jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jie Feng | Hongshuai Wang | Yujin Ji | Youyong Li
Source: Nano Select (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning Abstract Over past two decades, organic photovoltaics (OPVs) with unique advantages of low cost and flexibility meet significant development opportunities and the official world record for the power conversion efficiency (PCE) of organic solar cells (OSCs) has reached to 17.3%. Traditionally, efficiency breakthrough need the constant input of intensive labor and time. The artificial intelligence, as a rising interdisciplinary, brings certainly a revolution in research methods. In this review, we introduce a state‐of‐art theoretical methodology of the synergy of high‐throughput screening and machine learning (ML) in accelerating the discovery of high‐efficient OSC materials. We present key details, rules and experience in database construction, selection of molecular features, fast‐screening calculations, models training and their predication capabilities. Meanwhile, three typical ML frameworks are concluded to reveal the structure‐property‐efficiency relationship, suggesting that this theoretical methodology can train powerful models with just molecular configurations and theoretical calculations for molecular design and efficiency improvements. Flexibility (engineering) ; Organic solar cell ; Throughput ; Computer science ; Energy conversion efficiency ; Artificial intelligence ; High-throughput screening ; Biochemical engineering ; Nanotechnology ; Process engineering ; Photovoltaic system ; Engineering ; Materials science ; Chemistry ; Electrical engineering ; Telecommunications Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Jinxu He | Jianhua Zhou | Jianpei Dong | Zhenning Su | Lü Huang
Source: Chemical Engineering Journal (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Revealing the effects of microwell sizes on the crystal growth kinetics of active pharmaceutical ingredients by deep learning Crystallization ; Materials science ; Crystal (programming language) ; Crystal growth ; Active ingredient ; Kinetics ; Nanotechnology ; Computer science ; Chemical engineering ; Crystallography ; Chemistry ; Bioinformatics ; Engineering ; Physics Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Drug Solubulity and Delivery Systems
Authors: B. Vigneshwaran | R. V. Maheswari | L. Kalaivani | Vimal Shanmuganathan | Seungmin Rho | Seifedine Kadry | Mi Young Lee
Source: Energy Reports (article, 2021-01-10)
Field: Materials Science — Materials Chemistry

Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator with three straight shed ball end fitting in a laboratory setup and corresponding PD readings are measured. The wavelet transform is employed to represent the measured PD signal as scalogram patterns. In general CNN uses a single input pattern for feature extraction. If the pattern quality is low, it is easy to cause misclassification. Hence in this proposed work, the feature fusion of a dual-input Visual Geometry Group (VGG) based CNN is used for the classification of contamination layer. VGG 19 is a pretrained deep learning network used for extracting the rich features from the patterns. In continuation to that, hyperparameter (HP) play a vital role in deep learning algorithms because they directly manage the behaviours of training algorithms and have a significant effect on the performance of deep learning models. Hence, Bayesian Optimization (BO) is used for tuning the HP. At last, to check the practicality of the proposed algorithm, a new dataset is created for 11 kV polymer insulator with three alternate shed clevis end fitting and different pollution levels—acceptable results obtained by using dual-input CNN with the minimum quantity of data. Computer science ; Hyperparameter ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Deep learning ; Hyperparameter optimization ; Feature extraction ; Grid ; Insulator (electricity) ; Artificial neural network ; Algorithm ; Support vector machine ; Mathematics ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Image and Signal Denoising Methods
Authors: Abhik Chakraborty | Raksha Sharma
Source: The Visual Computer (article, 2021-06-24)
Field: Materials Science — Materials Chemistry

A deep crystal structure identification system for X-ray diffraction patterns Convolutional neural network ; Artificial intelligence ; Diffraction ; Computer science ; Crystal (programming language) ; Artificial neural network ; Crystal structure ; Machine learning ; Algorithm ; Deep learning ; Crystallography ; Pattern recognition (psychology) ; Physics ; Optics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: Amit Gupta | Sabyasachi Chakraborty | Debashree Ghosh | Raghunathan Ramakrishnan
Source: PubMed (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Data-driven modeling of S<sub>0</sub> → S<sub>1</sub> excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design. Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by ±1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of ±0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of-even a few dozen-substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77 412 molecules and train a kernel-based quantum machine learning model providing <2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 × 10<sup>9</sup>) molecules is demonstrated by inverse-designing candidates with desired target excitation energies. BODIPY ; Excitation ; Computer science ; Chemical space ; Density functional theory ; Throughput ; Algorithm ; Computational science ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Fluorescence ; Telecommunications Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Sara Szymkuć | Tomasz Badowski | Bartosz A. Grzybowski
Source: Angewandte Chemie International Edition (article, 2021-09-24)
Field: Materials Science — Materials Chemistry

Is Organic Chemistry Really Growing Exponentially? In terms of molecules and specific reaction examples, organic chemistry features an impressive, exponential growth. However, new reaction classes/types that fuel this growth are being discovered at a much slower and only linear (or even sublinear) rate. The proportion of newly discovered reaction types to all reactions being performed keeps decreasing, suggesting that synthetic chemistry becomes more reliant on reusing the well-known methods. The newly discovered chemistries are more complex than decades ago and allow for the rapid construction of complex scaffolds in fewer numbers of steps. We study these and other trends in the function of time, reaction-type popularity and complexity based on the algorithm that extracts generalized reaction class templates. These analyses are useful in the context of computer-assisted synthesis, machine learning (to estimate the numbers of models with sufficient reaction statistics), and identifying erroneous entries in reaction databases. Sublinear function ; Exponential growth ; Context (archaeology) ; Popularity ; Exponential function ; Chemistry ; Function (biology) ; Computer science ; Mathematics ; Combinatorics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: David Merayo | Álvaro Rodríguez-Prieto | Ana María Camacho
Source: Metals (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements. Metalworking ; Artificial neural network ; Computer science ; Strain hardening exponent ; Artificial intelligence ; Perceptron ; Ultimate tensile strength ; Materials science ; Machine learning ; Characterization (materials science) ; Mechanical engineering ; Topology (electrical circuits) ; Engineering ; Composite material ; Nanotechnology Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Franklin L. Lee | Jaehong Park | Sushmit Goyal | Yousef Qaroush | Shihu Wang | Hong Jin Yoon | Aravind Rammohan | Youngseon Shim
Source: Polymers (article, 2021-10-23)
Field: Materials Science — Materials Chemistry

Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have a large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure-property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (Tg), melting temperature (Tm), density (ρ), and tensile modulus (E). The non-linear model using random forest is in general found to be more accurate than linear regression; however, using feature selection or regularization, the accuracy of linear models is shown to be improved significantly to become comparable to the more complex nonlinear algorithm. We find that none of the models or fingerprints were able to accurately predict the tensile modulus E, which we hypothesize is due to heterogeneity in data and data sources, as well as inherent challenges in measuring it. Finally, QSPR models revealed that the fraction of rotatable bonds, and the rotational degree of freedom affects polyamide properties most profoundly and can be used for back of the envelope calculations for a quick estimate of the polymer attributes (glass transition temperature, melting temperature, and density). These QSPR models, although having slightly lower prediction accuracy, show the most promise for the polymer chemist seeking to develop an intuition of ways to modify the chemistry to enhance specific attributes. Quantitative structure–activity relationship ; Glass transition ; Linear regression ; Computer science ; Artificial intelligence ; Polyamide ; Intuition ; Machine learning ; Biological system ; Polymer ; Materials science ; Thermodynamics ; Composite material ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Zhuoran Qiao | Anders S. Christensen | Frederick R. Manby | Matthew Welborn | Anima Anandkumar | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2021-05-31)
Field: Materials Science — Materials Chemistry

UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry. Equivariant neural networks have been successful in incorporating various types of symmetries, but are mostly limited to vector representations of geometric objects. Despite the prevalence of higher-order tensors in various application domains, e.g. in quantum chemistry, equivariant neural networks for general tensors remain unexplored. Previous strategies for learning equivariant functions on tensors mostly rely on expensive tensor factorization which is not scalable when the dimensionality of the problem becomes large. In this work, we propose unitary $N$-body tensor equivariant neural network (UNiTE), an architecture for a general class of symmetric tensors called $N$-body tensors. The proposed neural network is equivariant with respect to the actions of a unitary group, such as the group of 3D rotations. Furthermore, it has a linear time complexity with respect to the number of non-zero elements in the tensor. We also introduce a normalization method, viz., Equivariant Normalization, to improve generalization of the neural network while preserving symmetry. When applied to quantum chemistry, UNiTE outperforms all state-of-the-art machine learning methods of that domain with over 110% average improvements on multiple benchmarks. Finally, we show that UNiTE achieves a robust zero-shot generalization performance on diverse down stream chemistry tasks, while being three orders of magnitude faster than conventional numerical methods with competitive accuracy. Equivariant map ; Unitary state ; Normalization (sociology) ; Curse of dimensionality ; Tensor (intrinsic definition) ; Artificial neural network ; Factorization ; Generalization ; Homogeneous space ; Computer science ; Mathematics ; Algebra over a field ; Pure mathematics ; Artificial intelligence ; Algorithm ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Topic Modeling ; Computational Physics and Python Applications
Authors: Kōji Shimizu | Elvis F. Arguelles | Wenwen Li | Yasunobu Ando | Emi Minamitani | Satoshi Watanabe
Source: Physical review. B./Physical review. B (article, 2021-03-22)
Field: Materials Science — General Materials Science

Phase stability of Au-Li binary systems studied using neural network potential The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we constructed a neural network potential (NNP) of Au-Li binary systems based on density functional theory (DFT) calculations. To accelerate construction of NNPs, we proposed an efficient and inexpensive method of structural dataset generation. The predictions by the constructed NNP on lattice parameters and phonon properties agree well with those obtained by DFT calculations. We also investigated the mixing energy of ${\mathrm{Au}}_{1\ensuremath{-}x}{\mathrm{Li}}_{x}$ with fine composition grids, showing excellent agreement with DFT verifications. We found the existence of various compositions with structures on and slightly above the convex hull, which can explain the lack of consensus on the Au-Li stable phases in previous studies. Moreover, we found other stable phases, namely ${\mathrm{Au}}_{0.45}{\mathrm{Li}}_{0.55}, {\mathrm{Au}}_{0.389}{\mathrm{Li}}_{0.611}$, and ${\mathrm{Au}}_{0.357}{\mathrm{Li}}_{0.643}$. Finally, we examined the alloying process starting from the phase separated structure to the complete mixing phase. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. This paper demonstrates the applicability of NNPs toward miscible phases, and it provides an understanding of the alloying mechanism. Artificial neural network ; Stability (learning theory) ; Binary number ; Materials science ; Computer science ; Mathematics ; Artificial intelligence ; Machine learning ; Arithmetic Metallurgical and Alloy Processes ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: N. A. Gracheva | M V Lekanov | Alexander E. Mayer | E. V. Fomin
Source: Mechanics of Solids (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

APPLICATION OF NEURAL NETWORKS FOR MODELING SHOCK-WAVE PROCESSES IN ALUMINUM A technique has been developed for the use of artificial neural networks to describe the nonlinear relationship between the components of stresses and strains (tensor equation of state) and the onset of plastic flow (homogeneous nucleation of dislocations) in metal single crystals by the example of aluminum. Datasets for training neural networks are generated using molecular dynamics (MD) modeling of uniform deformation of representative volumes of a single crystal. Axisymmetric deformed states are considered when the symmetry axis coincides with the [100] direction of the single crystal. The trained neural networks are used as approximating functions within the dislocation plasticity model generalized to the case of finite deformations. It is used to simulate the propagation of shock waves arising from the collision of plates. In the case of nanoscale plates, a comparison is made with direct MD simulation of the process. In an ideal single crystal, the elastic precursor retains a constant amplitude corresponding to the threshold of homogeneous nucleation of dislocations, while in a deformed single crystal it has a significantly lower amplitude and rapidly decays with distance. Nucleation ; Shock (circulatory) ; Crystal (programming language) ; Materials science ; Dislocation ; Shock wave ; Nonlinear system ; Artificial neural network ; Symmetry (geometry) ; Amplitude ; Mechanics ; Rotational symmetry ; Deformation (meteorology) ; Plasticity ; Classical mechanics ; Geometry ; Physics ; Optics ; Mathematics ; Computer science ; Composite material ; Thermodynamics ; Artificial intelligence Microstructure and mechanical properties ; Material Properties and Failure Mechanisms ; Microstructure and Mechanical Properties of Steels
Authors: Sanjar Adilov
Source: ChemRxiv (preprint, 2021-09-16)
Field: Materials Science — Materials Chemistry

Generative Pre-Training from Molecules SMILES is a line notation for entering and representing molecules. Being inherently a language construct, it allows estimating molecular data in a self-supervised fashion by employing machine learning methods for natural language processing (NLP). The recent success of attention-based neural networks in NLP has made large-corpora transformer pretraining a de facto standard for learning representations and transferring knowledge to downstream tasks. In this work, we attempt to adapt transformer capabilities to a large SMILES corpus by constructing a GPT-2-like language model. We experimentally show that a pretrained causal transformer captures general knowledge that can be successfully transferred to such downstream tasks as focused molecule generation and single-/multi-output molecular-property prediction. For each task, we freeze model parameters and attach trainable lightweight networks between attention blocks—adapters—as alternative to fine-tuning. With a relatively modest setup, our transformer outperforms the recently proposed ChemBERTa transformer and approaches state-of-the-art MoleculeNet and Chemprop results. Overall, transformers pretrained on SMILES corpora are promising alternatives that do not require handcrafted feature engineering, make few assumptions about structure of data, and scale well with the pretraining data size. Transformer ; Computer science ; Notation ; Artificial intelligence ; Generative grammar ; Natural language processing ; Language model ; Artificial neural network ; De facto ; Machine learning ; Voltage ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Turin Datta | Anil D. Pathak | Soumyabrata Basak | Srikant Gollapudi | Κ. K. Sahu
Source: Applied Surface Science Advances (article, 2021-06-12)
Field: Materials Science — Materials Chemistry

Fractal behavior of surface oxide crack patterns on AISI 4140 high-strength low-alloy steel exposed to the simulated offshore environment Wear and corrosion are surface-dependent degradation phenomena pertinent to the hostile environment existing in deep-sea oil and gas wells containing aggressive chemical species such as chloride/bromide salts, organic acids, carbon dioxide, and hydrogen sulfide,etc. Apart from the identification of the potential failures of alloys that can occur on a large and devastating scale by its interaction with these deleterious species, it is also essential to comprehend the nature and character, particularly the cracking pattern of the surface oxides formed on alloys undergoing corrosion in a particular environment. This article uses fractal geometry to study the branching of the surface oxide crack patterns in AISI 4140 high-strength low-alloy steel after exposure in a simulated offshore corrosive environment containing aqueous 3.5 wt.% sodium chloride solution. The fractal geometry was evaluated using the box-count technique in three different electrolytic test conditions: Electrochemical Anodic Polarization, Electrochemical Cathodic Polarization, and Immersion Corrosion for 24 h. Our results showed that the magnitude of fractal dimension is highest during immersion corrosion for 24 h and lowest during anodic polarization. Fractals can provide complementary quantifiable information about the surface geometries and fracture crack patterns from macrostructural to microstructural length scales by image analysis. This approach can be leveraged by emerging technologies like Artificial Intelligence (AI), Machine Learning (ML) and Computer Vision (CV), in structural health monitoring of alloys used in exotic and challenging conditions. Materials science ; Corrosion ; Oxide ; Chloride ; Cathodic protection ; Polarization (electrochemistry) ; Immersion (mathematics) ; Metallurgy ; Sulfide ; Fractal dimension ; Fractal ; Alloy ; Electrochemistry ; Hydrogen embrittlement ; Geometry ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques