jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chen Zhang | Yewei Yu | Jingwen Xu | Zhiwu Han | Miaolei Zhou
Source:  (article, 2020-09-27)
Field: Materials Science — Materials Chemistry

Duhem Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator via Takagi-Sugeno Fuzzy Neural Network The magnetic shape memory alloy (MSMA)-based actuator is a promising candidate in the micro positioning field by virtues of its large stroke and small volume. However, the inherent hysteresis nonlinearity between the input current and the output displacement seriously limited the application of the MSMA-based actuator. In this paper, the hysteresis, which is related to the input frequency and working condition (such as load), is analyzed. Then a mathematical modeling method using Duhem model (DM) and Takagi-Sugeno fuzzy neural network (TSFNN) is introduced to describe the hysteresis behavior. The mathematical expression of the DM is explicit and simple; and the TSFNN, which has the advantages of both fuzzy system and NN structure, is used to identify the DM parameter. Hence, the proposed TSFNN-DM method has the merits of self adjustment and clear expression. To certify the validity of the developed model, comparative experiments with the modeling methods in other literatures are executed. Experimental results confirm that the TSFNN-DM has the better modeling performance to depict the hysteresis under the different input frequencies and loads than other modeling methods in previous studies. Hysteresis ; Control theory (sociology) ; Actuator ; Shape-memory alloy ; Fuzzy logic ; Artificial neural network ; Displacement (psychology) ; Nonlinear system ; Computer science ; Magnetic hysteresis ; Expression (computer science) ; Control engineering ; Magnetic field ; Engineering ; Artificial intelligence ; Physics ; Control (management) ; Magnetization Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Amit Gupta | Sabyasachi Chakraborty | Raghunathan Ramakrishnan
Source: arXiv (Cornell University) (preprint, 2020-09-15)
Field: Materials Science — Materials Chemistry

Revving up 13C NMR shielding predictions across chemical space: Benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust `local' machine learning (ML) strategy capturing the effect of neighbourhood on an atom's `near-sighted' property -- chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model trained on 100k samples, accurately predict isotropic shielding of 50k `hold-out' atoms with a mean error of less than $1.9$ ppm. For rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a $Δ$-ML strategy, we quench the error below $1.4$ ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10 to 17 heavy atoms and drugs. Isotropy ; Molecule ; Benchmark (surveying) ; Kernel (algebra) ; Electromagnetic shielding ; Chemical shift ; Chemical space ; Transferability ; Atom (system on chip) ; Computer science ; Artificial intelligence ; Molecular physics ; Machine learning ; Pattern recognition (psychology) ; Materials science ; Algorithm ; Computational chemistry ; Statistical physics ; Nuclear magnetic resonance ; Chemistry ; Physics ; Mathematics ; Discrete mathematics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jingbai Li | Patrick Reiser | André Eberhard | Pascal Friederich | Steven A. Lopez
Source: ChemRxiv (preprint, 2020-10-05)
Field: Materials Science — Materials Chemistry

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr et al. demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH2NH2+).We have developed a Python-based code, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), to accomplish the unprecedented 10 ns cis-trans photodynamics of trans-hexafluoro-2-butene (CF3–CH=CH–CF3) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S1 half-life (60.5 fs), the photochemical product ratio (trans: cis = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (trans → cis but not cis → trans pathways) that may offer future automated photochemical reaction discoveries. Isomerization ; Picosecond ; Excited state ; Computer science ; Molecular dynamics ; Chemistry ; Ab initio ; Photochemistry ; Computational chemistry ; Physics ; Atomic physics ; Catalysis ; Quantum mechanics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Data Quality and Management
Authors: Sh. Kianfar | Ali Reza Keshtkar | Bahman ZareNezhad
Source: Polymer Bulletin (article, 2020-01-22)
Field: Materials Science — Biomaterials

Graft polymerization of acrylonitrile onto cross-linked (alginate/polyvinyl alcohol) beads initiated by potassium persulfate: synthesis and artificial neural network modeling Potassium persulfate ; Acrylonitrile ; Thermogravimetric analysis ; Polyvinyl alcohol ; Grafting ; Materials science ; Polymerization ; Thermal stability ; Monomer ; Polymer chemistry ; Chemical engineering ; Persulfate ; Fourier transform infrared spectroscopy ; Chemistry ; Copolymer ; Composite material ; Organic chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; Conducting polymers and applications
Authors: Cong Yu | Wei Fan | Haiquan Yu | Fengqi Si
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A machine learning NO<sub>x</sub>emission model for SCR system considering mechanism knowledge and catalyst deactivation In this work, an adaptive NO x emission model is proposed for a SCR system of a 660 MW utility boiler. First, 3-years operating data was collected from the plant SIS system as raw data, which was then filtered using the R-statistic method and clustered by the condensed nearest neighbor (CNN) rule to form a classified steady-state database. In addition, a sliding window approach was used to deal with the continuous data stream. As the newest steady state sample was introduced into the database, the most similar old sample in the same data class was replaced. The crowding distance (CD) operator was also used to eliminate the redundant samples. This new method RCNN-CD is proven to be a good tool to improve the representatives of the samples. Based on the selected samples, a fusion monotony support vector regression (FM-SVR) was used to establish the NO x emission model. The results show that, this model can reasonably reflect SCR mechanism and follow the degradation of SCR performance. Statistic ; Boiler (water heating) ; Support vector machine ; Computer science ; Sliding window protocol ; Data mining ; Artificial intelligence ; Engineering ; Mathematics ; Window (computing) ; Statistics Catalytic Processes in Materials Science ; Thermochemical Biomass Conversion Processes ; Catalysis and Oxidation Reactions
Authors: Yoshihide Sawada | Koji Morikawa | Mikiya Fujii
Source: Chemistry Letters (article, 2020-11-13)
Field: Materials Science — Materials Chemistry

Conditional Generative Adversarial Networks for Inorganic Chemical Compositions Abstract In recent years, inverse material design using machine learning techniques has attracted attention for material development. Almost all studies have used crystal structures of materials, although material engineers rarely store the crystal information and they only save chemical compositions and target properties for high-throughput materials discovery. Thus, we propose a method to generate chemical compositions for desired target properties by using conditional generative adversarial networks (CondGAN) and a post-processing method to balance the oxidation numbers. Numerical experimental results demonstrate that our CondGAN generates chemical compositions holding the desired properties. Valency ; Generative grammar ; Chemistry ; Generative adversarial network ; Crystal (programming language) ; Chemical species ; Adversarial system ; Artificial intelligence ; Biological system ; Computer science ; Deep learning ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: R. Patrick Xian | Vincent Stimper | Marios Zacharias | Maciej Dendzik | Shuo Dong | Samuel Beaulieu | Bernhard Schölkopf | Martin Wolf | Laurenz Rettig | Christian Carbogno | Sebastian Bauer | Ralph Ernstorfer
Source: arXiv (Cornell University) (preprint, 2020-05-20)
Field: Materials Science — Materials Chemistry

A machine learning route between band mapping and band structure Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials. While convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting quasiparticle dispersion (closely related to BS) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data. Computer science ; Artificial intelligence ; Pipeline (software) ; Machine learning ; Scalability ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Ilyes Batatia
Source:  (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

A Deep Learning Method with CRF for Instance Segmentation of Metal-Organic Frameworks in Scanning Electron Microscopy Images This paper proposes an integrated method for recognizing special crystals, called metal-organic frameworks (MOF), in scanning electron microscopy images (SEM). The proposed approach combines two deep learning networks and a dense conditional random field (CRF) to perform image segmentation. A modified Unet-like convolutional neural network (CNN), incorporating dilatation techniques using atrous convolution, is designed to segment cluttered objects in the SEM image. The dense CRF is tailored to enhance object boundaries and recover small objects. The unary energy of the CRF is obtained from the CNN. And the pairwise energy is estimated using mean field approximation. The resulting segmented regions are fed to a fully connected CNN that performs instance recognition. The method has been trained on a dataset of 500 images with 3200 objects from 3 classes. Testing achieves an overall accuracy of 95.7% MOF recognition. The proposed method opens up the possibility for developing automated chemical process monitoring that allows researchers to optimize conditions of MOF synthesis. Conditional random field ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Unary operation ; Computer science ; Segmentation ; Deep learning ; Image segmentation ; Convolution (computer science) ; Energy (signal processing) ; Artificial neural network ; Image (mathematics) ; Computer vision ; Mathematics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Trevor David Rhone | Wei Chen | Shaan Desai | Steven B. Torrisi | Daniel T. Larson | Amir Yacoby | Efthimios Kaxiras
Source: Scientific Reports (preprint, 2020-09-25)
Field: Materials Science — Materials Chemistry

Data-driven studies of magnetic two-dimensional materials We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form [Formula: see text], based on the known material [Formula: see text], using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior. van der Waals force ; Density functional theory ; Magnetic moment ; Inductive coupling ; Monolayer ; Materials science ; Condensed matter physics ; Chemical physics ; Physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Cristina García–Cardona | Ramakrishnan Kannan | Travis Johnston | Thomas Proffen | Sudip K. Seal
Source:  (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach One of the main goals of neutron data analysis is to determine the internal structure of materials from their neutron scattering profiles. These structures are defined by a crystallographic class label and a set of real-valued parameters specific to that class. Existing structure analysis approaches use computationally expensive loop refinements methods that routinely take days, and even weeks, to complete. Additionally, the outcomes often rely on the fidelity of physical models that are computed during the refinement process. Here, we evaluate the feasibffity of using trained data-driven machine learning models as fast and accurate substitutes for these expensive methods. We report on the efficacies of a variety of ML models, including convolutional neural networks, auto-encoders, random forests and combinations thereof, in addition to techniques such as transfer learning in predicting these structural parameters. Specifically, we evaluate two categories of models which we call class-conditional and integrated. The first relies on a two-stage inference pipeline in which a crystallographic class label is first predicted followed by regression to predict the length/angle parameters. In the second category, the classification and regression tasks are performed as a single learning task. We train these models on synthetically generated data, validate them against experimental observa-tions and show that integrated models outperform their class-conditional counterparts opening up the possibffity of deep learning models as a viable alternative to existing resource-intensive loop refinement methods in neutron data analysis. Computer science ; Artificial intelligence ; Machine learning ; Pipeline (software) ; Data set ; Encoder ; Inference ; Class (philosophy) ; Artificial neural network ; Set (abstract data type) ; Regression ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Jun Zhang | Yao-Kun Lei | Yaqiang Zhou | Yi Yang | Yi Qin Gao
Source: arXiv (Cornell University) (preprint, 2020-12-22)
Field: Materials Science — Materials Chemistry

Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems. Along with this trend arises the increasing demand of expressive and versatile neural network architectures which are compatible with molecular systems. A new deep neural network architecture, Molecular Configuration Transformer (Molecular CT), is introduced for this purpose. Molecular CT is composed of a relation-aware encoder module and a computationally universal geometry learning unit, thus able to account for the relational constraints between particles meanwhile scalable to different particle numbers and invariant with respect to the trans-rotational transforms. The computational efficiency and universality make Molecular CT versatile for a variety of molecular learning scenarios and especially appealing for transferable representation learning across different molecular systems. As examples, we show that Molecular CT enables representational learning for molecular systems at different scales, and achieves comparable or improved results on common benchmarks using a more light-weighted structure compared to baseline models. Universality (dynamical systems) ; Computer science ; Scalability ; Artificial intelligence ; Deep learning ; Artificial neural network ; Molecular model ; Representation (politics) ; Theoretical computer science ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Seismology and Earthquake Studies
Authors: Alain C. Vaucher | Philippe Schwaller | Joppe Geluykens | Vishnu H Nair | Anna Iuliano | Teodoro Laino
Source: ChemRxiv (preprint, 2020-10-21)
Field: Materials Science — Materials Chemistry

Inferring Experimental Procedures from Text-Based Representations of Chemical Reactions The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present, for the first time, data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. When evaluated on the ground truth data, the best performing model (transformer) achieves an accuracy of 72.7% for single action predictions, and a 100% match of the full action sequence for 3.6% of experimental procedures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases. Computer science ; Transformer ; Synthetic data ; Sequence (biology) ; Retrosynthetic analysis ; Representation (politics) ; Set (abstract data type) ; Context (archaeology) ; Artificial intelligence ; Experimental data ; Data mining ; Machine learning ; Chemistry ; Programming language ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques