jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng Chen | Chong Fang
Source: Chemosensors (article, 2023-01-23)
Field: Chemistry — Physical and Theoretical Chemistry

Fluorescence Modulation by Amines: Mechanistic Insights into Twisted Intramolecular Charge Transfer (TICT) and Beyond Amine groups are common constituents of organic dyes and play important roles in tuning fluorescence properties. In particular, intensive research works have demonstrated the tendency and capabilities of amines in influencing chromophore brightness. Such properties have been explained by multiple mechanisms spanning from twisted intramolecular charge transfer (TICT) to the energy gap law and beyond, which introduce additional nonradiative energy dissipation pathways. In this review, we aim to provide a focused overview of the mechanistic insights mainly for the TICT mechanism, accompanied by a few other less common or influential fluorescence quenching mechanisms in the amine-containing fluorescent molecules. Various aspects of current scientific findings including the rational design and synthesis of organic chromophores, theoretical calculations, steady-state and time-resolved electronic and vibrational spectroscopies are reviewed. These in-depth understandings of how the amine groups with diverse chemical structures at various atomic sites affect excited-state nonradiative decay pathways will facilitate the strategic and targeted development of fluorophores with desired emission properties as versatile chemosensors for broad applications. Intramolecular force ; Chromophore ; Photochemistry ; Quenching (fluorescence) ; Fluorescence ; Chemistry ; Rational design ; Excited state ; Chemical physics ; Förster resonance energy transfer ; Amine gas treating ; Molecule ; Nanotechnology ; Materials science ; Atomic physics ; Organic chemistry ; Physics Photochemistry and Electron Transfer Studies ; Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection
Authors: K.N. Sasidhar | Nima H. Siboni | Jaber Rezaei Mianroodi | Michael Rohwerder | Jörg Neugebauer | Dierk Raabe
Source: Science Advances (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

Enhancing corrosion-resistant alloy design through natural language processing and deep learning We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine learning models for materials datasets is often limited by their inability to incorporate textual data. Manual extraction of numerical parameters from descriptions of alloy processing or experimental methodology inevitably leads to a reduction in information density. To overcome this, we have developed a fully automated natural language processing approach to transform textual data into a form compatible for feeding into a deep neural network. This approach has resulted in a pitting potential prediction accuracy substantially beyond state of the art. Second, we have implemented a deep learning model with a transformed-input feature space, consisting of a set of elemental physical/chemical property-based numerical descriptors of alloys replacing alloy compositions. This helped identification of those descriptors that are most critical toward enhancing their pitting potential. In particular, configurational entropy, atomic packing efficiency, local electronegativity differences, and atomic radii differences proved to be the most critical. Electronegativity ; Artificial intelligence ; Computer science ; Deep learning ; Alloy ; Artificial neural network ; Corrosion ; Machine learning ; Natural language processing ; Materials science ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Ivan Malashin | В С Тынченко | Vladimir A. Nelyub | А. С. Бородулин | Andrei Gantimurov
Source: Polymers (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Estimation and Prediction of the Polymers’ Physical Characteristics Using the Machine Learning Models This article investigates the utility of machine learning (ML) methods for predicting and analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of polymers' characteristics, the study encompasses an extensive range of polymer properties, spanning compressive and tensile strength to thermal and electrical behaviors. Using various regression methods like Ensemble, Tree-based, Regularization, and Distance-based, the research undergoes thorough evaluation using the most common quality metrics. As a result of a series of experimental studies on the selection of effective model parameters, those that provide a high-quality solution to the stated problem were found. The best results were achieved by Random Forest with the highest R2 scores of 0.71, 0.73, and 0.88 for glass transition, thermal decomposition, and melting temperatures, respectively. The outcomes are intricately compared, providing valuable insights into the efficiency of distinct ML approaches in predicting polymer properties. Unknown values for each characteristic were predicted, and a method validation was performed by training on the predicted values, comparing the results with the specified variance values of each characteristic. The research not only advances our comprehension of polymer physics but also contributes to informed model selection and optimization for materials science applications. Predictive modelling ; Machine learning ; Artificial intelligence ; Computer science ; Polymer ; Econometrics ; Materials science ; Mathematics ; Composite material Machine Learning in Materials Science ; Injection Molding Process and Properties ; Polymer crystallization and properties
Authors: Haoran Li | Diego Serrano | Shukai Wang | Minjie Chen
Source: IEEE Transactions on Power Electronics (article, 2023-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation This paper presents the MagNet-AI platform as an online platform to demonstrate the “Neural Network as Datasheet” concept for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc-bias. LSTM and Transformer based neural network models are developed, verified, and compared. The neural network can be used to rapidly predict hysteresis loops and core losses under different operating conditions, compare materials, and recommend materials for design. The neural network model is also proved effective in reconstructing the raw measurement while accurately maintaining the magnetic characteristics, enabling rapid material evaluation and comparison. Artificial neural network ; Datasheet ; Notation ; Algorithm ; Artificial intelligence ; Computer science ; Mathematics ; Electrical engineering ; Engineering ; Arithmetic Magnetic Properties and Applications ; Magnetic properties of thin films ; Magneto-Optical Properties and Applications
Authors: Yuuta Aoki | Stephen Wu | Teruki Tsurimoto | Yoshihiro Hayashi | Shunya Minami | Okubo Tadamichi | Kazuya Shiratori | Ryo Yoshida
Source: Macromolecules (article, 2023-07-10)
Field: Materials Science — Materials Chemistry

Multitask Machine Learning to Predict Polymer–Solvent Miscibility Using Flory–Huggins Interaction Parameters Predicting and understanding the phase equilibria or phase separation in polymer–solvent solutions represent unresolved fundamental problems in polymer science. The phase behavior and thermodynamics of polymer miscibility depend on the inter- and intramolecular interactions of a polymer with a certain molecular weight distribution mixed with a solvent. Here, we develop a machine-learning framework to achieve highly generalized and robust prediction of Flory–Huggins χ parameters for polymer–solvent solutions. The model was trained using experimentally observed temperature-dependent χ parameters for 1190 samples, comprising 46 unique polymers and 140 solvent species. However, the difficulty was that the data set was quantitatively limited and qualitatively biased owing to technical issues in determining the Flory–Huggins χ parameters. To overcome these limitations, we produced an in-house data set of χ parameters obtained from quantum chemical calculations for thousands of polymer–solvent pairs and a large list of soluble and insoluble polymer–solvent pairs. Using these three data sets, we conducted multitask machine learning that simultaneously performed the “soluble/insoluble” classification and quantitative evaluation of both experimental and calculated χ parameters. Consequently, we obtained a highly generalized model applicable to a wide range of polymer solution spaces. In this paper, the predictive power and physicochemical implications of the model are demonstrated, along with quantitative comparisons with existing methods. Miscibility ; Polymer ; Solvent ; Flory–Huggins solution theory ; Thermodynamics ; Lower critical solution temperature ; Phase (matter) ; Materials science ; Chemistry ; Organic chemistry ; Copolymer ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Polymer crystallization and properties
Authors: Hyunwook Jung | Lena Sauerland | Sina Stocker | Karsten Reuter | Johannes T. Margraf
Source: npj Computational Materials (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Machine-learning driven global optimization of surface adsorbate geometries Abstract The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces. Surface (topology) ; Global optimization ; Adsorption ; Computer science ; Potential energy surface ; Stability (learning theory) ; Set (abstract data type) ; Catalysis ; Chemistry ; Materials science ; Molecule ; Machine learning ; Mathematics ; Algorithm ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Topic Modeling
Authors: Isaiah O. Betinol | Junshan Lai | Saumya Thakur | Jolene P. Reid
Source: Journal of the American Chemical Society (article, 2023-06-02)
Field: Chemistry — Inorganic Chemistry

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis. Generality ; Workflow ; Catalysis ; Chemistry ; Identification (biology) ; Chemical space ; Computer science ; Biochemical engineering ; Organic chemistry ; Database Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Juan V. Alegre‐Requena | Shree Sowndarya S. V. | Raúl Pérez‐Soto | Turki M. Alturaifi | Robert S. Paton
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2023-02-26)
Field: Materials Science — Materials Chemistry

AQME: Automated quantum mechanical environments for researchers and educators Abstract AQME, automated quantum mechanical environments, is a free and open‐source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi‐empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble‐averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre‐configured workflows are available in various formats, and hands‐on video tutorials illustrate their use. This article is categorized under: Data Science &gt; Chemoinformatics Data Science &gt; Computer Algorithms and Programming Software &gt; Quantum Chemistry Computer science ; Workflow ; Python (programming language) ; Computational science ; JavaScript ; Cheminformatics ; Modular design ; Programming language ; Bioinformatics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: J. Zhao | Jesper Byggmästar | Huan He | K. Nordlund | Flyura Djurabekova | Mengyuan Hua
Source: npj Computational Materials (article, 2023-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials Abstract Ga 2 O 3 is a wide-band gap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting Ga 2 O 3 polymorphs and low-symmetry disordered structures is missing. We develop two types of machine-learning Gaussian approximation potentials (ML-GAPs) for Ga 2 O 3 with high accuracy for β / κ / α / δ / γ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for high accuracy and exceeding speedup, respectively. Both potentials can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with 5 × 10 2 and 2 × 10 5 computing speed increases compared to density functional theory, respectively. Moreover, the Ga 2 O 3 liquid-solid phase transition proceeds in three different stages. This experimentally unrevealed complex dynamics can be understood in terms of distinctly different mobilities of O and Ga sublattices in the interfacial layer. Stoichiometry ; Ab initio ; Materials science ; Density functional theory ; Molecular dynamics ; Band gap ; Chemical physics ; Statistical physics ; Physics ; Crystallography ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Quantum mechanics Ga2O3 and related materials ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Zheng Yao | Carlos E. Romero | Jonas Baltrušaitis
Source: Fuel (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks NOx ; Boiler (water heating) ; Selective catalytic reduction ; Combustion ; Process engineering ; Artificial neural network ; Environmental science ; Power station ; Coal ; Pollutant ; Automotive engineering ; Waste management ; Computer science ; Engineering ; Chemistry ; Catalysis ; Electrical engineering Catalytic Processes in Materials Science ; Air Quality Monitoring and Forecasting ; Radiative Heat Transfer Studies
Authors: Shu-Wen Li | Li‐Cheng Xu | Cheng Zhang | Shuo‐Qing Zhang | Xin Hong
Source: Nature Communications (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose. Interpretability ; Steric effects ; Computer science ; Artificial intelligence ; Graph ; Synthetic data ; Embedding ; Machine learning ; Function (biology) ; Biochemical engineering ; Chemistry ; Theoretical computer science ; Stereochemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Yüjie Guo | Zhenyu Xu | Alberto G. Curto | Y. J. Zeng | Dries Van Thourhout
Source: Progress in Materials Science (article, 2023-06-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic semiconductors: materials, tunability and applications Plasmon ; Semiconductor ; Materials science ; Nanotechnology ; Photonics ; Heterojunction ; Optoelectronics ; Metamaterial ; Engineering physics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications