jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vidushi Sharma | Maxwell J. Giammona | Dmitry Yu. Zubarev | Tim Erdmann | Andy Tek | Khanh Nugyuen | Young-Hye Na
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Materials Chemistry

Transfer Learning Enabled Deep Learning Model for the Prediction of Battery Performance from Electrolyte Formulations Next generation batteries utilizing lithium (Li) metal anode could represent a 10x improvement in anode specific capacity compared to currently used graphite anodes however they still face major challenges associated with their evenness of plating and interfacial stability with the electrolyte. The capability to quickly evaluate and optimize liquid electrolyte formulations that can form stable solid-electrolyte interface (SEI) layers and bring out the best battery performance across a range of relevant performance outcomes will be critical to the realization of these next generation batteries. Integrating AI into battery research and development workflows has the potential to significantly increase the rate of new materials discovery, however, while machine learning models can successfully predict expected specific capacity or cycle life of the battery based on electrode designs, the complexity of liquid electrolytes used in batteries needs more contextual information than a machine or deep learning models can capture from simple structure-performance correlations. The lithium metal anode SEI layer is a formed out of the complex physio-chemical interactions between the lithium metal and the electrolyte and models will likely need to be able to capture those molecular attributes within the electrolyte composition that are decisive in electrolyte-Li interfacial chemistry. For this purpose, we will discuss the scope of two AI approaches that can predict battery performance based on electrolyte composition. The first approach showcases the capability of a deep learning model that is designed specially to represent electrolyte formulation based on structure-to-performance relationship. The second approach improves upon the first by incorporating transfer learning of quantum chemical attributes of electrolyte molecules and building a property-to-performance correlation. We will also discuss how transfer learning of ‘selective’ quantum chemical properties of electrolyte molecules to the capacity predicting deep learning model broadens the scope of the model towards recognizing new beneficial additives to the electrolyte formulation. Using this approach, the model retains impressive predictive accuracies despite limited experimental training data and predictions are validated and improved with experimental feedback for new electrolyte formulations in coin cell tests. Electrolyte ; Anode ; Battery (electricity) ; Computer science ; Transfer of learning ; Lithium–sulfur battery ; Materials science ; Process engineering ; Nanotechnology ; Artificial intelligence ; Electrode ; Chemistry ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Meltem Süpürtülü | Çiğdem DİNDAR | Recep AKYÜZ
Source: Afyon Kocatepe University Journal of Sciences and Engineering (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Implementation of Machine Learning Approaches for Crystal Structure Estimation in Lithium-ion Battery Cathode Materials It has commonly been assumed that the physical and chemical characteristics of lithium-ion silicate cathodes are influenced significantly by the crystal structure system. Because of this, crystal structure estimation has played a vital role in bringing about forecasting many other features of cathodes in battery applications. Using a variety of classification techniques in machine learning which three primary crystal structure (monoclinic, orthorhombic, and triclinic) of silicate-based cathode materials with compound systems of Li-Si-(Mn,Fe,Co)-O has been estimated. The computations are based on the Materials Project's density functional theory computations. In this study, it has been explained that based on property evaluation in statistical models, the considerable correlation between the crystal system and other physical characteristics of the cathodes was validated. Furthermore, the parameters of several categorization techniques have been tuned in order to achieve maximum prediction accuracy. This case has shown that the XGBoost and Support Vector Classifier algorithms carried out in this study the highest forecasting accuracy in this study along with many other classification methods in cross-validation tests. Orthorhombic crystal system ; Monoclinic crystal system ; Triclinic crystal system ; Cathode ; Crystal structure ; Materials science ; Artificial intelligence ; Machine learning ; Lithium-ion battery ; Crystal (programming language) ; Computer science ; Support vector machine ; Battery (electricity) ; Chemistry ; Crystallography ; Physics ; Physical chemistry ; Thermodynamics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Non-Destructive Testing Techniques
Authors: Lina Liu | Yuhao Qiao | Dongxia Wang | Xiaoguang Tian | Fei-Yue Qin
Source: Journal of Circuits Systems and Computers (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

A Neural Network-Based Method for Surface Metallization of Polymer Materials It’s no secret that polymers have been employed extensively in a variety of industries. Polymers, on the other hand, have faced difficulties in their development because of their complicated chemical composition and structure. Data-driven approaches in polymer science and technology have resulted in new directions in research leading to the implementation of deep learning models and vast data assets. In the growing area of polymer informatics, deep learning methods based on factual data are being used to speed up the performance assessment and process improvement of new polymers. Using a deep neural network (DNN), we can now forecast the surface metallization properties of polymer materials, which we describe in this research. First, we collect a raw dataset of polymer materials’ characteristics. The raw data are filtered and normalized using the min–max normalization approach. To convert normalized data into numerical characteristics, principal component analysis (PCA) is employed. Polymer surface metallization characteristics can then be predicted using a suggested DNN technique. The proposed and conventional approaches are also compared so that our research can be done to its full potential. Normalization (sociology) ; Artificial neural network ; Polymer ; Deep learning ; Principal component analysis ; Computer science ; Database normalization ; Artificial intelligence ; Raw data ; Process (computing) ; Materials science ; Surface (topology) ; Process engineering ; Machine learning ; Pattern recognition (psychology) ; Engineering ; Composite material ; Mathematics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Electron and X-Ray Spectroscopy Techniques
Authors: An Su | Chengwei Zhang | Yuanbin She | Yun‐Fang Yang
Source: ChemRxiv (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures Metalloporphyrins have been studied as biomimetic catalysts for more than 120 years and have accumulated a large amount of data, which provides a solid foundation for deep learning to discover chemical trends and structure-function relationships. In this study, key components of deep learning of metalloporphyrins, including databases, molecular representations, and model architectures, were systematically investigated. A protocol to construct canonical SMILES for metalloporphyrins was proposed, which was then used to represent the two-dimensional structures of over 10,000 metalloporphyrins in an existing computational database. Subsequently, several state-of-the-art chemical deep learning models, including graph neural network-based models and natural language processing-based models, were employed to predict the energy gaps of metalloporphyrins. Two models showed satisfactory predictive performance (R2>0.94) with canonical SMILES as the only source of structural information. In addition, an unsupervised visualization algorithm was used to interpret the molecular features learned by the deep learning models. Deep learning ; Computer science ; Artificial intelligence ; Construct (python library) ; Artificial neural network ; Graph ; Machine learning ; Database ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Zhilong Liang | Zhiwei Li | Shuo Zhou | Yiwen Sun | Jinying Yuan | Changshui Zhang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility Compatibility (geochemistry) ; Computer science ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Property value ; Key (lock) ; Data mining ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Ruslan Lukin | Maxim Faleev | Rostislav Grigorev
Source: ChemRxiv (preprint, 2022-04-25)
Field: Materials Science — Materials Chemistry

Revising Message Passing Graph Neural Networks for Catalyst Design Applying deep learning to search for catalysts is an important challenge for solving energy storage problems and the conversion of greenhouse gases into more valuable products caused by global warming. We present several graph neural networks (GNNs) in our work, including convolutional and message passing architectures with physically informed node and edge attributes for atomistic systems. We demonstrate an improvement in adsorption energy predictions on the OC20 dataset using our proposed architecture in terms of the mean absolute error of predicted energy and energy within threshold metrics. Proposed architectures are stable to overfitting and can be used to predict experimental and quantum chemical properties of a wide spectrum of materials and molecules. We propose the use of two GNN architectures (EdgeUpdateNet and OFMNet) together with an advanced featurization method of nodes and edges. We represent edge fingerprints as elements of interatomic interaction matrices (Coulomb matrix, Ewald sum matrix, Sine matrix). For node fingerprints, we use elements of the orbital field matrix (OFM), a one-hot representation of the electronic state of atoms with surrounding atomic orbitals. Also, we propose and implement the representation of catalytically active atoms as a subgraph. Proposed methods and architectures demonstrate improvement in the accuracy of adsorption energy predictions. Especially significant improvements are observed in out of domain examples for both adsorbates and catalysts. The generalizability and extrapolation capabilities on out of domain examples of the proposed architectures also make proposed GNNs feasible for use in catalyst screening in the vast chemical space. Computer science ; Overfitting ; Convolutional neural network ; Graph ; Theoretical computer science ; Node (physics) ; Artificial neural network ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: R. Yu. Lukin | R. A. Grigoriev
Source: Doklady Mathematics (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Geometric Deep Learning for Design of Catalysts and Molecules Application of deep learning to search for catalysts is an important challenge for solving problems caused by global warming, such as energy storage and conversion of greenhouse gases into more valuable products. In this work, several graph neural networks (GNNs) are presented, including convolutional and message passing architectures with physically informed node and edge attributes for atomistic systems. It is shown that the proposed architectures lead to an improvement in adsorption energy prediction on the OC20 dataset in terms of the mean absolute error of predicted energy and energy within threshold metrics. The proposed architectures are robust to overfitting and can be used to predict experimental and quantum chemical properties of a wide range of materials and molecules. Two GNN architectures (EdgeUpdateNet and OFMNet) with advanced featurization of nodes and edges are proposed. Edge fingerprints are represented as elements of interatomic interaction matrices (Coulomb matrix, Ewald sum matrix, sine matrix). For node fingerprints, we use elements of the orbital field matrix, which is a one-hot representation of the electronic state of atoms with surrounding atomic orbitals. Additionally, a representation of catalytically active atoms in the form of a subgraph is proposed and implemented. The proposed methods and architectures demonstrate an improvement in the accuracy of adsorption energy prediction. Especially significant improvements are observed in out-of-domain examples for both adsorbates and catalysts. The generalization and extrapolation capabilities of the proposed GNN architectures make them feasible for catalyst screening in a vast chemical space. Computer science ; Overfitting ; Convolutional neural network ; Theoretical computer science ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Rishi Gurnani | Christopher Kuenneth | Aubrey R. Toland | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2022-09-27)
Field: Materials Science — Materials Chemistry

Polymer informatics at-scale with multitask graph neural networks Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units -- a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly "machine-learning" important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand. Our approach -- based on graph neural networks, multitask learning, and other advanced deep learning techniques -- speeds up feature extraction by one to two orders of magnitude relative to presently adopted handcrafted methods without compromising model accuracy for a variety of polymer property prediction tasks. We anticipate that our approach, which unlocks the screening of truly massive polymer libraries at scale, will enable more sophisticated and large scale screening technologies in the field of polymer informatics. Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Task (project management) ; Deep learning ; Variety (cybernetics) ; Scale (ratio) ; Informatics ; Graph ; Polymer ; Field (mathematics) ; Selection (genetic algorithm) ; Data science ; Theoretical computer science ; Systems engineering ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Md Saef Ullah Miah | Junaida Sulaiman
Source: arXiv (Cornell University) (preprint, 2022-11-04)
Field: Materials Science — Materials Chemistry

Material Named Entity Recognition (MNER) for Knowledge-driven Materials Using Deep Learning Approach The scientific literature contains a wealth of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical data from experimental results, material properties and structure). These data are critical for data-driven machine learning (ML) and deep learning (DL) methods to accelerate material discovery. Due to the large and growing number of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. The proposed deep neural network-based model achieves an f-1 score of \~97\% for the Material Named Entity Recognition (MNER) task. The study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. The analysis provides insight into the results of the experiment and points to future directions for current research. Computer science ; Deep learning ; Artificial intelligence ; Artificial neural network ; Context (archaeology) ; Hyperparameter ; Task (project management) ; Machine learning ; Field (mathematics) ; Knowledge extraction ; Deep neural networks ; Enhanced Data Rates for GSM Evolution ; Knowledge base ; Data science ; Natural language processing ; Engineering Machine Learning in Materials Science ; Text and Document Classification Technologies
Authors: Nihang Fu | Jeffrey Hu | Ying Feng | Gregory Morrison | Hans‐Conrad zur Loye | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Composition based oxidation state prediction of materials using deep learning Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition based oxidation state prediction still remains elusive so far, which is more important in new material discovery for which the structures are not even available. This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition. Our model achieves 96.82\% accuracy for all-element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61\% accuracy for oxide materials. We also demonstrate how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery. Oxidation state ; Computer science ; Heuristic ; Ionic bonding ; Oxide ; Transformer ; Artificial intelligence ; Machine learning ; Ion ; Algorithm ; Materials science ; Metal ; Chemistry ; Voltage ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kangjie Lin | Junren Li | Haoyu Lin | Jianfeng Pei | Luhua Lai
Source: ChemRxiv (preprint, 2022-08-30)
Field: Materials Science — Materials Chemistry

Reaxtica: A knowledge-guided machine learning platform for fast and accurate reaction selectivity and yield prediction Reaction selectivity and yield prediction are important for chemical synthesis. Most existing computational methods use either computational expensive and complicated quantum mechanics-based models that are not easy for experimental chemists to use or black-box deep learning models that do not generalize well outside of the training space and lack explanation. Herein, using convenient physics-based electronic descriptors and structure-based steric descriptors, we developed an explainable machine learning platform, Reaxtica, that outperformed previous methods in four different reaction types and tasks, including regioselectivity, site-selectivity, enantioselectivity, and yield predictions. Further descriptor analysis helps understand reaction mechanisms behind the data. As a practical and robust toolbox, Reaxtica can be easily applied to different chemical reactions and extended to out-of-sample reaction. To assist chemists’ daily research, we further built an easy-to-use webserver, which only takes seconds to run and can be accessed at http://www.pkumdl.cn:8000/reaxtica/. Toolbox ; Yield (engineering) ; Computer science ; Regioselectivity ; Artificial intelligence ; Machine learning ; Selectivity ; Steric effects ; Chemical space ; Chemistry ; Materials science ; Programming language ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: himanshu . | Tarak K. Patra
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning model is intricately connected to its topology and the volume of training data. There is no facile protocol available to select a deep learning architecture, and there is a lack of a large volume of homogeneous sequence-property data of polymers. These two factors are the primary bottleneck for the efficient development of deep learning models. Here we assess the severity of these factors and propose new algorithms to address them. We show that a linear layer-by-layer expansion of a neural network can help in identifying the best neural network topology for a given problem. Moreover, we map the discrete sequence space of a polymer to a continuous one-dimensional latent space using a machine learning pipeline to identify minimal data points for building a universal deep learning model. We implement these approaches for three representative cases of building sequence-property surrogate models, viz., the single-molecule radius of gyration of a copolymer, adhesive free energy of a copolymer, and copolymer compatibilizer, demonstrating the generality of the proposed strategies. This work establishes efficient methods for building universal deep learning models with minimal data and hyperparameters for predicting sequence-defined properties of polymers. Deep learning ; Artificial intelligence ; Computer science ; Machine learning ; Hyperparameter ; Artificial neural network ; Sequence (biology) ; Surrogate model ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization