jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin P. Treder | Chen Huang | Cameron G. Bell | Thomas J. A. Slater | M. Schuster | Doğan Özkaya | Judy S. Kim | Angus I. Kirkland
Source: npj Computational Materials (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems Abstract We describe nNPipe for the automated analysis of morphologically diverse catalyst materials. Automated imaging routines and direct-electron detectors have enabled the collection of large data stacks over a wide range of sample positions at high temporal resolution. Simultaneously, traditional image analysis approaches are slow and hence unsuitable for large data stacks and consequently, researchers have progressively turned towards machine learning and deep learning approaches. Previous studies often detail work on morphologically uniform material systems with clearly discernible features, limited workable image sizes and training data that may be biased due to manual labelling. The nNPipe data-processing method consists of two standalone convolutional neural networks that were exclusively trained on multislice image simulations and enables fast analysis of 2048 × 2048 pixel images. Inference performance compared between idealised and real industrial catalytic samples and insights derived from subsequent data analysis are placed into the context of an automated imaging scenario. Computer science ; Convolutional neural network ; Pipeline (software) ; Context (archaeology) ; Artificial intelligence ; Pixel ; Artificial neural network ; Deep learning ; Sample (material) ; Image processing ; Pattern recognition (psychology) ; Slicing ; Inference ; Image (mathematics) ; Computer graphics (images) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yanbing Guo | Mingze Sun | Wang Zhang | Lvyuan Wang
Source: Metals (review, 2023-10-23)
Field: Materials Science — Biomaterials

Machine Learning in Enhancing Corrosion Resistance of Magnesium Alloys: A Comprehensive Review While magnesium alloys have garnered attention for their lightweight properties across diverse applications, their susceptibility to corrosion presents a formidable challenge. Recent years have witnessed the emergence of machine learning (ML) as a formidable tool for predicting and augmenting material properties, notably corrosion resistance. This comprehensive review investigates the latest advancements and hurdles in utilizing ML techniques to investigate the corrosion behavior of magnesium alloys. This article delves into a spectrum of ML algorithms, encompassing artificial neural networks, support vector machines, and random forests, elucidating their roles in predicting corrosion rates, morphologies, and other corrosion-related characteristics in magnesium alloys. Furthermore, it underscores the pivotal challenges and opportunities within this field, such as data quality, model interpretability, and model transferability. Finally, it examines the potential of ML methods in the conception and enhancement of magnesium alloys endowed with superior corrosion resistance. This review aspires to offer valuable insights into harnessing ML’s potential for optimizing magnesium alloy designs with heightened corrosion resistance, a facet of paramount importance across diverse industries, including the automotive, aerospace, and biomedical sectors. By addressing the challenges inherent in using ML to forecast corrosion rates, including data limitations and the intricacies of corrosion mechanisms, ML stands poised to emerge as a potent instrument for advancing the development of corrosion-resistant materials. Corrosion ; Interpretability ; Transferability ; Magnesium ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Machine learning Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Abbigayle E. Cuomo | Sebastian Ibarraran | Sanil Sreekumar | Haote Li | Jungmin Eun | Jan Paul Menzel | Pengpeng Zhang | Frédéric G. Buono | Jinhua J. Song | Robert H. Crabtree | Víctor S. Batista | Timothy R. Newhouse
Source: ACS Central Science (article, 2023-08-24)
Field: Chemistry — Inorganic Chemistry

Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with <i>P</i>-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures. Negishi coupling ; Artificial neural network ; Density functional theory ; Computer science ; Transition state ; Set (abstract data type) ; Chemistry ; Computational chemistry ; Combinatorial chemistry ; Artificial intelligence ; Catalysis ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Surface Chemistry and Catalysis
Authors: Joshua F. Belot | Valentin Taufour | Stefano Sanvito | Gus L. W. Hart
Source: Applied Physics Letters (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Machine learning predictions of high-Curie-temperature materials Technologies that function at room temperature often require magnets with a high Curie temperature, TC, and can be improved with better materials. Discovering magnetic materials with a substantial TC is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid TC predictions solely based on the chemical composition of a material. We train a random-forest model and a k-NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-TC materials and under-predict high-TC materials. For exhaustive searches to find new high-TC materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary. Curie temperature ; Random forest ; Ternary operation ; Curse of dimensionality ; Machine learning ; Marie curie ; Dimensionality reduction ; Curie ; Materials science ; Regression ; Artificial intelligence ; Computer science ; Thermodynamics ; Condensed matter physics ; Mathematics ; Physics ; Statistics ; Ferromagnetism Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yiqing Chen | Ying Zhao | Pengfei Ou | Jun Song
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Basal plane activation of two-dimensional transition metal dichalcogenides<i>via</i>alloying for the hydrogen evolution reaction: first-principles calculations and machine learning prediction Alloying induced basal plane activation of 2D cation-mixed transition metal dichalcogenides (TMDCs) for the hydrogen evolution reaction (HER) was systematically investigated using machine learning in conjunction with DFT calculations. Basal plane ; Transition metal ; Hydrogen ; Metal ; Materials science ; Plane (geometry) ; Chemistry ; Chemical physics ; Nanotechnology ; Crystallography ; Metallurgy ; Catalysis ; Geometry ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; Electrocatalysts for Energy Conversion
Authors: Chao Xin | Yaohui Yin | Bingqian Song | Zhen Fan | Yongli Song | Feng Pan
Source: Chip (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated discovery of novel 2D ferromagnetic materials with strong magnetization Two-dimensional ferromagnetic (2DFM) semiconductors (metals, half-metals, and so on) are important materials for next-generation nano-electronic and nano-spintronic devices. However, these kinds of materials remain scarce, and “trial and error” experiments and calculations are time-consuming and expensive. In the present work, to obtain optimal 2DFM materials with strong magnetization, we established a machine learning (ML) framework to search the 2D material space containing over 2417 samples, and identified 615 compounds whose magnetic orders was then determined via high-through-put first-principles calculations. Using ML algorithms, we trained two classification models and a regression model. The interpretability of the regression model was evaluated through Shapley additive explanations (SHAP) analysis. Unexpectedly, we found that Cr2NF2 is a potential antiferromagnetic ferroelectric 2D multiferroic material. More importantly, 60 novel 2DFM candidates were predicted, and among them, 13 candidates have magnetic moments of > 7 μB. Os2Cl8, Fe3GeSe2, and Mn4N3S2 were predicted to be novel 2DFM semiconductors, metals, and half-metals, respectively. Our ML approach can accelerate the prediction of 2DFM materials with strong magnetization and reduce the computation time by more than one order of magnitude. Spintronics ; Magnetization ; Ferromagnetism ; Condensed matter physics ; Antiferromagnetism ; Interpretability ; Materials science ; Multiferroics ; Magnetic semiconductor ; Machine learning ; Magnetic moment ; Computer science ; Ferroelectricity ; Artificial intelligence ; Physics ; Magnetic field ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Inorganic Chemistry and Materials
Authors: Junmin Chen | Kuang Yu
Source: Journal of Chemical Theory and Computation (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations An accurate, generalizable, and transferable force field plays a crucial role in the molecular dynamics simulations of organic polymers and biomolecules. Conventional empirical force fields often fail to capture precise intermolecular interactions due to their negligence of important physics, such as polarization, charge penetration, many-body dispersion, etc. Moreover, the parameterization of these force fields relies heavily on top-down fittings, limiting their transferabilities to new systems where the experimental data are often unavailable. To address these challenges, we introduce a general and fully ab initio force field construction strategy, named PhyNEO. It features a hybrid approach that combines both the physics-driven and the data-driven methods and is able to generate a bulk potential with chemical accuracy using only quantum chemistry data of very small clusters. Careful separations of long-/short-range interactions and nonbonding/bonding interactions are the key to the success of PhyNEO. By such a strategy, we mitigate the limitations of pure data-driven methods in long-range interactions, thus largely increasing the data efficiency and the scalability of machine learning models. The new approach is thoroughly tested on poly(ethylene oxide) and polyethylene glycol systems, giving superior accuracies in both microscopic and bulk properties compared to conventional force fields. This work thus offers a promising framework for the development of advanced force fields in a wide range of organic molecular systems. Force field (fiction) ; Scalability ; Nanotechnology ; Computer science ; Ab initio ; Field (mathematics) ; Materials science ; Statistical physics ; Chemical physics ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Yibing Zhang | Shengwen Bai | Bin Jiang | Kun Li | Zhihua Dong | Fusheng Pan
Source: Journal of Materials Research and Technology (article, 2023-04-22)
Field: Materials Science — Biomaterials

Modeling the correlation between texture characteristics and tensile properties of AZ31 magnesium alloy based on the artificial neural networks This work is aimed to investigate the relationship between the texture and tensile properties of the AZ31 Mg alloy by the machine learning method. The texture characteristics parameters, namely the maximum pole intensity (Imax), texture dispersion (D), and texture directivities along the longitudinal direction (PLD) and transverse direction (PTD), are extracted from the (0002) pole figures of the AZ31 Mg alloy. An artificial neural networks (ANN) model to describe the relationship between the texture characteristic parameters and tensile properties is constructed and trained by the data collected from the literature. To validate the reliability and generalization performance of the ANN, 6 samples with different texture characteristics are prepared, and their textures and tensile properties are evaluated through electron backscattered diffraction (EBSD) measurement and uniaxial tensile test, respectively. The results indicate that the ANN model exhibits good prediction performance in yield strength and elongation of the AZ31 Mg alloy when it is applied to the new cases. The correlations between the texture characteristics and tensile properties are analyzed according to the ANN-predicted results. The maximum pole intensity and texture dispersion significantly influence the tensile properties of the AZ31 Mg alloy. With increasing the Imax or decreasing the D, the strength is increased but the elongation is reduced. As increasing the texture directivity along the LD, the tensile properties of the AZ31 Mg alloy show non-monotonic changes. This research presents a correlation model between the texture and mechanical properties of the Mg alloy, which contributes to the development of high-performance Mg alloys. Materials science ; Ultimate tensile strength ; Texture (cosmology) ; Electron backscatter diffraction ; Alloy ; Tensile testing ; Elongation ; Pole figure ; Composite material ; Magnesium alloy ; Artificial intelligence ; Microstructure ; Computer science ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Dong Zhu | Chunyang Wang | Peichao Zou | Rui Zhang | Shefang Wang | Bohang Song | Xiaoyu Yang | Ke-Bin Low | Huolin L. Xin
Source: Nano Letters (article, 2023-08-29)
Field: Materials Science — Materials Chemistry

Deep-Learning Aided Atomic-Scale Phase Segmentation toward Diagnosing Complex Oxide Cathodes for Lithium-Ion Batteries Phase transformation─a universal phenomenon in materials─plays a key role in determining their properties. Resolving complex phase domains in materials is critical to fostering a new fundamental understanding that facilitates new material development. So far, although conventional classification strategies such as order-parameter methods have been developed to distinguish remarkably disparate phases, highly accurate and efficient phase segmentation for material systems composed of multiphases remains unavailable. Here, by coupling hard-attention-enhanced U-Net network and geometry simulation with atomic-resolution transmission electron microscopy, we successfully developed a deep-learning tool enabling automated atom-by-atom phase segmentation of intertwined phase domains in technologically important cathode materials for lithium-ion batteries. The new strategy outperforms traditional methods and quantitatively elucidates the correlation between the multiple phases formed during battery operation. Our work demonstrates how deep learning can be employed to foster an in-depth understanding of phase transformation-related key issues in complex materials. Atomic units ; Phase (matter) ; Cathode ; Transformation (genetics) ; Materials science ; Segmentation ; Ion ; Lithium (medication) ; Computer science ; Deep learning ; Scale (ratio) ; Atom (system on chip) ; Battery (electricity) ; Nanotechnology ; Artificial intelligence ; Chemistry ; Physics ; Electrical engineering ; Power (physics) ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Hridya P. Lal | Abhiram B.R. | Debraj Ghosh
Source: European Journal of Mechanics - A/Solids (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning Statistical physics ; Boundary value problem ; Length scale ; Molecular dynamics ; Regression ; Gaussian ; Scale parameter ; Computer science ; Mathematics ; Algorithm ; Physics ; Mechanics ; Mathematical analysis ; Statistics Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Pengcheng Xu | Xiaobo Ji | Minjie Li | Wencong Lu
Source: Journal of Materials Informatics (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

Virtual sample generation in machine learning assisted materials design and discovery Virtual sample generation (VSG), as a cutting-edge technique, has been successfully applied in machine learning-assisted materials design and discovery. A virtual sample without experimental validation is defined as an unknown sample, which is either expanded from the original data distribution for modeling or designed via algorithms for predicting. This review aims to discuss the applications of VSG techniques in machine learning-assisted materials design and discovery based on the research progress in recent years. First, we summarize the commonly used VSG algorithms in materials design and discovery for data expansion of the training set, including Bootstrap, Monte Carlo, particle swarm optimization, mega trend diffusion, Gaussian mixture model, random forest, and generative adversarial networks. Next, frequently employed searching algorithms for materials discovery are introduced, including particle swarm optimization, efficient global optimization, and proactive searching progress. Then, universally adopted inverse design methods are presented, including genetic algorithm, Bayesian optimization, and pattern recognition inverse projection. Finally, the future directions of VSG in the design and discovery of materials are proposed. Computer science ; Bayesian optimization ; Machine learning ; Sample (material) ; Artificial intelligence ; Particle swarm optimization ; Generative Design ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Face and Expression Recognition
Authors: Hakan Demir | Seda Keskın
Source: Chemical Engineering Journal (article, 2023-04-01)
Field: Chemistry — Inorganic Chemistry

Revealing acetylene separation performances of anion-pillared MOFs by combining molecular simulations and machine learning Acetylene is a crucial chemical feedstock that can be efficiently purified from CH4 and CO2 through adsorption-based separation methods. Combining advantages of organic and inorganic chemistry, metal–organic frameworks (MOFs) provide high separation performances in adsorption processes. In this work, anion-pillared (AP) MOFs were computationally investigated for C2H2/CH4 and C2H2/CO2 separations using Grand Canonical Monte Carlo (GCMC) simulations. Results of molecular simulations were used to compute selectivity, working capacity and regenerability, which were then combined to identify the top adsorbents and their structural features for C2H2/CH4 and C2H2/CO2 separations. The best adsorbents were computed to have C2H2 selectivities, working capacities, regenerabilities of 25.5–30.6 (6.1–7.3), 5.6–6 (4.9–5.8) mol/kg, 81.3–83 % (81.2–85.5 %) for C2H2/CH4 (C2H2/CO2) separation, respectively. We then developed machine learning (ML) models to accurately predict C2H2, CH4, and CO2 adsorption amounts in AP-MOFs for equimolar C2H2/CH4 and C2H2/CO2 mixtures by using pore-limiting diameter, surface area, isosteric heat of adsorption as the input features. ML-predicted gas adsorption amounts, separation performance metrics and adsorbent rankings were found to be in good agreement with those directly obtained from GCMC simulations. Therefore, ML models that we developed can be used to accurately and quickly screen large number of AP-MOFs and related materials to identify the top performing materials for C2H2/CH4 and C2H2/CO2 separations. Adsorption ; Acetylene ; Selectivity ; Limiting ; Chemistry ; Metal-organic framework ; Work (physics) ; Gas separation ; Materials science ; Chemical engineering ; Thermodynamics ; Organic chemistry ; Catalysis ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Qinyun Chen | Chao‐Hui Zheng | Yue Cui | Yan-Ru Lin | S.J. Zinkle
Source: Computational Materials Science (article, 2023-02-27)
Field: Materials Science — Surfaces, Coatings and Films

A deep learning model for automatic analysis of cavities in irradiated materials Ellipse ; Rectangle ; Computer science ; Segmentation ; Artificial intelligence ; Process (computing) ; Range (aeronautics) ; Deep learning ; Object detection ; Image (mathematics) ; Set (abstract data type) ; Image segmentation ; Materials science ; Computer vision ; Pattern recognition (psychology) ; Algorithm ; Geometry ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Neural Network Applications