefore, the methods developed can be used for routine drug analysis without chemical pre-treatment. Mean squared error ; Correlation coefficient ; Chromatography ; Principal component regression ; Partial least squares regression ; Chemistry ; Calibration ; Chemometrics ; Multivariate statistics ; Mathematics ; Principal component analysis ; Statistics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pharmacological Effects and Assays
Authors: Stephen Lam | Qing‐Jie Li | R. G. Ballinger | Charles Forsberg | Ju Li
Source: ChemRxiv (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3¾F2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts the structures and dynamics at normal operating conditions, high temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long timescales (e.g., nanosecond) and large system sizes (e.g., 105 atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties. Molecular dynamics ; Melting point ; Materials science ; Lithium (medication) ; Chemical physics ; Interatomic potential ; Atom (system on chip) ; Work (physics) ; Absorption (acoustics) ; Molten salt ; Thermodynamics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Graphite, nuclear technology, radiation studies ; Nuclear Materials and Properties
Authors: Xiufeng Yang | Tanuj Kr Aasawat | Kazuki Yoshizoe
Source:  (article, 2021-05-03)
Field: Materials Science — Materials Chemistry

Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design It is common practice to use large computational resources to train neural networks, as is known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used for searching solutions for combinatorial optimization problems. In this paper, we propose a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for 1,000 worker scale, and apply it to molecular design. This is the first work that applies distributed MCTS to a real-world and non-game problem. Existing work on large-scale parallel MCTS show efficient scalability in terms of the number of rollouts up to 100 workers, but suffer from the degradation in the quality of the solutions. MP-MCTS maintains the search quality at larger scale, and by running MP-MCTS on 256 CPU cores for only 10 minutes, we obtained candidate molecules having similar score to non-parallel MCTS running for 42 hours. Moreover, our results based on parallel MCTS (combined with a simple RNN model) significantly outperforms existing state-of-the-art work. Our method is generic and is expected to speed up other applications of MCTS. Computer science ; Scalability ; Monte Carlo tree search ; Massively parallel ; Reinforcement learning ; Monte Carlo method ; Tree (set theory) ; Scale (ratio) ; Parallel computing ; Distributed computing ; Artificial intelligence Machine Learning in Materials Science ; Artificial Intelligence in Games ; Metaheuristic Optimization Algorithms Research
Authors: Anahita Iravanizad | Edgar Iván Sánchez Medina | Martin Stoll
Source: arXiv (Cornell University) (preprint, 2021-09-15)
Field: Materials Science — Materials Chemistry

RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs In recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs. The majority of GNN architectures are designed based on developing new convolutional and/or pooling layers that better extract the hidden and deeper representations of the graphs to be used for different prediction tasks. The inputs to these layers are mainly the three default descriptors of a graph, node features $(X)$, adjacency matrix $(A)$, and edge features $(W)$ (if available). To provide a more enriched input to the network, we propose a random walk data processing of the graphs based on three selected lengths. Namely, (regular) walks of length 1 and 2, and a fractional walk of length $γ\in (0,1)$, in order to capture the different local and global dynamics on the graphs. We also calculate the stationary distribution of each random walk, which is then used as a scaling factor for the initial node features ($X$). This way, for each graph, the network receives multiple adjacency matrices along with their individual weighting for the node features. We test our method on various molecular datasets by passing the processed node features to the network in order to perform several classification and regression tasks. Interestingly, our method, not using edge features which are heavily exploited in molecular graph learning, let a shallow network outperform well known deep GNNs. Adjacency matrix ; Random walk ; Computer science ; Pooling ; Weighting ; Graph ; Theoretical computer science ; Adjacency list ; Node (physics) ; Scaling ; PageRank ; Random graph ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Complex Network Analysis Techniques
Authors: Daniel Schwalbe‐Koda | Aik Rui Tan | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2021-01-27)
Field: Materials Science — Materials Chemistry

Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials. Neural network (NN)-based interatomic potentials provide fast prediction of potential energy surfaces with the accuracy of electronic structure methods. However, NN predictions are only reliable within well-learned training domains, with unknown behavior when extrapolating. Uncertainty quantification through NN committees identify domains with low prediction confidence, but thoroughly exploring the configuration space for training NN potentials often requires slow atomistic simulations. Here, we employ adversarial attacks with a differentiable uncertainty metric to sample new molecular geometries and bootstrap NN potentials. In combination with an active learning loop, the extrapolation power of NN potentials is improved beyond the original training data with few additional samples. The framework is demonstrated on multiple examples, leading to better sampling of kinetic barriers and collective variables without extensive prior data on the relevant geometries. Adversarial attacks are new ways to simultaneously sample the phase space and bootstrap NN potentials, increasing their robustness and enabling a faster, accurate prediction of potential energy landscapes. Extrapolation ; Artificial neural network ; Adversarial system ; Computer science ; Artificial intelligence ; Metric (unit) ; Robustness (evolution) ; Machine learning ; Uncertainty quantification ; Mathematics ; Statistics ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Weixin Jiang | Eric Schwenker | Trevor Spreadbury | Kai Li | Maria K. Y. Chan | Oliver Cossairt
Source: arXiv (Cornell University) (preprint, 2021-07-06)
Field: Chemistry — Analytical Chemistry

Plot2Spectra: an Automatic Spectra Extraction Tool Different types of spectroscopies, such as X-ray absorption near edge structure (XANES) and Raman spectroscopy, play a very important role in analyzing the characteristics of different materials. In scientific literature, XANES/Raman data are usually plotted in line graphs which is a visually appropriate way to represent the information when the end-user is a human reader. However, such graphs are not conducive to direct programmatic analysis due to the lack of automatic tools. In this paper, we develop a plot digitizer, named Plot2Spectra, to extract data points from spectroscopy graph images in an automatic fashion, which makes it possible for large scale data acquisition and analysis. Specifically, the plot digitizer is a two-stage framework. In the first axis alignment stage, we adopt an anchor-free detector to detect the plot region and then refine the detected bounding boxes with an edge-based constraint to locate the position of two axes. We also apply scene text detector to extract and interpret all tick information below the x-axis. In the second plot data extraction stage, we first employ semantic segmentation to separate pixels belonging to plot lines from the background, and from there, incorporate optical flow constraints to the plot line pixels to assign them to the appropriate line (data instance) they encode. Extensive experiments are conducted to validate the effectiveness of the proposed plot digitizer, which shows that such a tool could help accelerate the discovery and machine learning of materials properties. Plot (graphics) ; Computer science ; Artificial intelligence ; Pixel ; Pattern recognition (psychology) ; Segmentation ; Graph ; Detector ; Computer vision ; Mathematics ; Theoretical computer science Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Remote-Sensing Image Classification
Authors: Marián Kubliha | Ondrej Bošák | П. Костка | V. Labaš | S.R. Lukić-Petrović | N. Celic | P. Tanuska | M. Kebisek | Mohamed Toufik Soltani
Source: Russian Journal of Electrochemistry (article, 2021-07-01)
Field: Materials Science — Ceramics and Composites

Experimental and Simulation of Electric Transport in Alkali Antimonite Glasses The non-linear response of various physical properties of glasses on mixing of alkali ions is a well-known anomaly in materials science. In this paper, the mixed alkali effect in antimony oxide based glasses with composition 60Sb2O3–20WO3–(20 – x)Li2O–x(M2O), where x = 0, 5, 10, 15, 20 (in mol %) and M = Na or K, is studied. The influence of Na/Li and K/Li ratios on ionic AC and DC conductivities is studied. Temperature dependences of the DC conductivity obey Arrhenius-like relation. The conductivity steeply decreases with increasing Na or K content due to larger ionic radius of Na or K ions compared to that of Li. The relation between composition and local movement of electrical charge was investigated and quantified using the measurement of thermally stimulated depolarization currents. The artificial neural network methods for optimizing experimental parameters used in this paper represent a new approach in comparison with works done on glasses with similar composition. The prepared numerical model could be used for the description of the influence of polarization parameters and the optimization of further measurements oriented on activation energies of ion polarization related to local transport of electrical charge, i.e. Li+ and Na+ ions in our case. Ionic radius ; Alkali metal ; Ion ; Arrhenius equation ; Analytical Chemistry (journal) ; Ionic conductivity ; Conductivity ; Electrical resistivity and conductivity ; Antimony ; Ionic bonding ; Chemistry ; Polarization (electrochemistry) ; Electrochemistry ; Materials science ; Mineralogy ; Inorganic chemistry ; Activation energy ; Electrolyte ; Physical chemistry ; Electrode ; Physics Glass properties and applications ; Phase-change materials and chalcogenides ; Nuclear materials and radiation effects
Authors: Liang Zhu | Anshan Xiao | Xiaoming Chi | Guolong Wang | Mingjun Li | Shaohua Gao | Ming Ma | He Zhang | Shengjie Zhu | Ming Jiang
Source:  (article, 2021-05-22)
Field: Chemistry — Spectroscopy

Research on Intelligent Identification and Quantitative Detection of Gas Leakage in Open Space Leakage is accident, leakage is emission. The accurate detection of gas leakage in open space will help enterprises to detect the leakage as early as possible, reducing the loss and impact of safety and environmental protection. Firstly, this paper introduces the current situation of gas leakage detection technology in the open space, and the principle of quantum cascade lasers detection technology, comparing the different technologies of open path Fourier transform infrared spectrometry, solar occultation flux, tunable diode laser absorption spectroscopy, optic gas image and so on. Then, according to the technical advantages of open path quantum cascade lasers, combined with the characterization analysis of gas type, a three-layer neural network intelligent recognition model is designed by taking the specific wavelength spectrum absorption detection value as the input and the material type as the output, 2480 groups of sample data were used to train and verify the detection accuracy. The results show that the accuracy rate of leakage gas identification is more than 98%, and the quantitative detection error is less than 8%. Finally, the paper describes an open space application test based on an unmanned aerial vehicle platform, with a horizontal distance of 150 meters and a vertical height of 30 meters, which provides a new method for rapid identification and quantitative detection of gas leakage in open space. Leakage (economics) ; Quantum cascade laser ; Computer science ; Optical path ; Laser ; Materials science ; Optoelectronics ; Optics ; Physics Spectroscopy and Laser Applications ; Air Quality Monitoring and Forecasting ; Water Quality Monitoring and Analysis