jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janine Lins | Thomas Harweg | Frank Weichert | Kerstin Wohlgemuth
Source: Applied Sciences (article, 2022-02-26)
Field: Materials Science — Materials Chemistry

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies. Agglomerate ; Crystallization ; Economies of agglomeration ; Characterization (materials science) ; Materials science ; Particle (ecology) ; Particle size ; Process engineering ; Nanotechnology ; Biological system ; Chemical engineering ; Composite material ; Engineering ; Geology Crystallization and Solubility Studies ; Enzyme Structure and Function ; Machine Learning in Materials Science
Authors: Vadim Korolev | Iurii M. Nevolin | Pavel Protsenko
Source: Scientific Reports (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

A universal similarity based approach for predictive uncertainty quantification in materials science Immense effort has been exerted in the materials informatics community towards enhancing the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) of state-of-the-art algorithms also demands further development. Most prominent UQ methods are model-specific or are related to the ensembles of models; therefore, there is a need to develop a universal technique that can be readily applied to a single model from a diverse set of ML algorithms. In this study, we suggest a new UQ measure known as the Δ-metric to address this issue. The presented quantitative criterion was inspired by the k-nearest neighbor approach adopted for applicability domain estimation in chemoinformatics. It surpasses several UQ methods in accurately ranking the predictive errors and could be considered a low-cost option for a more advanced deep ensemble strategy. We also evaluated the performance of the presented UQ measure on various classes of materials, ML algorithms, and types of input features, thus demonstrating its universality. Computer science ; Machine learning ; Metric (unit) ; Artificial intelligence ; Uncertainty quantification ; Universality (dynamical systems) ; Data mining ; Ranking (information retrieval) ; Measure (data warehouse) ; Cheminformatics ; Ensemble learning ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zeyu Liu | Meng Jiang | Tengfei Luo
Source: Materials Today Physics (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Leveraging low-fidelity data to improve machine learning of sparse high-fidelity thermal conductivity data via transfer learning Materials science ; Leverage (statistics) ; Fidelity ; High fidelity ; Materials informatics ; Computer science ; Transfer of learning ; Machine learning ; Experimental data ; Ranging ; Microelectronics ; Artificial intelligence ; Optoelectronics ; Health informatics ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Chika Maduabuchi | Hassan Fagehi | Ibrahim Alatawi | Mohammad Alkhedher
Source: Energies (article, 2022-08-19)
Field: Materials Science — Materials Chemistry

Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural network predicts the modified device performance about 800× faster than the conventional finite element method. Overall, the paper provides insights into how thermoelectric manufacturing companies can harness the power of artificial intelligence to design very high-performing devices while saving time and cost. Finite element method ; Artificial neural network ; Thermoelectric generator ; Computer science ; Generator (circuit theory) ; Power (physics) ; Software ; Thermoelectric effect ; Electronic engineering ; Engineering ; Mechanical engineering ; Artificial intelligence ; Structural engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Building Energy and Comfort Optimization
Authors: Prakash Parajuli | Sriparna Bhattacharya | Rahul Rao | Apparao M. Rao
Source: Materials Horizons (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Phonon anharmonicity in binary chalcogenides for efficient energy harvesting Thermoelectric (TE) materials have received much attention due to their ability to harvest waste heat energy. TE materials must exhibit a low thermal conductivity (<i>κ</i>) and a high power factor (PF) for efficient conversion. Both factors define the figure of merit (<i>ZT</i>) of the TE material, which can be increased by suppressing <i>κ</i> without degrading the PF. Recently, binary chalcogenides such as SnSe, GeTe, and PbTe have emerged as attractive candidates for thermoelectric energy generation at moderately high temperatures. These materials possess simple crystal structures with low <i>κ</i> in their pristine forms, which can be further lowered through doping and other approaches. Here, we review the recent advances in the temperature-dependent behavior of phonons and their influence on the thermal transport properties of chalcogenide-based TE materials. Because phonon anharmonicity is one of the fundamental contributing factors for low thermal conductivity in SnSe, Sb-doped GeTe, and related chalcogenides, we discuss complementary experimental approaches such as temperature-dependent Raman spectroscopy, inelastic neutron scattering, and calorimetry to measure anharmonicity. We further show how data gathered using multiple techniques helps us understand and engineer better TE materials. Finally, we discuss the rise of machine learning-aided efforts to discover, design, and synthesize TE materials of the future. Anharmonicity ; Thermoelectric materials ; Materials science ; Phonon ; Raman spectroscopy ; Thermal conductivity ; Chalcogenide ; Thermoelectric effect ; Figure of merit ; Condensed matter physics ; Inelastic neutron scattering ; Nanotechnology ; Optoelectronics ; Neutron scattering ; Scattering ; Thermodynamics ; Optics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Zhixu Li | Yiming Ma | Jiawei Lin | Zhenjie Gao | Songgu Wu | Wei Li | Dandan Han | Junbo Gong | Jingkang Wang
Source: Journal of Crystal Growth (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

The polymorph and crystal habit control of dl-methionine assisted by ultrasound Nucleation ; Crystal habit ; Crystal (programming language) ; Crystallization ; Supersaturation ; Metastability ; Materials science ; Ultrasound ; Crystal growth ; Yield (engineering) ; Crystallography ; Chemistry ; Computer science ; Physics ; Organic chemistry ; Composite material ; Acoustics Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Chemical and Physical Properties in Aqueous Solutions
Authors: Rachid Ghritli | Yasunori Okano | Yuko Inatomi | S. Dost
Source: Japanese Journal of Applied Physics (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning Abstract The growth of high-quality InGaSb crystals by Vertical Gradient Freezing (VGF) under microgravity was numerically simulated. Machine learning tools were used to optimize the growth conditions. The study focuses on controlling growth interface shape which directly affects the quality and homogeneity of the grown crystals. Initially, Bayesian optimization was utilized to search for the most favorable growth conditions that promote a desirable flatter growth interface shape. Afterward, a reinforcement learning model was developed. The system was subjected to a lower temperature gradient near the feed crystal and to crucible rotation with a rate ranging according to the obtained optimal strategy. Results showed that the interface deformation is considerably reduced, and a flatter growth interface could be maintained. The growth rate and solute concentration uniformity were also improved. This adaptive control recipe proves to hold great potential in the continuous and rapid optimization of other crystal growth processes. Crystal growth ; Homogeneity (statistics) ; Materials science ; Bayesian optimization ; Growth rate ; Interface (matter) ; Crucible (geodemography) ; Temperature gradient ; Crystal (programming language) ; Rotation (mathematics) ; Computer science ; Biological system ; Mechanics ; Composite material ; Chemistry ; Mathematics ; Crystallography ; Artificial intelligence ; Geometry ; Physics Solidification and crystal growth phenomena ; Heat Transfer and Optimization ; Advanced Semiconductor Detectors and Materials
Authors: Renhai Wang | Weiyi Xia | Tyler J. Slade | Xinyu Fan | Huafeng Dong | Kai‐Ming Ho | P. C. Canfield | Cai‐Zhuang Wang
Source: npj Computational Materials (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements Abstract Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery. Exploring what third element and its ratio that can be added to make stable ternary compounds out of an immiscible pair of elements remains a great challenge. In this work, we combine a machine learning (ML) method with ab initio calculations to efficiently search for the energetically favorable ternary La-Co-Pb compounds containing immiscible elements Co and Pb. Three previously reported structures are correctly captured by our approach. Moreover, we predict a ground state La 3 CoPb compound and 57 low-energy La-Co-Pb ternary compounds. Attempts to synthesize La 3 CoPb via multiple techniques produce mixed or multi-phases samples with, at best, ambiguous signals of the predicted lowest-energy La 3 CoPb and the second lowest-energy La 18 Co 28 Pb 3 phases. The calculated results of Gibbs free energy are consistent with experiments, and will provide very useful guidance for further experimental synthesis. Ternary operation ; Chemistry ; Ab initio ; Ab initio quantum chemistry methods ; Gibbs free energy ; Quantum chemical ; Work (physics) ; Thermodynamics ; Molecule ; Computational chemistry ; Chemical physics ; Physical chemistry ; Computer science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Ryan Snyder | Bryant Kim | Xiaoliang Pan | Yihan Shao | Jingzhi Pu
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Facilitating <i>ab initio</i> QM/MM free energy simulations by Gaussian process regression with derivative observations In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of <i>ab initio</i> (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase S<sub>N</sub>2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol<sup>-1</sup> for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol<sup>-1</sup> Å<sup>-1</sup>. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol<sup>-1</sup> and a reaction free energy of -34.1 kcal mol<sup>-1</sup>, in closer agreement with the AI/MM benchmarks and experimental results. Kriging ; Ab initio ; Gaussian process ; Gaussian ; QM/MM ; Energy (signal processing) ; Regression ; Computational chemistry ; Ab initio quantum chemistry methods ; Derivative (finance) ; Process (computing) ; Statistical physics ; Chemistry ; Physics ; Molecular dynamics ; Computer science ; Mathematics ; Quantum mechanics ; Machine learning ; Molecule ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Shivesh Pathak | I. Ema | Alex J. Lee | William P. Bricker | R. López | Susi Lehtola | Joshua A. Rackers
Source: The Journal of Chemical Physics (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

Accurate Hellmann–Feynman forces from density functional calculations with augmented Gaussian basis sets The Hellmann-Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learning (ML) models for the electron density. The main issue holding back the general acceptance of the HF approach for atom-centered basis sets is the well-known Pulay force which, if naively discarded, typically constitutes an error upward of 10 eV/Å in forces. In this work, we demonstrate that if a suitably augmented Gaussian basis set is used for density functional calculations, the Pulay force can be suppressed, and HF forces can be computed as accurately as analytical forces with state-of-the-art basis sets, allowing geometry optimization and molecular dynamics to be reliably performed with HF forces. Our results pave a clear path forward for the accurate and efficient simulation of large systems using ML densities and the HF theorem. Basis (linear algebra) ; Gaussian ; Statistical physics ; Feynman diagram ; Basis set ; Physics ; Set (abstract data type) ; Density functional theory ; Work (physics) ; Classical mechanics ; Atom (system on chip) ; Quantum mechanics ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Valentin C. Menzel | Xuerui Yi | Franziska Bößl | Caroline Kirk | Neil Robertson | Ignacio Tudela
Source: Additive manufacturing (article, 2022-02-23)
Field: Materials Science — Polymers and Plastics

Additive manufacturing of polyaniline electrodes for electrochemical applications Polyaniline (PANI), particularly in its emeraldine salt form (PANI-ES), possesses several desirable properties such as high tuneable conductivity, pseudocapacitance and chemical stability, which make PANI a promising material for a wide range of electrochemical applications. However, its use is severely limited by its poor processability due to its non-thermoformable nature. In this work, a novel additive manufacturing method to process PANI is introduced, which enables the fabrication of electrodes with complex geometries exhibiting conductivities of up to 20000 mS/cm. This novel additive manufacturing process is based on the thermal doping of PANI in its low-conductive emeraldine base form (PANI-EB) with dodecyl benzene sulfonic acid (DBSA). For this purpose, a commercially available 3D printer was modified with a custom-made syringe extrusion system to enable the extrusion of a viscous PANI-EB/DBSA paste. The influence of the additive manufacturing process parameters on the conductivity of printed electrodes was evaluated, and the resulting printed materials were characterised by Fourier-transform infrared spectroscopy – attenuated total reflection, X-ray powder diffraction, thermo-gravimetric analysis and scanning electron microscopy. The feasibility of the 3D printed PANI-ES/DBSA electrodes was studied by conducting a proof-of-concept electrochemical study of 3D printed interdigitated symmetric electrochemical capacitors, demonstrating how the printed structures could be easily integrated in prototypes of electrochemical devices. Although the fabricated devices did not exhibit superior capacitance, they presented a memristive behaviour with a potential for other applications such as artificial neural networks. Moreover, the results of this proof-of-concept study illustrate the significant importance of the conductivity of the printed electrodes, hence the importance of the additive manufacturing process parameters on the electrochemical performance. Materials science ; Polyaniline ; Conductive polymer ; Electrode ; Sulfonic acid ; Fourier transform infrared spectroscopy ; Supercapacitor ; Chemical engineering ; Electrochemistry ; Nanotechnology ; Composite material ; Polymer chemistry ; Polymer ; Polymerization Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Chun‐Yan Qin | Zhilian Yue | Xu‐Feng Huang | Robert J. Forster | Gordon G. Wallace | Jun Chen
Source: Applied Materials Today (article, 2022-04-22)
Field: Materials Science — Polymers and Plastics

Enhanced wireless cell stimulation using soft and improved bipolar electroactive conducting polymer templates PEDOT:PSS ; Conductive polymer ; Polypyrrole ; Materials science ; Neurite ; Polymer ; Dopant ; Nanotechnology ; Combinatorial chemistry ; Chemistry ; Computer science ; In vitro ; Biomedical engineering ; Doping ; Optoelectronics ; Biochemistry ; Composite material ; Medicine Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Bart Klumpers | Emiel J. M. Hensen | Ivo A. W. Filot
Source: The Journal of Physical Chemistry C (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Lateral Interactions of Dynamic Adlayer Structures from Artificial Neural Networks Lateral interactions are a key factor in the correct description of adsorption isotherms relevant to heterogeneous catalytic reactions. To model these lateral interactions, a large number of monolayer structures have to be investigated, far exceeding the limitations of conventional techniques such as density functional theory. We have developed a new hybrid neural network model that can substitute the electronic structure calculations for these monolayer structures, without significant loss of accuracy. The low computational cost of this model allows the study of the adlayer structures close to industrial operating conditions. Lateral interactions are found to increase at elevated temperatures as a result of increased adsorbate mobility, and this contribution is found to be key in unifying theoretical and experimental observations. We show that the inclusion of dispersion interactions in stabilizing the adlayers is necessary to obtain correct predictions for both isotherms and adsorption site distributions. Monolayer ; Adsorption ; Density functional theory ; Chemical physics ; Key (lock) ; Dispersion (optics) ; Materials science ; Artificial neural network ; Biological system ; Statistical physics ; Nanotechnology ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Physical chemistry ; Artificial intelligence ; Optics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Theoretical and Computational Physics
Authors: Siyu Liu | Jing Wang | Zhongtao Duan | Kongxiang Wang | Wanlu Zhang | Ruiqian Guo | Fengxian Xie
Source: ACS Applied Materials & Interfaces (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites Symbolic classification is an approach of interpretable machine learning for building mathematical formulas that fit certain data sets. In this work, symbolic classification is used to establish the relationship between oxygen vacancy defect formation energy and structural features. We find a structural descriptor <i>n</i><sub><i>a</i></sub>(<i>r</i><sub>a</sub>/<i>E</i><sub>na</sub> - <i>r</i><sub><i>b</i></sub>), where <i>n</i><sub>a</sub> is the valence of the a-site ion, <i>r</i><sub>a</sub> is the radius of the a-site ion, <i>E</i><sub>na</sub> is the electronegativity of the a-site ion, and <i>r</i><sub>b</sub> is the radius of the b-site ion. It accelerates the screening of defect-free oxide perovskites in advance of density functional theory (DFT) calculations and experimental characterization. Our results demonstrate the potential of symbolic classification for accelerating the data-driven design and discovery of materials with improved properties. Materials science ; Simple (philosophy) ; Vacancy defect ; Oxygen ; Statistical physics ; Chemical physics ; Condensed matter physics ; Computational chemistry ; Physics ; Quantum mechanics Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors