jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fengrui Wang | Shiyi Qin | Claribel Acevedo-Vélez | Reid C. Van Lehn | Ví­ctor M. Zavala | David M. Lynn
Source: ACS Applied Materials & Interfaces (article, 2023-10-19)
Field: Chemistry — Spectroscopy

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights into surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles. Thermotropic crystal ; Materials science ; Amphiphile ; Aqueous solution ; Liquid crystal ; Sensitivity (control systems) ; Selectivity ; Decoding methods ; Nanotechnology ; Optoelectronics ; Liquid crystalline ; Organic chemistry ; Polymer ; Composite material ; Telecommunications ; Computer science ; Electronic engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Lipid Membrane Structure and Behavior
Authors: Genki Deguchi | Ryo Kobayashi | Hikaru Azuma | Shūji Ogata | Masayuki Uranagase | Samuele Spreafico
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Asymmetric Domain Nucleation from Dislocation Core in Barium Titanate: Molecular Dynamics Simulation Using Machine‐Learning Potential through Active Learning Barium titanate (BaTiO 3 ) is a ferroelectric material without toxic elements, whose ferroelectric properties such as permittivity, coercive field, and spontaneous polarization are affected by the nucleation of domains of reversed polarization and the motion of domain walls. Dislocations can act as obstacles to domain‐wall migration or as active sites for domain nucleation. Thus, studies are conducted on the utilization of dislocations to improve the ferroelectric properties of BaTiO 3 . However, the atomistic mechanism of domain nucleation around the dislocation core is still unclear. In this article, a machine learning (ML) potential is developed to study the influence of dislocations on domain nucleation. The potential is trained using an active‐learning approach to ensure accuracy in the bulk properties of the ferroelectric and paraelectric phases, as well as in the dislocation core structures in BaTiO 3 . Molecular dynamics simulations using the ML potential show that the influence of dislocations on polarization reversal depends on the directional relationship between the external electric field and the dislocation. Furthermore, strong local polarizations exist surrounding the dislocation core, owing to vacancies in the core. These polarizations can act as both domain nucleation sites and obstacles for domain migration when ordered along the dislocation line. Nucleation ; Ferroelectricity ; Barium titanate ; Materials science ; Dislocation ; Condensed matter physics ; Polarization (electrochemistry) ; Electric field ; Molecular dynamics ; Chemical physics ; Dielectric ; Composite material ; Physics ; Chemistry ; Computational chemistry ; Optoelectronics ; Physical chemistry ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Mesfin Diro Chaka | Chernet Amente Geffe | Álex Rodríguez | Nicola Seriani | Qin Wu | Yedilfana Setarge Mekonnen
Source: ACS Omega (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Promising Redox-Active Molecules with MolGAT Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from -4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB, QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as -2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing. Redox ; Computer science ; Chemistry ; Granularity ; Molecule ; chEMBL ; Chemical space ; Biological system ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Advanced battery technologies research ; Electrochemical Analysis and Applications
Authors: Ilia Kichev | Lyuben Borislаvov | Alia Tadjer | Radostina Stoyanova
Source: Materials (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of the Redox Activity of Quinones The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, these redox properties could be precisely tuned by using different substituent groups. Machine learning and statistics, on the other hand, have proven to be very powerful approaches for the efficient in silico design of novel materials. Herein, we demonstrated the machine learning approach for the prediction of the redox activity of quinones that potentially can serve as organic battery components. For the needs of the present study, a database of small quinone-derived molecules was created. A large number of quantum chemical and chemometric descriptors were generated for each molecule and, subsequently, different statistical approaches were applied to select the descriptors that most prominently characterized the relationship between the structure and the redox potential. Various machine learning methods for the screening of prospective organic battery electrode materials were deployed to select the most trustworthy strategy for the machine learning-aided design of organic redox materials. It was found that Ridge regression models perform better than Regression decision trees and Decision tree-based ensemble algorithms. Redox ; Quinone ; Computer science ; Machine learning ; Chemical space ; Ensemble learning ; Artificial intelligence ; Molecular descriptor ; Decision tree ; Battery (electricity) ; Quantitative structure–activity relationship ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Shuyuan Li | Yunjiang Zhang | Zhaolin Fang | Kong Meng | Rui Tian | Hong He | Shaorui Sun
Source: Journal of Chemical Information and Modeling (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%. Computer science ; Paragraph ; Parsing ; Flowchart ; Artificial intelligence ; Reduction (mathematics) ; Machine learning ; Natural language processing ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Giovanni Trezza | Eliodoro Chiavazzo
Source: Materials Today Communications (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Leveraging composition-based energy material descriptors for machine learning models A comparison of several classifiers is presented, with a focus on the key choice and construction of a minimal set of suitable material features. To this end, an investigation is conducted over a properly selected and high quality database reporting low temperature superconductors, featurized by composition-based descriptors. Fully general strategies to reduce the number of descriptors for material classification are proposed and discussed. The first strategy aims at testing possible invariance of the target material property (here the critical temperature) with respect to (binary) groups of composition-based features in the form xiaxjb,a,b∈R. In addition, a multi-objective optimization procedure for reducing the set of composition-based material descriptors is also suggested and tested on the chosen use case. The latter procedure is then proven to be particularly convenient to be used in combination with Bayesian type classifiers. Finally, by means of the best-performing classification models, an analysis is conducted over all the ∼40,000 inorganic compounds without Ni, Fe, Cu, O in Materials Project (and not in the SuperCon database, here used for model training) and the corresponding predictions are provided. Among those, 41 materials are classified to show Tc≥15K with a probability higher than or equal to 0.6. Set (abstract data type) ; Composition (language) ; Binary number ; Bayesian probability ; Materials science ; Computer science ; Property (philosophy) ; Quality (philosophy) ; Artificial intelligence ; Focus (optics) ; Key (lock) ; Machine learning ; Data mining ; Pattern recognition (psychology) ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexandre Duval | Victor Schmidt | Alex Hernandez | Santiago Miret | Fragkiskos D. Malliaros | Yoshua Bengio | David Rolnick
Source: arXiv (Cornell University) (preprint, 2023-04-28)
Field: Materials Science — Materials Chemistry

FAENet: Frame Averaging Equivariant GNN for Materials Modeling Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io. Equivariant map ; Scalability ; Computer science ; Invariant (physics) ; Homogeneous space ; Frame (networking) ; Theoretical computer science ; Representation (politics) ; Simple (philosophy) ; Algorithm ; Artificial intelligence ; Distributed computing ; Mathematics ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yaorui Shi | An Zhang | Enzhi Zhang | Zhiyuan Liu | Xiang Wang
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably those employing Graph Neural Networks (GNNs), are often limited by insufficient training data and their inability to utilize textual information, undermining their applicability in real-world applications. In this work, we propose **ReLM**, a novel framework that leverages the chemical knowledge encoded in language models (LMs) to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. To further enhance the model’s robustness and interpretability, we incorporate the confidence score strategy, enabling the LMs to self-assess the reliability of their predictions. Our experimental results demonstrate that ReLM improves the performance of state-of-the-art GNN-based methods across various chemical reaction datasets, especially in out-of-distribution settings. Codes are available at https://github.com/syr-cn/ReLM. Interpretability ; Computer science ; Robustness (evolution) ; Machine learning ; Artificial intelligence ; Reliability (semiconductor) ; Chemical process ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Danpeng Cheng | Wuxin Sha | Xu Zuo | Shide Li | Zhigao Yin | Yuling Lang | Shun Tang | Yuan‐Cheng Cao
Source: Science China Information Sciences (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

AtomGAN: unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale Scale (ratio) ; Atomic units ; Artificial intelligence ; Deep learning ; Computer science ; Materials science ; Physics ; Geography ; Cartography Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Jun Dai | Roman V. Krems
Source: Machine Learning Science and Technology (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules Abstract Kernel models of potential energy surfaces (PESs) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems with very expensive data, the functional form of the model kernels can be optimized in the Gaussian process (GP) setting through compositional function search guided by the Bayesian information criterion. However, the compositional kernel search is computationally demanding and relies on greedy strategies, which may yield sub-optimal kernels. An alternative strategy of increasing complexity of GP kernels treats a GP as a Bayesian neural network (NN) with a variable number of hidden layers, which yields NNGP models. Here, we present a direct comparison of GP models with composite kernels and NNGP models for applications aiming at the construction of global PES for polyatomic molecules. We show that NNGP models of PES can be trained much more efficiently and yield better generalization accuracy without relying on any specific form of the kernel function. We illustrate that NNGP models trained by distributions of energy points at low energies produce accurate predictions of PES at high energies. We also illustrate that NNGP models can extrapolate in the input variable space by building the free energy surface of the Heisenberg model trained in the paramagnetic phase and validated in the ferromagnetic phase. By construction, composite kernels yield more accurate models than kernels with a fixed functional form. Therefore, by illustrating that NNGP models outperform GP models with composite kernels, our work suggests that NNGP models should be a preferred choice of kernel models for PES. Gaussian process ; Kernel (algebra) ; Polyatomic ion ; Computer science ; Gaussian ; Function (biology) ; Gaussian function ; Artificial neural network ; Kriging ; Generalization ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Machine learning ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Tao Wei | Wancheng Yu | Xiangyu Zou | Wei Chen
Source: Journal of Magnetic Resonance (article, 2023-06-07)
Field: Chemistry — Spectroscopy

Machine learning assisted interpretation of 2D solid-state nuclear magnetic resonance spectra Solid-state nuclear magnetic resonance ; Heteronuclear molecule ; Artificial neural network ; Tensor (intrinsic definition) ; Biological system ; Anisotropy ; Spectral line ; NMR spectra database ; Robustness (evolution) ; Nuclear magnetic resonance ; Dipole ; Computer science ; Nuclear magnetic resonance spectroscopy ; Artificial intelligence ; Materials science ; Chemistry ; Chemical physics ; Physics ; Mathematics ; Optics ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Amit Samanta | Prasanna Balaprakash | Sylvie Aubry | Brian K. Lin
Source: Scripta Materialia (article, 2023-11-18)
Field: Materials Science — Materials Chemistry

Machine-learning-aided density functional theory calculations of stacking fault energies in steel Stacking-fault energy ; Materials science ; Stacking ; Stacking fault ; Manganese ; Density functional theory ; Fault (geology) ; Aluminium ; Condensed matter physics ; Crystallography ; Metallurgy ; Computational chemistry ; Dislocation ; Alloy ; Chemistry ; Composite material ; Physics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Oleh Yasniy | Iaroslav Pasternak | Iryna Didych | Sergiy Fedak | Dmytro Tymoshchuk
Source: Procedia Structural Integrity (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Methods of jump-like creep modeling of AMg6 aluminum alloy Different methods for investigating the jump-like creep of the AMg6 aluminium alloy based on its preliminary plastic deformation during tensile test under the mild loading mode are compared. It is more efficient to predict the magnitude of the jump during the creep by methods of machine learning, in particular, neural networks, which show the best results. It was found that the obtained models can make predictions according to the data that were not used in the training sample. The jump creep simulated by the neural network method agrees with the experimental data. It was determined that the prediction accuracy of neural networks is 96.2% in the test sample. Creep ; Jump ; Artificial neural network ; Materials science ; Alloy ; Aluminium ; Deformation (meteorology) ; Test data ; Ultimate tensile strength ; Aluminium alloy ; Sample (material) ; Structural engineering ; Composite material ; Computer science ; Engineering ; Artificial intelligence ; Physics ; Thermodynamics Material Properties and Failure Mechanisms ; Aerospace, Electronics, Mathematical Modeling ; Material Properties and Applications
Authors: K. Mahesh Prasanna | Aasheesh Shukla | K Tamizharasu. | Amit Ganatra | Atmaram Shelke | Ahmed Sayed M. Metwally | Sikandar Aftab
Source: Optical and Quantum Electronics (article, 2023-11-23)
Field: Materials Science — Polymers and Plastics

RETRACTED ARTICLE: AI-driven electro chromic materials and devices for nanofabrication in machine learning integrated environments Electrochromism ; Nanolithography ; Computer science ; Electrochromic devices ; Fabrication ; Materials science ; Modulation (music) ; Nanotechnology ; Characterization (materials science) ; Field (mathematics) ; Optoelectronics ; Electrode ; Chemistry ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Conducting polymers and applications