jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mandana Saebi | Bozhao Nan | John C. Herr | Jessica Wahlers | Zhichun Guo | A. Zuranski | Thierry Kogej | Per‐Ola Norrby | Abigail G. Doyle | Olaf Wiest | Nitesh V. Chawla
Source: ChemRxiv (preprint, 2021-09-26)
Field: Materials Science — Materials Chemistry

On the Use of Real-World Datasets for Reaction Yield Prediction The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as good or better than the best previous models on two HTE datasets for the Suzuki and Buchwald-Hartwig reactions. However, training of the AGNN on the ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions. Bottleneck ; Computer science ; Yield (engineering) ; Key (lock) ; Task (project management) ; Machine learning ; Context (archaeology) ; Artificial intelligence ; Training set ; Graph ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Hao Luo | Kai Jin | Jie Tao | Hao Wang
Source: Materials Research Express (article, 2021-04-01)
Field: Materials Science — Polymers and Plastics

Properties prediction and design of self-healing epoxy resin combining molecular dynamics simulation and back propagation neural network Abstract It is difficult to synergistically improve the self-healing and mechanical properties of self-healing epoxy resin system based on Diels-Alder (DA) reversible covalent bond due to its different compositions. In this study, a method combining molecular dynamics (MD) simulation and back propagation (BP) neural network was employed to achieve a balance between self-healing property and mechanical property. In addition, self-healing and mechanical properties were predicted and the influence of different compositions of the system on these properties was investigated by the BP neural network. It was found that the DA bond formed by the furan group of furfuryl glycidyl ether (FGE) and the maleimide group of bismaleimide (BMI) was of great importance for self-healing property, and that diglycidyl ether of bisphenol-A (DGEBA) with a mass fraction of 25%–48% could improve the tensile property of the system. Finally, a self-healing epoxy resin system with superior comprehensive properties was designed through the proposed method under four indexes of Young’s modulus (E), self-healing efficiency ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> ), Ultimate Tensile Strength (UTS), and glass transition temperature (Tg). Epoxy ; Molecular dynamics ; Artificial neural network ; Self-healing ; Dynamics (music) ; Materials science ; Composite material ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Medicine ; Computational chemistry ; Acoustics Polymer composites and self-healing ; Photopolymerization techniques and applications ; Epoxy Resin Curing Processes
Authors: N. N. Kiselyova | Victor Dudarev | V. V. Ryazanov | O. V. Sen’ko | A. A. Dokukin
Source: Inorganic Materials Applied Research (article, 2021-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predictions of Chalcospinels with Composition ABCX4 (X = S or Se) New chalcospinels of the most common compositions were predicted: AIBIIICIVX4 (X = S or Se) and AIIBIIICIIIS4 (A, B, and C are various chemical elements). They are promising for the search for new materials for magneto-optical memory elements, sensors, and anodes in sodium-ion batteries. The parameter “a” values of their crystal lattice are estimated. When predicting, only the values of the properties of chemical elements were used. The calculations were carried out using machine learning programs that are part of the information-analytical system developed by the authors (various ensembles of algorithms of the binary decision trees, the linear machine, the search for logical regularities of classes, the support vector machine, Fisher linear discriminant, the k-nearest neighbors, the learning a multilayer perceptron, and a neural network) for predicting chalcospinels not yet obtained, as well as an extensive family of regression methods, presented in the scikit-learn package for the Python language, and multilevel machine learning methods that were proposed by the authors for estimation of the lattice parameter value of new chalcospinels. The prediction accuracy of new chalcospinels according to the results of the cross-validation is not lower than 80%, and the prediction accuracy of the parameter of their crystal lattice (according to the results of calculating the mean absolute error when cross-validation in the leave-one-out mode) is ±0.1 Å. The effectiveness of using multilevel machine learning methods to predict the physical properties of substances is shown. Artificial neural network ; Python (programming language) ; Artificial intelligence ; Support vector machine ; Machine learning ; Multilayer perceptron ; Linear discriminant analysis ; Computer science ; Lattice (music) ; Binary number ; Linear regression ; Perceptron ; Algorithm ; Mathematics ; Physics Crystal Structures and Properties ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Arsenios Gurras | Leonidas N. Gergidis
Source: Advanced Theory and Simulations (article, 2021-01-20)
Field: Chemistry — Inorganic Chemistry

Modeling Sorption and Diffusion of Alkanes, Alkenes, and their Mixtures in Silicalite: From MD and GCMC Molecular Simulations to Artificial Neural Networks Abstract Molecular dynamics (MD) in canonical (NVT) statistical ensemble and grand canonical‐Monte Carlo (GCMC) simulations along with artificial neural network (ANN) techniques are used for the study of diffusion and sorption characteristics of small alkanes, alkenes, and their mixtures in silicalite. In particular, sorption isotherms and self‐diffusion coefficients of alkanes (ethane to hexane), alkenes (ethene to hexene), and the respective alkane–alkene mixtures (consisting of the same number of carbon atoms) in silicalite are studied. The findings are directly compared with recent magic‐angle spinning pulsed field‐gradient nuclear magnetic resonance experimental diffusivity measurements and are in close agreement. Furthermore, new results are provided for the alkane–alkene systems. The sorption data from GCMC simulations, the self‐diffusivity calculations from the MD simulations along with available experimental data are used for the development of ANN predictive modeling procedures in order to give generic sorption and diffusion predictions for pure alkanes, alkenes in different input values of fugacity, temperature, and sorbate loadings at the minimum computational resources and time. Finally, structural characteristics for pure alkane, alkenes, and alkane–alkene mixtures when confined in the silicalite framework are computed revealing sorption domains and siting preferences. Alkane ; Sorption ; Alkene ; Thermal diffusivity ; Thermodynamics ; Chemistry ; Nonane ; Diffusion ; Physical chemistry ; Molecular dynamics ; Computational chemistry ; Hydrocarbon ; Organic chemistry ; Physics ; Adsorption ; Catalysis Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; NMR spectroscopy and applications
Authors: Emily Mazeau | Priyanka Satpute | Katrín Blöndal | Franklin Goldsmith | Richard H. West
Source: ChemRxiv (preprint, 2021-02-03)
Field: Materials Science — Materials Chemistry

Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane Kinetic parameters for surface reactions can be predicted using a combination of DFT calculations, scaling relations, and machine learning algorithms; however, construction of microkinetic models still requires a knowledge of all the possible, or at least reasonable, reaction pathways. The recently developed Reaction Mechanism Generator (RMG) for heterogeneous catalysis, now included in RMG version 3.0, is built upon well-established, open-source software that can provide detailed reaction mechanisms from user-supplied initial conditions without making a priori assumptions. RMG is now able to estimate adsorbate thermochemistry and construct detailed microkinetic models on a range of hypothetical metal surfaces using linear scaling relationships. These relationships are a simple, computationally efficient way to estimate adsorption energies by scaling the energy of a calculated surface species on one metal to any other metal. By conducting simulations with sensitivity analyses, users can not only determine the rate limiting step on each surface by plotting a "volcano surface" for the degree of rate control of each reaction as a function of elemental binding energies, but also screen novel catalysts for desirable properties. We investigated the catalytic partial oxidation of methane to demonstrate the utility of this new tool and determined that an inlet gas C/O ratio of 0.8 on a catalyst with carbon and oxygen binding energies of -6.75 eV and -5.0 eV, respectively, yields the highest amount of synthesis gas. Sensitivity analyses show that while the dissociative adsorption of O2 has the highest degree of rate control, the interactions between individual reactions and reactor conditions are complex, which result in a dynamic rate-limiting step across differing metals. Scaling ; Catalysis ; Chemistry ; Adsorption ; Thermochemistry ; Methane ; Sensitivity (control systems) ; Reaction rate ; Reaction mechanism ; Thermodynamics ; Physical chemistry ; Physics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Tolou Pourashraf | Saeid Shokri | Mohammad Yousefi | Abbas Ahmadi | Parviz Aberoomand Azar
Source: Advanced Theory and Simulations (article, 2021-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms Abstract Numerous studies have been performed to modify ferrites to achieve the desired magnetic properties for the intended applications. Many variables with interactions between themselves are involved in modifying these properties. This study aims to provide an accurate and effective method for predicting the magnetic properties of M‐type ferrites under the influence of involved variables. For this purpose, ferrites are synthesized by doping 17 different ions and the results of the analysis of samples form the experimental data. The support vector regression (SVR) model is selected for prediction and in order to optimize hyper parameters, genetic, particle swarm optimization, and sequential minimal optimization techniques are used. Based on the comparison between the outcomes of these algorithms, the model with the best performance is used to predict coercivity and residual magnetism in M‐type magnetic ferrites. Furthermore, with the cross validation technique, the accuracy and robustness of the model designed for new samples are evaluated. The results show that the selected SVR model, with an average absolute relative error of 2% and 0.7%, is able to predict coercivity and residual magnetism, respectively. Consequently, the prediction method presented has the capability to accelerate and develop the modification of magnetic properties for different applications. Particle swarm optimization ; Robustness (evolution) ; Coercivity ; Support vector machine ; Residual ; Magnetism ; Algorithm ; Computer science ; Machine learning ; Genetic algorithm ; Artificial intelligence ; Materials science ; Chemistry ; Physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Machine Learning in Materials Science
Authors: Jiansu Pu | Hui Shao | Boyang Gao | Zhengguo Zhu | Yanlin Zhu | Yunbo Rao | Yong Xiang
Source: IEEE Transactions on Visualization and Computer Graphics (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

matExplorer: Visual Exploration on Predicting Ionic Conductivity for Solid-state Electrolytes Lithium ion batteries (LIBs) are widely used as important energy sources for mobile phones, electric vehicles, and drones. Experts have attempted to replace liquid electrolytes with solid electrolytes that have wider electrochemical window and higher stability due to the potential safety risks, such as electrolyte leakage, flammable solvents, poor thermal stability, and many side reactions caused by liquid electrolytes. However, finding suitable alternative materials using traditional approaches is very difficult due to the incredibly high cost in searching. Machine learning (ML)-based methods are currently introduced and used for material prediction. However, learning tools designed for domain experts to conduct intuitive performance comparison and analysis of ML models are rare. In this case, we propose an interactive visualization system for experts to select suitable ML models and understand and explore the predication results comprehensively. Our system uses a multifaceted visualization scheme designed to support analysis from various perspectives, such as feature distribution, data similarity, model performance, and result presentation. Case studies with actual lab experiments have been conducted by the experts, and the final results confirmed the effectiveness and helpfulness of our system. Visualization ; Data visualization ; Flammable liquid ; SPARK (programming language) ; Helpfulness ; Usability ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Chia‐Chen Wu | Fei Pan | Yen‐Hsun Su
Source: Advanced Photonics Research (article, 2021-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface Plasmon Resonance of Gold Nano‐Sea‐Urchins Controlled by Machine‐Learning‐Based Regulation in Seed‐Mediated Growth Machine-Learning Predicted Surface Plasmon Resonance In article number 2100052, Chia-Chen Wu, Fei Pan, and Yen-Hsun Su employ genetic-algorithm-based artificial neural networks (GANNs) to coordinate the synthesis parameters with surface plasmon. A well-trained GANN model through machine learning and empirical database yields a precise projection of the surface plasmon wavelength of gold sea-urchin-like nanoparticles (GSNPs) via seed-mediated growth. The optimal fabrication parameters of GSNPs can thus be efficiently specified to meet a desired standard. Surface plasmon resonance ; Artificial neural network ; Surface plasmon ; Plasmon ; Artificial intelligence ; Gold coast ; Biological system ; Computer science ; Surface (topology) ; Nanoparticle ; Projection (relational algebra) ; Machine learning ; Materials science ; Nanotechnology ; Algorithm ; Biology ; Mathematics ; Optoelectronics ; Geography ; Geometry Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yuheng Zhou | Hui Deng | Xubo Huang | Yuntao Hu | Bin Ye | Linfang Lu
Source: Chemical Engineering Journal (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Predicting the oxidation of carbon monoxide on nanoporous gold by a deep-learning method Nanoporous ; Artificial neural network ; Carbon monoxide ; Catalysis ; Deep learning ; Computer science ; Artificial intelligence ; Nonlinear system ; Transformation (genetics) ; Network structure ; Materials science ; Nanotechnology ; Process engineering ; Machine learning ; Chemistry ; Engineering ; Organic chemistry Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Andrew C. Meng | Ke-Bin Low | Alexandre C. Foucher | Yuejin Li | Ivan Petrović | Eric A. Stach
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Anomalous metal vaporization from Pt/Pd/Al <sub>2</sub> O <sub>3</sub> under redox conditions Al2O3-supported Pt/Pd bimetallic catalysts were studied using in situ atmospheric pressure and ex situ transmission electron microscopy. Real-time observation during separate oxidation and reduction processes provides nanometer-scale structural details - both morphology and chemistry - of supported Pt/Pd particles at intermediate states not observable through typical ex situ experiments. Significant metal vaporization was observed at temperatures above 600 °C, both in pure oxygen and in air. This behavior implies that material transport through the vapor during typical catalyst aging processes for oxidation can play a more significant role in catalyst structural evolution than previously thought. Concomitantly, Pd diffusion away from metallic nanoparticles on the surface of Al2O3 can also contribute to the disappearance of metal particles. Electron micrographs from in situ oxidation experiments were mined for data, including particle number, size, and aspect ratio using machine learning image segmentation. Under oxidizing conditions, we observe not only a decrease in the number of metal particles but also a decrease in the surface area to volume ratio. Some of the metal that diffuses away from particles on the oxide support can be regenerated and reappears back on the catalyst support surface under reducing conditions. These observations provide insight on how rapid cycling between oxidative and reductive catalytic operating conditions affects catalyst structure. Bimetallic strip ; Vaporization ; Redox ; Transmission electron microscopy ; Materials science ; Catalysis ; In situ ; Metal ; Analytical Chemistry (journal) ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Nanotechnology ; Metallurgy ; Environmental chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Hankook Lee | Sung Soo Ahn | Seung-Woo Seo | You Young Song | Eunho Yang | Sungju Hwang | Jinwoo Shin
Source: arXiv (Cornell University) (preprint, 2021-05-03)
Field: Materials Science — Materials Chemistry

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO {database} are given as candidates, our RetCL achieves top-1 exact match accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches. Retrosynthetic analysis ; Benchmark (surveying) ; Selection (genetic algorithm) ; Computer science ; Artificial intelligence ; Template ; Machine learning ; Set (abstract data type) ; Graph ; Natural language processing ; Theoretical computer science ; Programming language ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Bowen Zheng | Zeyu Zheng | Grace X. Gu
Source: Nanomaterials (article, 2021-09-09)
Field: Materials Science — Materials Chemistry

Scalable Graphene Defect Prediction Using Transferable Learning Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials. Graphene ; Scalability ; Machine learning ; Computer science ; Materials science ; Artificial intelligence ; Electronics ; Nanotechnology ; Range (aeronautics) ; Engineering Machine Learning in Materials Science ; Graphene research and applications ; Fuel Cells and Related Materials
Authors: Vu Ngoc Tuoc | Nga T. T. Nguyen | Vinit Sharma | Tran Doan Huan
Source: Physical Review Materials (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Probabilistic deep learning approach for targeted hybrid organic-inorganic perovskites We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic structures for each of 192 chemically distinct HOIP formulas. Therefore, any property prediction on a given formula must be associated with an irreducible ``uncertainty'' that comes from its unknown atomic details. As a result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50 eV were identified and validated against suitable first-principles computations. Through this demonstration we show that the probabilistic deep learning approach is robust, versatile, and can be used to properly quantify this uncertainty. In conclusion, the probabilistic standpoint and approach described herein could be widely useful for the very common and inevitable data uncertainty which is rooted at the incompleteness of information during experiments and/or computations. Probabilistic logic ; Computation ; Property (philosophy) ; Set (abstract data type) ; Band gap ; Work (physics) ; Materials informatics ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications