jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nada Alfryyan | Muhammad Saqib | Saman Ali | Tayyaba Mubashir | Mudassir Hussain Tahir | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Materials Today Communications (article, 2023-06-28)
Field: Materials Science — Polymers and Plastics

Designing of near-IR organic semiconductors for photodetectors: Machine learning and data mining assisted efficient pipeline Photodetector ; Computer science ; Random forest ; Machine learning ; Artificial intelligence ; Materials science ; Pipeline (software) ; Outlier ; Organic semiconductor ; Visualization ; Data mining ; Materials informatics ; Optoelectronics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Ruihan Wang | Yeshuang Zhong | Xuehua Dong | Meng Du | Haolun Yuan | Yurong Zou | Xin Wang | Zhien Lin | Dingguo Xu
Source: Inorganic Chemistry (article, 2023-03-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design due to the issues of computational accuracy and cost of first-principles methods. Although machine learning (ML) techniques have shown great successes in predicting the versatile properties of materials, their practicality is often limited by the data set quality. Here, by using a combination of natural language processing searches and domain knowledge, we built an experimental database of inorganic borates, including their chemical compositions, band gaps, and crystal structures. We performed graph network deep learning to predict the band gaps of borates with accuracy, and the results agreed favorably with experimental measurements from the visible-light to the deep-ultraviolet (DUV) region. For a realistic screening problem, our ML model could correctly identify most of the investigated DUV borates. Furthermore, the extrapolative ability of the model was validated against our newly synthesized borate crystal Ag<sub>3</sub>B<sub>6</sub>O<sub>10</sub>NO<sub>3</sub>, supplemented by the discussion of an ML-based material design for structural analogues. The applications and interpretability of the ML model were also evaluated extensively. Finally, we implemented a web-based application, which could be utilized conveniently in material engineering for the desired band gap. The philosophy behind this study is to use cost-effective data mining techniques to build high-quality ML models, which can provide useful clues for further material design. Interpretability ; Band gap ; Boron ; Artificial intelligence ; Deep learning ; Machine learning ; Crystal structure prediction ; Computer science ; Graph ; Chemistry ; Data mining ; Crystal structure ; Materials science ; Theoretical computer science ; Optoelectronics Crystal Structures and Properties ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Marius Herbold | Jörg Behler
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning transferable atomic forces for large systems from underconverged molecular fragments Machine learning potentials (MLP) enable atomistic simulations with first-principles accuracy at a small fraction of the costs of electronic structure calculations. Most modern MLPs rely on constructing the potential energy, or a major part of it, as a sum of atomic energies, which are given as a function of the local chemical environments up to a cutoff radius. Since analytic forces are readily available, nowadays it is common practice to make use of both, reference energies and forces, for training these MLPs. This can be computationally demanding since often large systems are required to obtain structurally converged reference forces experienced by atoms in realistic condensed phase environments. In this work we show how density-functional theory calculations of molecular fragments, which are too small to provide such structurally converged forces, can be used to learn forces exhibiting excellent transferability to extended systems. The general procedure and the accuracy of the method are illustrated for metal-organic frameworks using second-generation high-dimensional neural network potentials. Construct (python library) ; Artificial neural network ; Chemical physics ; Molecular recognition ; Chemistry ; Crystal (programming language) ; Nanotechnology ; Computer science ; Molecule ; Biological system ; Materials science ; Artificial intelligence ; Biology ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yasaman J. Soofi | Yijia Gu | Jinling Liu
Source: Computational Materials Science (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery Encoding (memory) ; Categorical variable ; Feature (linguistics) ; Feature engineering ; Computer science ; Variable (mathematics) ; Artificial intelligence ; Machine learning ; Mathematics ; Deep learning Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Arno Annys | Daen Jannis | Johan Verbeeck
Source: Scientific Reports (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping. Electron energy loss spectroscopy ; Convolutional neural network ; Artificial neural network ; Computer science ; Multilayer perceptron ; Artificial intelligence ; Spectroscopy ; Pattern recognition (psychology) ; Elemental analysis ; Transformer ; Biological system ; Materials science ; Analytical Chemistry (journal) ; Physics ; Chemistry ; Nanotechnology ; Transmission electron microscopy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Kun Jin | Wentao Wang | Guangpei Qi | Xiaohong Peng | Haonan Gao | Hongjiang Zhu | Xin He | Haixia Zou | Lin Yang | Junjie Yuan | Liyuan Zhang | Hong Chen | Xiangmeng Qu
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An explainable machine-learning approach for revealing the complex synthesis path–property relationships of nanomaterials Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 10<sup>4</sup> prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics. Nanomaterials ; Path (computing) ; Property (philosophy) ; Quantum ; Quantum yield ; Nanotechnology ; Yield (engineering) ; Phase diagram ; Materials science ; Computer science ; Fluorescence ; Phase (matter) ; Chemistry ; Physics ; Organic chemistry ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanocluster Synthesis and Applications
Authors: Nuttawat Parse | Supree Pinitsoontorn
Source: APL Materials (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range Machine learning (ML) is increasingly being adopted to accelerate the development of materials research. In this work, we applied the ML approach to predict the figure-of-merit (ZT) of thermoelectric (TE) materials. The experimental datasets were gathered from 150 published articles for five high-performance TE groups in the mid-temperature range, i.e., PbTe, Co4Sb12, Mg2Si, BiCuSeO, and Cu2Se, resulting in 1563 data points in total. The chemical formulas of individual compounds, including the dopant types and concentrations, were extracted as ML features using the Magpie software. The ZT value was set as the target value. The model was built based on different regression algorithms, and its accuracy for predicting ZT was evaluated using the coefficient of determination (R2) and the root mean squared error (RMSE). It was found that the model’s accuracy increased with increasing datasets and by incorporating features from experimental parameters (measurement temperature, sintering temperature, and sintering pressure). The final ML model showed relatively high accuracy, with an R2 of 0.859 and an RMSE of 0.156 for a test set. It means that the model can confidently predict the ZT of specific doped compounds in the selected TE groups. To utilize the model effectively, it is implemented as a webpage application with a user-friendly interface so that researchers without ML expertise can explore the ZT values of the doped TE materials. It will certainly be beneficial for experimentalists as a guideline for designing their experiments. Materials science ; Mean squared error ; Machine learning ; Dopant ; Coefficient of determination ; Thermoelectric effect ; Test set ; Artificial intelligence ; Doping ; Atmospheric temperature range ; Computer science ; Algorithm ; Statistics ; Thermodynamics ; Mathematics ; Optoelectronics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Bilge Coşkuner Filiz | Beyza Nur Kinsiz | Serpil Kılıç Depren | Aysel Kantürk Fıgen
Source: Journal of Cleaner Production (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Regeneration of Co-based bead type catalyst in ammonia borane hydrolysis for hydrogen generation: Artificial neural networks and response surface methodology Ammonia borane ; Catalysis ; Response surface methodology ; Hydrogen production ; Chemical engineering ; Hydrogen ; Hydrolysis ; Materials science ; Chemistry ; Fourier transform infrared spectroscopy ; Cobalt ; Ammonia ; Solvent ; Inorganic chemistry ; Chromatography ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Pauline Böhringer | D. Sommer | Thomas Haase | Martin Barteczko | Joachim Sprave | Markus Stoll | Celalettin Karadoğan | David Koch | Peter Middendorf | Mathias Liewald
Source: Computer Methods in Applied Mechanics and Engineering (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

A strategy to train machine learning material models for finite element simulations on data acquirable from physical experiments Artificial neural network ; Computer science ; Field (mathematics) ; Finite element method ; Process (computing) ; Computation ; Evolutionary algorithm ; Evolutionary computation ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Algorithm ; Engineering ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Structural Health Monitoring Techniques
Authors: Yuuki Ishiwatari | T. Yokoyama | Tomoya Kojima | Taisuke Banno | Noriyoshi Arai
Source: Molecular Systems Design & Engineering (article, 2023-10-20)
Field: Chemistry — Organic Chemistry

Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter We used machine learning to predict the self-assembly structures of amphiphilic molecules and analyzed the physical factors affecting their morphologies. Self-assembly ; Amphiphile ; Molecule ; Materials science ; Chemical physics ; Nanotechnology ; Computer science ; Artificial intelligence ; Biological system ; Chemistry ; Polymer ; Copolymer ; Composite material ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Surfactants and Colloidal Systems
Authors: Alexander A. Ksenofontov | Yaroslav I. Isaev | Michail M. Lukanov | Dmitriy M. Makarov | Varvara A. Eventova | Ilya A. Khodov | Mechail B. Berezin
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accurate prediction of <sup>11</sup>B NMR chemical shift of BODIPYs <i>via</i> machine learning We present the results of developing a new model based on machine learning methods for predicting the 11 B NMR chemical shift of boron-containing dyes. Chemical shift ; Test set ; Chemistry ; Mean squared error ; Carbon-13 NMR ; Proton NMR ; Training set ; Artificial intelligence ; Machine learning ; Computer science ; Physical chemistry ; Mathematics ; Statistics ; Organic chemistry Luminescence and Fluorescent Materials ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Han Liu | Zijie Huang | Samuel S. Schoenholz | Ekin D. Cubuk | Morten M. Smedskjær | Yizhou Sun | Wei Wang | Mathieu Bauchy
Source: Materials Horizons (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "<i>bypass all physics laws</i>" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale. Dynamics (music) ; Molecular dynamics ; Computer science ; Simulation ; Materials science ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Guoxiang Zhao | Weiyin Yan | Zirui Wang | Yao Kang | Zuju Ma | Zhi‐Gang Gu | Qiaohong Li | Jian Zhang
Source: The Journal of Physical Chemistry A (article, 2023-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning In order to determine the polarizability and hyperpolarizability of a molecule, several key parameters need to be known, including the excitation energy of the ground and excited states, the transition dipole moment, and the difference of dipole moment between the ground and excited states. In this study, a machine-learning model was developed and trained to predict the molecular polarizability and second-order hyperpolarizability on a subset of QM9 data set. The density of states was employed as input to the model. The results demonstrated that the machine-learning model effectively estimated both polarizability and the order of magnitude of second-order hyperpolarizability. However, the model was unable to predict the dipole moment and first-order hyperpolarizability, suggesting limitations in its ability to predict the difference of dipole moment between the ground and excited states. The computational efficiency of machine-learning models compared to traditional quantum mechanical calculations enables the possibility of large-scale screening of molecules that satisfy specific requirements using existing databases. This work presents a potential solution for the efficient exploration and analysis of molecules on a larger scale. Hyperpolarizability ; Polarizability ; Dipole ; Excited state ; Moment (physics) ; Chemistry ; Statistical physics ; Molecule ; Atomic physics ; Physics ; Quantum mechanics Nonlinear Optical Materials Research ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jinglong Lin | Fanyang Mo
Source: Artificial Intelligence Chemistry (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Empowering research in chemistry and materials science through intelligent algorithms In this review, we delve into the burgeoning utilization of intelligent algorithms within the realms of chemistry and materials science. Starting with an elucidation of the fundamental tenets of Machine Learning (ML), Deep Learning (DL), and optimization algorithms, we examine their tailored fit to the unique exigencies of chemical materials. We accentuate the integral role of data collection, refinement, and feature engineering, providing methodologies for data extraction from specific databases and literature. A detailed exploration follows, encapsulating the distinct applications of these intelligent algorithms in chemistry and materials science. The review concludes with a forward-looking perspective on the emergent applications of these intelligent paradigms in chemistry, spurring future inquiries. Computer science ; Perspective (graphical) ; Science and engineering ; Feature (linguistics) ; Data science ; Artificial intelligence ; Algorithm ; Chemistry ; Engineering ; Engineering ethics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Daisuke Takagi | Kazuki Ishizaki | Toru Asahi | Takuya Taniguchi
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Molecular screening for solid–solid phase transitions by machine learning The solid–solid phase transition in molecular solids can be predicted by positive-unlabeled learning using molecular descriptors. Molecular solid ; Phase transition ; Phase (matter) ; Solid surface ; Solid solution ; Materials science ; Computer science ; Chemical physics ; Chemistry ; Physics ; Thermodynamics ; Molecule ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shizhuang Weng | Le Tang | Mengqing Qiu | Jinghong Wang | Yehang Wu | Rui Zhu | Cong Wang | Pan Li | Wen Sha | Dong Liang
Source: Analytica Chimica Acta (article, 2023-04-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy charged probes under inverted superhydrophobic platform for detection of agricultural chemicals residues in rice combined with lightweight deep learning network Chemistry ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Chloride ; Analytical Chemistry (journal) ; Spectroscopy ; Nanotechnology ; Raman scattering ; Chromatography ; Optics ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research