jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hari Narayan Bhargaw | Samarth Singh | B. A. Botre | Poonam Sinha
Source: Engineering Research Express (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

Differential resistance based self-sensing recurrent neural network model for position estimation and control of antagonistic Shape Memory Alloy actuator Abstract The paper presents the development and experimental investigation of recurrent neural network (RNN) based self-sensing position estimation (SSPE) model for shape memory alloy actuator (SMA). RNN is used as an estimator in the position feedback control loop to replace the external additional position sensor. The model was inspired by the physics-based analogy of the Mass-Spring-Damper (MSD) system for the antagonistic SMA wire actuator. Actuator displacement presents the hysteresis and non-linear dynamic relationship with observed differential resistance (sensing signal) during phase transformation. The resistance variation causes due to dissimilar resistivity between primary phases of SMA material. The RNN based estimation model was considered because it consists of memory elements for storing the processed information and feedback connections for dynamic modeling of the system. RNN was trained with input sensing signal and target displacement datasets. The estimation accuracy of the model was real-time evaluated during trajectory tracking of reference signals in the feedback control loop. A quantitative performance analysis is assessed in terms of correlation (ℛ), mean absolute error (MEA), and root mean square error (RSME) of the learned model along with the developed actuator system. The tracking results confirm the close agreement between estimated and measured displacement at a reasonable accuracy. Control theory (sociology) ; Actuator ; Recurrent neural network ; Displacement (psychology) ; SMA* ; Position (finance) ; Estimator ; Computer science ; Feed forward ; Artificial neural network ; Mean squared error ; Artificial intelligence ; Engineering ; Control engineering ; Algorithm ; Mathematics ; Statistics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Structural Health Monitoring Techniques
Authors: Li‐Cheng Xu | Shuo‐Qing Zhang | Xin Li | Miao‐Jiong Tang | Pei‐Pei Xie | Xin Hong
Source: Angewandte Chemie (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning Abstract Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time‐ and resource‐consuming process due to the lack of predictive catalyst design strategy. Targeting the data‐driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few‐shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening. Olefin fiber ; Enantioselective synthesis ; Asymmetric hydrogenation ; Catalysis ; Computer science ; Substrate (aquarium) ; Process (computing) ; Chemistry ; Combinatorial chemistry ; Database ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Topic Modeling
Authors: Hossein Shokri | Ben Gardner | P. Fowler | Timotej Bernat | John A. Stone | Gary A. Eiceman
Source: International Journal of Mass Spectrometry (article, 2021-09-08)
Field: Chemistry — Spectroscopy

Successive reactions in field induced fragmentation spectra from tandem ion mobility spectrometry at ambient pressure and their influence on classification by neural networks Chemistry ; Fragmentation (computing) ; Ion ; Analytical Chemistry (journal) ; Spectral line ; Mass spectrum ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical chemistry methods development
Authors: A. I. Gircha | Aleksey S. Boev | K. Avchaciov | П. О. Федичев | Aleksey K. Fedorov
Source: arXiv (Cornell University) (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Hybrid quantum-classical machine learning for generative chemistry and drug design Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications. chEMBL ; Chemical space ; Autoencoder ; Computer science ; Drug discovery ; Boltzmann machine ; Restricted Boltzmann machine ; Artificial intelligence ; Deep learning ; Generative model ; Quantum chemical ; Quantum ; Quantum chemistry ; Generative grammar ; Theoretical computer science ; Machine learning ; Chemistry ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Hiroaki Matsumoto
Source: ISIJ International (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Globularization and Recrystallization of a Ti-6Al-2Sn-4Zr-6Mo Alloy under Forging in the (&lt;i&gt;α&lt;/i&gt;+&lt;i&gt;β&lt;/i&gt;) Region: Experiment, Phenomenological Modeling and Machine Learning The Ti–6Al–2Sn–4Zr–6Mo (Ti-6246) alloy is an α + β titanium alloy with good combination of strength, toughness, high temperature creep property and crack growth resistance. This work examined the dynamic microstructural conversion behavior (specifically focusing on dynamic globularization of α phase) occurred under hot forging process (at temperatures ranging from 750°C to 1050°C) of the Ti-6246 alloy with various kinds of starting microstructure. Plastic flow behavior of the microstructure having the α-lamellae exhibited a peak stress followed by continuous flow softening due to frequent occurrence of lamellae kinking and globularization of the α phase. For the microstructural conversion of β phase, continuous dynamic recrystallization (CDRX) was dominantly occurred. And the enhanced CDRX is observed for the lamellar starting microstructure as compared to the bimodal and the equiaxed starting microstructures. On the globularization kinetics according to a phenomenological Avrami approach and a machine learning approach, when the processing conditions were categorized into 3 groups from a hierarchical clustering, the estimated globularization fraction from Avrami approach corresponded reasonably well with the experimental data only in a specific process condition group in which corresponded with a higher temperature and a higher dynamic globularization fraction. Thus, this work points out the possibility of a reliable prediction of dynamic globularization according to an optimal combination of the Avrami approach and the machine learning approach. Materials science ; Microstructure ; Dynamic recrystallization ; Creep ; Recrystallization (geology) ; Alloy ; Equiaxed crystals ; Forging ; Titanium alloy ; Lamellar structure ; Phenomenological model ; Hot working ; Composite material ; Metallurgy ; Mathematics Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Alexander V. Yakubovich | A. V. Odinokov | Sergey Nikolenko | Yongsik Jung | Hyeonho Choi
Source: Frontiers in Chemistry (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Computational Discovery of TTF Molecules with Deep Generative Models We present a computational workflow based on quantum chemical calculations and generative models based on deep neural networks for the discovery of novel materials. We apply the developed workflow to search for molecules suitable for the fusion of triplet-triplet excitations (triplet-triplet fusion, TTF) in blue OLED devices. By applying generative machine learning models, we have been able to pinpoint the most promising regions of the chemical space for further exploration. Another neural network based on graph convolutions was trained to predict excitation energies; with this network, we estimate the alignment of energy levels and filter molecules before running time-consuming quantum chemical calculations. We present a comprehensive computational evaluation of several generative models, choosing a modification of the Junction Tree VAE (JT-VAE) as the best one in this application. The proposed approach can be useful for computer-aided design of materials with energy level alignment favorable for efficient energy transfer, triplet harvesting, and exciton fusion processes, which are crucial for the development of the next generation OLED materials. Computer science ; Workflow ; Chemical space ; Artificial neural network ; Artificial intelligence ; Deep learning ; Generative grammar ; Chemistry ; Drug discovery ; Database Machine Learning in Materials Science ; Complex Network Analysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Yousef Mohammadi | Mohammad Reza Saeb | Alexander Penlidis | Esmaiel Jabbari | Florian J. Stadler | Philippe Zinck | Eduardo Vivaldo‐Lima
Source: Macromolecular Theory and Simulations (article, 2021-04-13)
Field: Chemistry — Organic Chemistry

Toward Olefin Multiblock Copolymers with Tailored Properties: A Molecular Perspective Abstract Recent progress in macromolecular reaction engineering has enabled the synthesis of sequence‐controlled polymers. The advent of olefin block copolymers (OBCs) via chain shuttling polymerization of ethylene with α‐olefins has opened new horizons for the synthesis of polyolefins having a dual character of thermoplastics and elastomers. Nevertheless, the use of two catalysts with different comonomer selectivities and a chain shuttling agent, dragging and dropping live chains between active catalyst centers, made precise tailoring of OBCs microstructure containing hard and soft units a feasible challenge. This work discusses the possibility of predicting properties of OBCs from its simulated molecular patterns. The microstructural characteristics of OBCs are discussed in terms of topology‐related and property‐related features. An intelligent tool, which combines the benefits of kinetic Monte Carlo simulation and artificial neural network modeling, is used to explore the connection between polymerization recipe (catalyst composition, ethylene to 1‐octene monomer ratio, and chain shuttling agent level) and topology‐related as well as property‐related microstructural features. The properties of target OBCs are reflected in the hard block percent, the number of 1‐octene units in the copolymer chains, and the longest ethylene sequence length of the hard and soft segments. Comonomer ; Copolymer ; Materials science ; Polymerization ; Monomer ; Elastomer ; Polymer ; Post-metallocene catalyst ; Ethylene ; Topology (electrical circuits) ; Catalysis ; Polymer chemistry ; Polymer science ; Organic chemistry ; Chemistry ; Metallocene ; Composite material ; Mathematics Advanced Polymer Synthesis and Characterization ; Organometallic Complex Synthesis and Catalysis ; Polymer crystallization and properties
Authors: Geemi P. Wellawatte | Aditi Seshadri | Andrew Dickson White
Source:  (preprint, 2021-09-06)
Field: Materials Science — Materials Chemistry

Model agnostic generation of counterfactual explanations for molecules An outstanding challenge in deep learning in chemistry is its lack of interpretability. The inability of explaining why a neural network makes a prediction is a major barrier to deployment of AI models. This not only dissuades chemists from using deep learning predictions, but also has led to neural networks learning spurious correlations that are difficult to notice. Counterfactuals are a category of explanations that provide a rationale behind a model prediction with satisfying properties like providing chemical structure insights. Yet, counterfactuals are have been previously limited to specific model architectures or required reinforcement learning as a separate process. In this work, we show a universal model-agnostic approach that can explain any black-box model prediction. We demonstrate this method on random forest models, sequence models, and graph neural networks in both classification and regression. Interpretability ; Spurious relationship ; Counterfactual thinking ; Counterfactual conditional ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Deep learning ; Reinforcement learning ; Recurrent neural network ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Na Li | Tianxin Zong | Zhigang Zhang
Source:  (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Prediction of the Electronic Work Function by Regression Algorithm in Machine Learning The electronic work function is a simple and basic parameter, which can well connect the properties of materials with their electronic behaviors. Three kinds of elastic modulus are used to predict the electronic work function of pure metals. Different machine learning methods are used to establish regression models, which are based on linear regression, decision tree, ensemble algorithm, support vector machine and neural network. The cross-validation method is used to improve the prediction accuracy and generalization ability, and the fit of the model is evaluated by several indicators. The results show that the fit of ensemble algorithm is better than other machine learning methods. Through this experiment, we can see that the model based on machine learning will not only become an accurate prediction of material properties, but also become a particularly useful tool to accelerate the design of alloy materials. Machine learning ; Decision tree ; Computer science ; Support vector machine ; Artificial neural network ; Artificial intelligence ; Algorithm ; Ensemble learning ; Generalization ; Function (biology) ; Online machine learning ; Regression analysis ; Regression ; Function approximation ; Mathematics ; Statistics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Corrosion Behavior and Inhibition
Authors: Xi Jiang | Nitash P. Balsara
Source: ACS Applied Polymer Materials (article, 2021-06-01)
Field: Materials Science — Surfaces, Coatings and Films

High-Resolution Imaging of Unstained Polymer Materials Electron microscopy has played an important role in polymer characterization. Traditionally, electron diffraction is used to study crystalline polymers while transmission electron microscopy is used to study microphase separation in stained block copolymers and other multiphase systems. We describe developments that eliminate the barrier between these two approaches—it is now possible to image polymer crystals with atomic resolution. The focus of this Review is on high-resolution imaging (30 Å and smaller) of unstained polymers. Recent advances in hardware allow for capturing numerous (as many as 105) low-dose images from an unperturbed specimen; beam damage is a significant barrier to high-resolution electron microscopy of polymers. Machine-learning-based software is then used to sort and average the images to retrieve pristine structural information from a collection of noisy images. Acknowledging the heterogeneity in polymer samples prior to averaging is essential. Molecular conformations in a wide range of amphiphilic block copolymers, polymerized ionic liquids, and conjugated polymers can be gleaned from two-dimensional projections (2D), three-dimensional (3D) tomograms, and four-dimensional (4D) scanning transmission electron microscopy (STEM) data sets where 2D diffraction patterns are taken as a function of position. Some methods such as phase contrast STEM have been used to image closely related materials such as metal–organic frameworks but not polymers. With improvements in hardware and software, such methods may soon be applied to polymers. Our goal is to provide a comprehensive understanding of the strategies toward the high-resolution imaging of radiation sensitive polymer materials at different length scales. Polymer ; Materials science ; Resolution (logic) ; Characterization (materials science) ; Scanning transmission electron microscopy ; Transmission electron microscopy ; Microscopy ; Nanotechnology ; Polymer characterization ; Image resolution ; Optics ; Computer science ; Energy filtered transmission electron microscopy ; Physics ; Composite material ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Taylor D. Sparks | Debanshu Banerjee
Source: Matter (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Materials informatics and polymer science: Pushing the frontiers of our understanding Generative grammar ; Glass transition ; Computer science ; Polymer ; Nanotechnology ; Generative model ; Data science ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Jeong Hoon Rhee | Sang-Il Kim | Kang Min Lee | Moon Kyum Kim | Yun Mook Lim
Source: Sensors (article, 2021-06-28)
Field: Chemistry — Analytical Chemistry

Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility. Silo ; Hotspot (geology) ; Detector ; Artificial neural network ; Algorithm ; Genetic algorithm ; Data mining ; Group method of data handling ; Computer science ; Position (finance) ; Real-time computing ; Engineering ; Artificial intelligence ; Machine learning ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Neural Networks and Applications
Authors: Michael J. Statt | Brian A. Rohr | Kris S. Brown | Dan Guevarra | Jens S. Hummelshøj | Linda Hung | abraham anapolsky | John M. Gregoire | Santosh K. Suram
Source: ChemRxiv (preprint, 2021-06-09)
Field: Materials Science — Materials Chemistry

ESAMP: Event-Sourced Architecture for Materials Provenance Management and Application to Accelerated Materials Discovery While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in data generation, ingestion, and materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence and interrelationships among events occurring in a simulation or experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in enabling several aspects of accelerated materials discovery such as dynamic workflow design, generation of knowledge graphs, and efficient integration of theory and experiment. Computer science ; Workflow ; Metadata ; Architecture ; Event (particle physics) ; Data science ; Synthetic data ; Distributed computing ; Data mining ; Database ; Artificial intelligence ; World Wide Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Sara Szymkuć | Tomasz Badowski | Bartosz A. Grzybowski
Source: Angewandte Chemie (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Is Organic Chemistry Really Growing Exponentially? Abstract In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well‐known methods. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction‐type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer‐assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases. Sublinear function ; Exponential growth ; Context (archaeology) ; Chemistry ; Popularity ; Function (biology) ; Computer science ; Exponential function ; Mathematics ; Combinatorics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis