jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zixin Zhuang | Amanda S. Barnard
Source: Chemistry of Materials (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Structure-Free Mendeleev Encodings of Material Compounds for Machine Learning Machine learning is a powerful tool to predict the properties of materials for a variety of applications. However, generating data sets of carefully characterized materials can be time-consuming and costly, particularly when numerous candidate materials are later found to be irrelevant. The problem could be alleviated if machine learning can be used with minimal information to provide guidance at an early stage before significant investment has been made. Since structural characterization is one of the most expensive parts of the process, this study explores structure-free encoding of materials using Mendeleev encoding, a method that does not require information such as lattice constants, lattice positions, or bonding networks. We evaluate Mendeleev encoding using three data sets of continuous, complex material compounds used for battery applications, with four different unsupervised learning methods, inclusive of six algorithms and four evaluation metrics and in addition visualizations of the results. Our results show that Mendeleev encoding is more accurate, stable, and reliable than alternative structure-free encoding, allowing both principle component analysis and archetypal analysis to capture more of the variance during dimensionality reduction and consistently provide superior clustering results. Mendeleev encoding is a simple and scientifically intuitive way of representing material data that is both human and machine-readable and is applicable to any machine-learning task training with tabular data. Computer science ; Artificial intelligence ; Encoding (memory) ; Machine learning ; Cluster analysis ; Curse of dimensionality ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ingo Dierking | Jason Dominguez | James Harbon | Joshua Heaton
Source: Liquid Crystals (article, 2023-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Testing different supervised machine learning architectures for the classification of liquid crystals Different convolutional neural network (CNN) and inception network architectures were trained for the classification of isotropic, nematic, cholesteric and smectic liquid crystal phase textures to test the prediction accuracy for each one of these models. Varying the number of layers and inception blocks, as well as the regularisation, and application to different phase transitions and classification tasks, it is shown that in general the architecture of an inception network with two blocks leads to the best classification results. Regularisation, such as image flipping, and dropout layers additionally somewhat increase the classification accuracy. Even for simple tasks like the isotropic-nematic transition, which is of importance for applications in the automatic readout of sensors, convolutional neural networks need more than one layer. Care must be taken to not apply architectures of too large complexity, as this will again reduce the classification accuracy due to overfitting. Architecture complexity needs to be adjusted to the given classification task Overfitting ; Dropout (neural networks) ; Liquid crystal ; Convolutional neural network ; Artificial intelligence ; Computer science ; Task (project management) ; Contextual image classification ; Artificial neural network ; Isotropy ; Pattern recognition (psychology) ; Materials science ; Phase (matter) ; Network architecture ; Machine learning ; Image (mathematics) ; Optics ; Physics ; Optoelectronics Liquid Crystal Research Advancements ; Visual perception and processing mechanisms ; Neurobiology and Insect Physiology Research
Authors: Alex M. Maldonado | Igor Poltavsky | Valentín Vassilev-Galindo | Alexandre Tkatchenko | John A. Keith
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Modeling molecular ensembles with gradient-domain machine learning force fields Gradient-domain machine learning (GDML) force fields show excellent accuracy, data efficiency, and applicability for molecules, and a many-body approach opens the possibility of increased transferability to molecular ensembles. Transferability ; Force field (fiction) ; Domain (mathematical analysis) ; Computer science ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: S. Agarwal | A. Sawant | Muhammad Faisal | S.E. Copp | July Reyes-Zacarias | Yan-Ru Lin | S.J. Zinkle
Source: Engineering Applications of Artificial Intelligence (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials Computer science ; Segmentation ; Artificial intelligence ; Bubble ; Pixel ; Deep learning ; Image resolution ; Image segmentation ; Computer vision ; Pattern recognition (psychology) ; Materials science Nuclear Materials and Properties ; Advanced X-ray and CT Imaging ; Nuclear reactor physics and engineering
Authors: Zhilei Wang | Toshiyuki Funada | Tetsuhiko Onda | Zhong‐Chun Chen
Source: Materials Today Physics (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Knowledge extraction and performance improvement of Bi2Te3-based thermoelectric materials by machine learning Materials science ; Thermoelectric effect ; Thermoelectric materials ; Extrusion ; Figure of merit ; Microstructure ; Serendipity ; Dopant ; Material Design ; Process engineering ; Nanotechnology ; Mechanical engineering ; Engineering physics ; Metallurgy ; Composite material ; Optoelectronics ; Doping ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Ziliang Lu | Jianwei Wang | Yuanfang Wu | Xiumei Guo | Tianrun Ma | Wei Xiao
Source: International Journal of Hydrogen Energy (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Prediction and theoretical investigation of dehydrogenation enthalpy of V–Ti–Cr–Fe alloy using machine learning and density functional theory The dehydrogenation enthalpy, a crucial thermodynamic parameter, determines the difficulty of hydrogen desorption in V–Ti–Cr–Fe alloy, but it is unaffordable to predict and modify the dehydrogenation enthalpy and investigate the synergistic effects of structural and chemical parameters relying on conventional experimental methods. In this paper, an extreme gradient boosting (Xgboost) model with feature selection is developed, which can obtain a relative error under 15% in prediction on most of the data. Moreover, the genetic algorithm is utilized for optimizing alloy composition having suitable dehydrogenation enthalpy. The accuracy of prediction and optimization is demonstrated by the subsequent experimental validation, where the error between calculation and experiment is within 4.5%. Density function theory (DFT) is used to analyze the key features provided by machine learning, with results suggesting that the volumes, valence electrons, and bulk module factors have a significant impact. This research supplies an efficient way of using machine learning to predict and optimize the dehydrogenation enthalpy. In addition, DFT calculation also validates the feature importance outputted by machine learning and reveals the principle of feature effects at the microscopic aspect. Dehydrogenation ; Enthalpy ; Density functional theory ; Extreme learning machine ; Alloy ; Materials science ; Thermodynamics ; Machine learning ; Computer science ; Hydrogen storage ; Feature selection ; Artificial intelligence ; Algorithm ; Chemistry ; Artificial neural network ; Computational chemistry ; Physics ; Metallurgy ; Catalysis Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Yiwen Lu | Dilek Yalçın | Paul J. Pigram | Lewis D. Blackman | Mario Boley
Source: Journal of Chemical Information and Modeling (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical phase diagrams whenever self-assemblies of novel monomer pairs are sought for specific applications. To alleviate this burden, we develop here the first framework for a data-driven methodology for the probabilistic modeling of PISA morphologies based on a selection and suitable adaption of statistical machine learning methods. As the complexity of PISA precludes generating large volumes of training data with <i>in silico</i> simulations, we focus on interpretable low variance methods that can be interrogated for conformity with chemical intuition and that promise to work well with only 592 training data points which we curated from the PISA literature. We found that among the evaluated linear models, generalized additive models, and rule and tree ensembles, all but the linear models show a decent interpolation performance with around 0.2 estimated error rate and 1 bit expected cross entropy loss (surprisal) when predicting the mixture of morphologies formed from monomer pairs already encountered in the training data. When considering extrapolation to new monomer combinations, the model performance is weaker but the best model (random forest) still achieves highly nontrivial prediction performance (0.27 error rate, 1.6 bit surprisal), which renders it a good candidate to support the creation of empirical phase diagrams for new monomers and conditions. Indeed, we find in three case studies that, when used to actively learn phase diagrams, the model is able to select a smart set of experiments that lead to satisfactory phase diagrams after observing only relatively few data points (5-16) for the targeted conditions. The data set as well as all model training and evaluation codes are publicly available through the GitHub repository of the last author. Computer science ; Random forest ; Artificial intelligence ; Probabilistic logic ; Machine learning ; Extrapolation ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Yanlei Kang | Jiahui Chen | Xiu-Rong Hu | Yunliang Jiang | Li Zhong
Source: CrystEngComm (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A cocrystal prediction method of graph neural networks based on molecular spatial information and global attention This paper proposes a PFGNet model based on graph neural network, which adds molecular 3D point cloud information to represent the global aspect of molecules through the global attention mechanism. Computer science ; Cocrystal ; Artificial neural network ; Graph ; Point cloud ; Theoretical computer science ; Global network ; Deep neural networks ; Artificial intelligence ; Data mining ; Molecule ; Chemistry ; Computer network ; Hydrogen bond Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Suming Chen | Jinjie You | Yuge Chen | Zeming Zhang | Aili Sun | Hua Liu | Xizhi Shi
Source: Sensors and Actuators B Chemical (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Broad application prospects near-infrared carbonized polymer dots combined with machine learning for the detection of Cu2+ in seawater and aquatic products Detection limit ; Fluorescence ; Materials science ; Quantum yield ; Aqueous solution ; Seawater ; Polymer ; Citric acid ; Nanotechnology ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Joshua Ojih | Chen Shen | Alejandro Rodriguez | Hongbin Zhang | Kamal Choudhary | Ming Hu
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models Using dual machine learning models, we identified 3218 inorganic crystals with ultralow lattice thermal conductivity (LTC), which will be of great interest for technologically important applications such as thermal insulators and thermoelectrics. Thermal conductivity ; Materials science ; Dual (grammatical number) ; Thermal ; Throughput ; Thermoelectric materials ; Lattice (music) ; Computer science ; Composite material ; Physics ; Thermodynamics ; Telecommunications Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Zhengheng Lian | Yingying Ma | Minjie Li | Wencong Lu | Wenyan Zhou
Source: Computational Materials Science (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery Metric (unit) ; Computer science ; Machine learning ; Set (abstract data type) ; Model selection ; Data mining ; Artificial intelligence ; Performance metric ; Selection (genetic algorithm) ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Computational Drug Discovery Methods
Authors: I-Ting Sung | Li‐Chiang Lin
Source: The Journal of Physical Chemistry C (article, 2023-07-10)
Field: Chemistry — Inorganic Chemistry

In Silico Study of Metal–Organic Frameworks for CO<sub>2</sub>/CO Separation: Molecular Simulations and Machine Learning Metal–organic frameworks (MOFs), an emerging class of nanoporous materials, have drawn considerable attention as promising adsorbents for gas separations. Among various separation applications, CO2/CO separation is of particular interest owing to its industrial relevance. While searching for promising MOFs from tens of thousands of candidates represents a great challenge, this study conducts large-scale molecular simulations to identify top-performing CO2 adsorbents, followed by investigating structure–property relationships for their design. Optimal MOFs are found to possess features such as metal nodes of greater metallic charges and dipole moments with a relatively confined pore structure. With the large-scale data at our disposal, machine learning models capable of predicting the CO2-to-CO selectivity and adsorption uptakes are also established. Specifically, three algorithms including support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest (RF) models are employed. The results show that the RF algorithm demonstrates the best accuracy, and the r value for the predicted CO2-to-CO selectivity (S) can be as large as ∼0.88. The relative importance of the adopted features is also investigated with results suggesting that the adsorption of CO2 initiates more preferentially than that of CO due to the stronger van der Waals interaction and electrostatic contribution between CO2 and the metal sites. Finally, a design rule is proposed for the optimal design of CO2-selective materials. Overall, this work demonstrates a successful hybrid approach combining molecular simulations and machine learning for screening highly CO2/CO selective MOFs and offering insights into the design of optimal adsorbents. Nanoporous ; Metal-organic framework ; Support vector machine ; Adsorption ; Computer science ; van der Waals force ; Selectivity ; Artificial intelligence ; Machine learning ; Gas separation ; Boosting (machine learning) ; Materials science ; Nanotechnology ; Chemistry ; Molecule ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Yanfei Guan | Taegyo Lee | Ke Wang | Shu Yu | J. Christopher McWilliams
Source: Journal of Chemical Information and Modeling (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

S <sub>N</sub> Ar Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state theory (TST) and machine learning (ML) methods has been widely used to predict reaction outcomes such as selectivity. However, TST reaction modeling and ML methods are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging ML and TST modeling by triggering resource-intensive but much less domain-sensitive DFT calculations only on less confident ML predictions. The proposed workflow was trained and tested on both the Pfizer internal dataset and the USPTO public dataset to predict regioselectivity for S<sub>N</sub>Ar reactions. Our method is accurate and fast, which achieves 96.3 and 94.7% accuracy in predicting the correct major product on Pfizer and USPTO datasets, respectively, in a fraction of conventional TST computing time. Regioselectivity ; Computer science ; Workflow ; Density functional theory ; Chemistry ; Computational chemistry ; Catalysis ; Database ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Zeyu Wang | Jinlong Ma | Run Hu | Xiaobing Luo
Source: Applied Physics Letters (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning Rapid identification of lattice thermal conductivity of semiconductors from their crystal structure is required in the discovery of functional materials. A promising strategy is using a machine learning method based on a first-principles dataset, which, however, suffers from the dilemma of too little data available. In this work, the crystal graph convolutional neural networks (CGCNN) model was improved by enhancing the information of atomic descriptors (for short CGCNN-D), and the transfer learning (TL) method was combined to overcome the problem of small datasets. It is found that the CGCNN-D has improved predicting performance for both electronic bandgap with large data volume and thermal conductivity with small data volume, with the mean absolute error reducing 7% and 10%, respectively, indicating the importance of the improved atomic description. Applying TL with electronic bandgap as a proxy into the CGCNN-D further upgrades the prediction accuracy for thermal conductivity that has only 95 pieces of data, yielding 19% decrease in the mean absolute error as compared to the original CGCNN. The trained CGCNN-D-TL model was used to quickly estimate the thermal conductivities of thousands of semiconductors, and the materials identified with potentially high thermal conductivity were further screened by the optimized Slack model. Finally, the most promising BC2N was discovered and then confirmed by the first-principles calculations, which shows room-temperature thermal conductivities of 731, 594, and 500 W m−1 K–1 along the three principal axes of its lattice structure. Thermal conductivity ; Materials science ; Semiconductor ; Band gap ; Lattice (music) ; Machine learning ; Artificial intelligence ; Computer science ; Optoelectronics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Ye Ji | Arnd Koeppe | Patrick Altschuh | D. Rajagopal | Yinghan Zhao | Weijin Chen | Yi Zhang | Yupeng Zheng | Britta Nestler
Source: Computational Materials Science (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Towards automatic feature extraction and sample generation of grain structure by variational autoencoder Autoencoder ; Granularity ; Computer science ; Unsupervised learning ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Deep learning ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Nuclear Materials and Properties
Authors: Richard J. Mandle
Source: Liquid Crystals (article, 2023-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Automated flow synthesis of algorithmically designed ferroelectric nematogens The synthesis of the ferroelectric nematic material RM734 is telescoped into a single continuous flow process, utilising inline liquid–liquid separation to remove reactants and by-products from the reaction stream. Following the final synthetic step, we subject the semi-crude material to offline automated chromatography. Materials can be prepared on a gram scale (12 minutes) and chromatographically purified (8 minutes) in around 20 minutes. Over a given time interval the system described herein can prepare a far larger number of materials (on gram scale) than an experienced chemist. Secondly, we implement an established process for generation of new molecular structures via a genetic algorithm, which generates many new candidate structures per iteration. A filter removes undesirable structures, and a fast semi-empirical QM calculation is performed on those that remain. A neural network trained against a library of nematic and ferroelectric nematic materials is used to rank the candidate structures based on the QM output and a fingerprinting process, with the highest scoring candidates then used to seed a new round of mutations. We combine these two approaches; we generated and ranked several tens of thousands of variants of RM734 and synthesised a small library of materials using our continuous flow protocol. Materials science ; Ferroelectricity ; Liquid crystal ; Flow (mathematics) ; Optoelectronics ; Mechanics Liquid Crystal Research Advancements ; Electrowetting and Microfluidic Technologies ; Advanced Materials and Mechanics