jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. A. Al-Owais | Ibrahim S. El-Hallag
Source: Oriental Journal Of Chemistry (article, 2018-12-07)
Field: Materials Science — Materials Chemistry

Electrochemical Investigation of Some Pharmaceutical Compounds at Mercury Electrode The present work aims to report the investigation of the electrochemical behavior of 3- arylazomethine-1,2,4-triazole derivatives in universal aqueous buffer series at mercury electrode. The electrode behavior of the studied compounds was performed via voltammetric studies, chronoamperometry, convoluted transforms, and dp polarography techniques. The relevant chemical and electrochemical parameters of 3- aryl azomethine -1,2,4- triazole derivatives were determined experimentally in universal aqueous buffer series as supporting electrolyte at mercury electrode. CPC was used for determination of the number of electrons to elucidate the mechanistic pathway of electrode reaction of aryl azomethine triazole compounds. Digital simulation method was used to confirm the accuracy of the experimental chemical parameters (homogeneous chemical rate constant) and electrochemical parameters (heterogeneous rate constant, symmetry coefficient and redox potential) via matching between the experimental and theoretical cyclic voltammograms. Chronoamperometry ; Chemistry ; Dropping mercury electrode ; Electrochemistry ; Polarography ; Electrode ; Reaction rate constant ; Aqueous solution ; Supporting electrolyte ; Inorganic chemistry ; Redox ; Analytical Chemistry (journal) ; Cyclic voltammetry ; Physical chemistry ; Organic chemistry ; Kinetics Covalent Organic Framework Applications ; Ionic liquids properties and applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Palash Mishra | Belaguppa Manjunath Ashwin Desai | R. Sarathi | Takahiro Imai
Source: IET Science Measurement & Technology (article, 2018-10-09)
Field: Materials Science — Materials Chemistry

Performance analysis of epoxy nanocomposites due to water droplet‐initiated discharges under AC and DC voltages and localisation of discharges Corona inception voltage (CIV) due to water droplet sitting over the surface of epoxy nanocomposite material depends on supply voltage frequency, the conductivity of water droplet and the contact angle of the test specimens. The contact angle of the specimen and CIV due to water droplet has a direct correlation. It is realised that the CIV is high under negative DC and the least under AC voltages. Surface charge accumulation studies indicate that the accumulated charge and its decay time constant reduces in the damage‐caused zone due to corona activity. The ultra‐high frequency (UHF) signal generated due to water droplet‐initiated corona activity has frequency content in the range of 0.8–1 GHz. The localisation of incipient discharges is demonstrated by using the non‐iterative technique and the cross recurrence plot (CRP) technique is used to estimate the time difference of arrival (TDOA) of UHF signals generated due to water droplet‐initiated discharge. Laser‐induced breakdown spectroscopy (LIBS) depicts the elemental composition and reveals the difference in plasma temperature and threshold fluence between all the test specimens. In short, the performance of ion trapping particle filled epoxy nanocomposite performance is found to be best followed by titania filled epoxy nanocomposite and the base epoxy resin. Epoxy ; Voltage ; Nanocomposite ; Materials science ; Electrical engineering ; Composite material ; Engineering High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Mariana Nayara Lima Silveira | L.O. Menali | M.Y.R. da Gloria | Romildo Dias Tolêdo Filho | Simão Ferreira
Source: Proceedings of the 4th Brazilian Conference on Composite Materials (article, 2018-01-01)
Field: Materials Science — Biomaterials

USE OF CACTUS WOOD CEREUS JAMACARU AS AGGREGATE IN LIGHTWEIGHT BIOCONCRETE PRODUCTION It is constant, especially in the civil construction sector, the search for new material sources that meet the assumptions of sustainable development. In this way, the cacti are pointed as a good alternative. Therefore, the objective of the present research was to develop and characterize physically and mechanically lightweight bioconcretes produced with Cereus wood from this lignocellulosic source. Cereus jamacaru wood was used after washing in hot water (at 80 C). In the studied matrix, Portland Cement CP V-ARI was used and a water-to-cement ratio of 0.4. To accelerate the cement hydration reactions 3% (relative to the cement mass) of calcium chloride was added. Mixtures with two types of wood particles classified as green and brown were produced and for both cases varying the percentage of compensation water (100 and 200%), cement-to-wood ratio (3,4 and 7) and moulding method (compacting with metal rod or not). A flow table test was used to characterize the fresh mixtures. The mechanical characterization was performed through uniaxial compression test. The density of the composites with the brown particles varied from 892 to 1452 kg/m and the compressive strength from 3.38 to 10.51 MPa. The blends with green particles reached between 872 and 1347 kg/m of density and between 1.72 and 8.94 MPa of compressive strength. The mixtures with higher cement-to-wood proportions and lower amount of compensating water reached higher compressive strength. The results show that Cactus Wood can be used in the production of bioconcretes with good properties and varied applications in civil construction. Cactus ; Aggregate (composite) ; Production (economics) ; Pulp and paper industry ; Engineering ; Materials science ; Composite material ; Horticulture ; Biology Advanced Cellulose Research Studies ; Botanical Research and Applications ; Growth and nutrition in plants
Authors: Jiali Li | Tiankai Chen | Kaizhuo Lim | Lingtong Chen | Saif A. Khan | Jianping Xie | Xiaonan Wang
Source: arXiv (Cornell University) (preprint, 2018-11-07)
Field: Materials Science — Materials Chemistry

Deep Learning Accelerated Gold Nanocluster Synthesis The understanding of inorganic reactions, especially those far from the equilibrium state, is relatively limited due to their inherent complexity. Poor understandings on the underlying synthetic chemistry have constrained the design of efficient synthesis routes towards desired final products, especially those inorganic materials at atomic precision. In this work, using the synthesis of atomically precise gold nanoclusters as a demonstration platform, we have successfully developed a deep learning framework for guiding material synthesis and accelerating the whole workflow. With only 54 examples, the proposed Graph Convolutional Neural Networks (GCNN) plus Siamese Neural Networks (SNN) classification model with the basic descriptors have been trained. The capability of predicting the target synthesis results has been demonstrated with a successful experimental validation. In addition, understandings in the synthesis process can be acquired from a decision tree trained by a large amount of generated data from the well-trained classification model. This study not only provides a data-driven method accelerating gold nanocluster synthesis, but also sheds light on understanding complex inorganic materials synthesis with low data amount. Nanoclusters ; Workflow ; Computer science ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Graph ; Machine learning ; Decision tree ; Artificial neural network ; Nanotechnology ; Theoretical computer science ; Materials science ; Database Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Michael Gastegger | Philipp Marquetand
Source: arXiv (Cornell University) (preprint, 2018-12-18)
Field: Materials Science — Materials Chemistry

Molecular Dynamics with Neural-Network Potentials Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine learning techniques can help to overcome these limitations by providing access to potential energies, forces and other molecular properties modeled directly after an electronic structure reference at only a fraction of the original computational cost. The present text discusses several practical aspects of conducting machine learning driven molecular dynamics simulations. First, we study the efficient selection of reference data points on the basis of an active learning inspired adaptive sampling scheme. This is followed by the analysis of a machine-learning based model for simulating molecular dipole moments in the framework of predicting infrared spectra via molecular dynamics simulations. Finally, we show that machine learning models can offer valuable aid in understanding chemical systems beyond a simple prediction of quantities. Computer science ; Artificial neural network ; Artificial intelligence ; Molecular dynamics ; Machine learning ; Computation ; Dipole ; Sampling (signal processing) ; Simple (philosophy) ; Statistical physics ; Algorithm ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Kristof T. Schütt | Michael Gastegger | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2018-06-27)
Field: Materials Science — Materials Chemistry

Quantum-chemical insights from interpretable atomistic neural networks With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler-Parrinello networks as well as the end-to-end model SchNet. Both models obtain predictions of chemical properties by aggregating atom-wise contributions. These latent variables can serve as local explanations of a prediction and are obtained during training without additional cost. Due to their correspondence to well-known chemical concepts such as atomic energies and partial charges, these atom-wise explanations enable insights not only about the model but more importantly about the underlying quantum-chemical regularities. We generalize from atomistic explanations to 3d space, thus obtaining spatially resolved visualizations which further improve interpretability. Finally, we analyze learned embeddings of chemical elements that exhibit a partial ordering that resembles the order of the periodic table. As the examined neural networks show excellent agreement with chemical knowledge, the presented techniques open up new venues for data-driven research in chemistry, physics and materials science. Interpretability ; Chemical space ; Quantum chemical ; Artificial neural network ; Computer science ; Interpretation (philosophy) ; Atom (system on chip) ; Statistical physics ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Molecule ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Rahul Singh | Aayush Sharma | Onur Rauf Bingöl | Aditya Balu | Ganesh Balasubramanian | D. D. Johnson | Soumik Sarkar
Source: arXiv (Cornell University) (preprint, 2018-11-23)
Field: Materials Science — Materials Chemistry

3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive experiments and computationally demanding first-principles simulations. Atomistic simulations can provide reasonable insights on properties in such material systems. However, the issue of designing robust potentials still exists. In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties. In the present work, we propose a voxel representation of the atomic configuration of a cell and design a 3D convolutional neural network to learn the interaction of the atoms. Our results highlight the performance of the 3D convolutional neural network and its efficacy in machine-learning the atomistic potential. We also explore the role of voxel resolution and provide insights into the two bounding box methodologies implemented for voxelization. Convolutional neural network ; Computer science ; Ductility (Earth science) ; Representation (politics) ; Deep learning ; Work (physics) ; Artificial neural network ; Minimum bounding box ; Artificial intelligence ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Aditya Nandy | Chenru Duan | Jon Paul Janet | Stefan Gugler | Heather J. Kulik
Source: ChemRxiv (article, 2018-08-21)
Field: Materials Science — Materials Chemistry

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry &lt;p&gt;Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO-LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO-LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20-30 features. Analysis of the essential descriptors for HOMO and HOMO-LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveals the paramount importance of non-local, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO-LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require. &lt;/p&gt; HOMO/LUMO ; Artificial intelligence ; Test set ; Machine learning ; Artificial neural network ; Chemical space ; Computer science ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Onur Çaylak | Anil Yaman | Björn Baumeier
Source: ChemRxiv (preprint, 2018-12-20)
Field: Materials Science — Materials Chemistry

An Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials We present a general framework for the construction of a deep feedforward neural network (FFNN) to predict distance and orientation dependent electronic coupling elements in disordered molecular materials. An evolutionary algorithm automatizes the selection of an optimal architecture of the artificial neural network within a predefined search space. Systematic guidance, beyond minimizing the model error with stochastic gradient descent based backpropagation, is provided by simultaneous maximization of a model fitness that takes into account additional physical properties, such as the field-dependent carrier mobility. As a prototypical system, we consider hole transport in amorphous tris(8-hydroxyquinolinato)aluminum. Reference data for training and validation is obtained from multiscale ab initio simulations, in which coupling elements are evaluated using density-functional theory, for a system containing 4096 molecules. The Coulomb matrix representation is chosen to encode the explicit molecular pair coordinates into a rotation and translation invariant feature set for the FFNN. The final optimized deep feedforward neural network is tested for transport models without and with energetic disorder. It predicts electronic coupling elements and mobilities in excellent agreement with the reference data. Such a FFNN is readily applicable to much larger systems at negligible computational cost, providing a powerful surrogate model to overcome the size limitations of the ab initio approach. Computer science ; Artificial neural network ; Feedforward neural network ; Feed forward ; Backpropagation ; Artificial intelligence ; Ab initio ; Coupling (piping) ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly
Authors: Matthew Spellings | Sharon C. Glotzer
Source: AIChE Journal (preprint, 2018-03-15)
Field: Materials Science — Materials Chemistry

Machine learning for crystal identification and discovery As computers get faster, researchers—not hardware or algorithms—become the bottleneck in scientific discovery. Computational study of colloidal self‐assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self‐assembly. We create numerical fingerprints—inspired by bond orientational order diagrams—of structures found in self‐assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis to keep up with the data generation ability of modern high‐throughput computing environments. © 2018 American Institute of Chemical Engineers AIChE J , 64: 2198–2206, 2018 Bottleneck ; Computer science ; Simple (philosophy) ; Throughput ; Identification (biology) ; Space (punctuation) ; Speedup ; Computational science ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Mineralogy and Gemology Studies
Authors: Amane Suzuki | Yuichiro Kikura | Ken‐ichi Tanaka | Kimito Funatsu
Source: Journal of Computer Aided Chemistry (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Solubility Prediction Using Neural Network and Chemical Explanation of Deep Learning Model 新規化合物の設計にあたり、溶解度は重要なパラメータのひとつである。しかし、候補化合物全てに対し実験で溶解度を求めるのは現実的に困難であるため、定量的構造物性相関(QSPR)を利用した推算が行われている。本研究では、QSPRに用いるモデルとして情報分野で広く使われ始めている深層学習を利用した。溶質・溶媒の構造記述子から溶解度予測に必要な情報を抽出する特徴抽出層、溶質・溶媒の相互作用を表現する関係表現層の2階層のモデルを作成することで実際の化学的現象に沿ったモデル構築を行った。構築したモデルが既往の手法(Random Forest)に比べて高い予測精度を示すことを確認したほか、モデルの中間出力をIsomapによって可視化した。可視化マップを用いることで溶媒の特性の理解が可能であり、代替溶媒の探索や併用の可否判定などができる可能性を示した。 Artificial neural network ; Artificial intelligence ; Deep learning ; Solubility ; Computer science ; Machine learning ; Biological system ; Chemistry ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: R.I. Zubatyuk | Justin S. Smith | Jerzy Leszczyński | Olexandr Isayev
Source: ChemRxiv (preprint, 2018-10-01)
Field: Materials Science — Materials Chemistry

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database. Benchmark (surveying) ; Solvation ; Artificial neural network ; Computer science ; Transferability ; Atom (system on chip) ; Dimension (graph theory) ; Modular design ; Modalities ; Artificial intelligence ; Modality (human–computer interaction) ; Quantum ; Machine learning ; Molecule ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ankita Mangal | Elizabeth A. Holm
Source: International Journal of Plasticity (preprint, 2018-08-17)
Field: Materials Science — Materials Chemistry

Applied machine learning to predict stress hotspots II: Hexagonal close packed materials Hotspot (geology) ; Materials science ; Crystallite ; Ultimate tensile strength ; Hexagonal crystal system ; Slip (aerodynamics) ; Microstructure ; Critical resolved shear stress ; Close-packing of equal spheres ; Shear stress ; Crystallography ; Geometry ; Composite material ; Metallurgy ; Geology ; Mathematics ; Physics ; Seismology ; Thermodynamics ; Chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties