jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James L. McDonagh | Benjamin H. Wunsch | Stamatia Zavitsanou | Alexander Harrison | Bruce G. Elmegreen | Stacey M. Gifford | Theodore van Kessel | Flaviu Cipcigan
Source: arXiv (Cornell University) (preprint, 2023-03-24)
Field: Materials Science — Materials Chemistry

Machine Guided Discovery of Novel Carbon Capture Solvents The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and efficiency, faces the challenge of materials development, which can require substantial costs and time. Machine learning offers a promising method for reducing the time and resource burdens of materials development through efficient correlation of structure-property relationships to allow down-selection and focusing on promising candidates. Towards demonstrating this, we have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture. We combine a simple, rapid laboratory assay for CO2 absorption with a machine learning based molecular fingerprinting model approach. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set. The discovery cycle determined several promising amines that were verified experimentally, and which had not been applied to carbon capture previously. In the process we have compiled a large, single-source data set for carbon capture amines and produced an open source machine learning tool for the identification of amine molecule candidates (https://github.com/IBM/Carbon-capture-fingerprint-generation). Computer science ; Scalability ; Fingerprint (computing) ; Data scrubbing ; Process (computing) ; Process engineering ; Carbon capture and storage (timeline) ; Artificial intelligence ; Database ; Engineering ; Operating system Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies ; Computational Drug Discovery Methods
Authors: Tao Sun | Jianmei Yuan
Source: Acta Physica Sinica (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Band gap prediction of perovskite materials based on transfer learning &lt;sec&gt;The band gap is a key physical quantity in material design. First-principles calculations based on density functional theory can approximately predict the band gap, which often requires significant computational resources and time. Deep learning models have the advantages of good fitting capability and automatic feature extraction from the data, and are gradually used to predict the band gap. In this paper, aiming at the problem of quickly obtaining the band gap value of perovskite material, a feature fusion neural network model, named CGCrabNet, is established, and the transfer learning strategy is used to predict the band gap of perovskite material. The CGCrabNet extracts features from both chemical equation and crystal structure of materials, and fits the mapping between feature and band gap. It is an end-to-end neural network model. Based on the pre-training data obtained from the Open Quantum Materials Database (OQMD dataset), the CGCrabNet parameters can be fine-tuned by using only 175 perovskite material data to improve the robustness of the model.&lt;/sec&gt;&lt;sec&gt;The numerical and experimental results show that the prediction error of the CGCrabNet model for band gap prediciton based on the OQMD dataset is 0.014 eV, which is lower than that obtained from the prediction based on compositionally restricted attention-based network (CrabNet). The mean absolute error of the model developed in this paper for predicting perovskite materials is 0.374 eV, which is 0.304 eV, 0.441 eV and 0.194 eV lower than that obtained from random forest regression, support vector machine regression and gradient boosting regression, respectively. The mean absolute error of the test set of CGCrabNet trained only by using perovskite data is 0.536 eV, and the mean absolute error of the pre-trained CGCrabNet decreases by 0.162 eV, which indicates that the transfer learning strategy plays a significant role in improving the prediction accuracy of small data sets (perovskite material data sets). The difference between the predicted band gap of some perovskite materials such as SrHfO&lt;sub&gt;3&lt;/sub&gt; and RbPaO&lt;sub&gt;3&lt;/sub&gt; by the model and the band gap calculated by first-principles is less than 0.05 eV, which indicates that the CGCrabNet can quickly and accurately predict the properties of new materials and accelerate the development process of new materials.&lt;/sec&gt; Band gap ; Robustness (evolution) ; Computer science ; Artificial intelligence ; Artificial neural network ; Perovskite (structure) ; Materials science ; Machine learning ; Algorithm ; Computational physics ; Optoelectronics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Srishyam Raghavan | Brian P. Chaplin | Shafigh Mehraeen
Source: Journal of Chemical Information and Modeling (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Small-Molecule Adsorption Energy Predictions for High-Throughput Screening of Electrocatalysts Predicting adsorption energies of small molecules (e.g., OH, OOH, CO) on electrocatalysts involved in electrochemical reactions aids in accelerating the design and screening of electrocatalysts. Avoiding computationally expensive electronic structure calculations increases the speed of such predictions. Geometric and electronic descriptors have been reported to characterize the environment around surface active sites and predict adsorption energies. However, these descriptors cannot be used to predict adsorption energies of small molecules on various substrates, e.g., metal-oxide and nonmetal electrocatalysts. We compare the performance of these descriptors in predicting adsorption energies of small molecules on various electrocatalysts with adsorption energies calculated from density functional theory. We show that two recently developed machine learning algorithms, Crystal Graph Convolutional Neural Network (CGCNN) and Atomistic Line Graph Neural Network (ALIGNN), outperform the reported descriptors based on geometric (coordination number of the active site and its nearest neighbors) and electronic (the bond-energy-integrated orbitalwise coordination number, the electronegativity, and the number of valence electrons of the active site) properties in predicting the adsorption energies. Our results suggest that ALIGNN is almost always more accurate than CGCNN in adsorption energy predictions. The improvement ranges from 0.02 to 1.0 eV in the mean absolute errors (MAEs). We also compare the performance of CGCNN and ALIGNN algorithms in predicting the overpotentials of the oxygen evolution reaction occurring on various electrocatalysts with MAEs of 0.06 and 0.05 V, respectively. Adsorption ; Electronegativity ; Molecule ; Density functional theory ; Electronic structure ; Valence (chemistry) ; Electrochemistry ; Materials science ; Artificial neural network ; Chemistry ; Chemical physics ; Computational chemistry ; Computer science ; Physical chemistry ; Machine learning ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Md Mokhlesur Rahman | Shazia Janwari | Minsu Choi | Umesh V. Waghmare | Jaichan Lee
Source: Materials & Design (article, 2023-11-25)
Field: Materials Science — Materials Chemistry

Accelerating search for the polar phase stability of ferroelectric oxide by machine learning Machine learning emerges to accelerate first-principles calculations in materials discovery and property prediction, but developing high-accuracy prediction models requires training on large datasets of relaxed structures, which can be computationally expensive. Besides, the crystal structure prediction algorithms show promising aspects but often deviate from the ground state, leading to poor predictions. Using a crystal graph convolutional neural network, we propose an alternative and efficient method of finding the ground state by utilizing the total energy of random unrelaxed structures. The prediction model is trained on a small dataset of unrelaxed energy obtained from first-principles calculations and effectively predicts the total energy of random structures with an accuracy almost equivalent to first-principles calculations. The resulting energy landscape is then reconstructed in terms of polarity. This leads to establishing an energy landscape of a perovskite oxide in a cost-effective way, enabling a polar phase stability map of strained BaTiO3, a representative ferroelectric oxide. The proposed methodology efficiently allows to obtain the ground state by eliminating the computationally intensive procedure of structure optimization, thus accelerating the ground state search to map out the phase stability of strained perovskite materials in a practical manner. Stability (learning theory) ; Ground state ; Energy landscape ; Materials science ; Ferroelectricity ; Perovskite (structure) ; Polar ; Crystal structure prediction ; Phase (matter) ; Computer science ; Energy (signal processing) ; Artificial intelligence ; Algorithm ; Machine learning ; Crystal structure ; Physics ; Engineering Machine Learning in Materials Science ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Shuqin Wang | Zhongchao Wei | Ruihuan Wu | Qiongxiong Ma | Wen Feng Ding | Jianping Guo
Source: Plasmonics (article, 2023-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Residual Parallel Neural Networks Aided Inverse Design for Multifunctional Reconfigurable Metamaterial Perfect Absorbers Residual ; Metamaterial ; Broadband ; Artificial neural network ; Computer science ; Inverse ; Bandwidth (computing) ; Absorption (acoustics) ; Materials science ; Artificial intelligence ; Telecommunications ; Algorithm ; Optoelectronics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Acoustic Wave Phenomena Research
Authors: Zhengyang Zhang | Han Fang | Xu Zhao | Jiajie Lv | Yao Shen | Yanming Wang
Source: APL Machine Learning (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Multi-objective generative design of three-dimensional material structures Generative design for materials has recently gained significant attention due to the rapid evolution of generative deep learning models. There have been a few successful generative design demonstrations of molecular-level structures with the help of graph neural networks. However, in the realm of macroscale material structures, most of the works are targeting two-dimensional, ungoverned structure generations. Hindered by the complexity of 3D structures, it is hard to extract customized structures with multiple desired properties from a large, unexplored design space. Here we report a novel framework, a multi-objective driven Wasserstein generative adversarial network (WGAN), to implement inverse designs of 3D structures according to given geometrical, structural, and mechanical requirements. Our framework consists of a WGAN-based network that generates 3D structures possessing geometrical and structural features learned from the target dataset. Besides, multiple objectives are introduced to our framework for the control of mechanical property and isotropy of the structures. An accurate surrogate model is incorporated into the framework to perform efficient prediction on the properties of generated structures in training iterations. With multiple objectives combined by their weight and the 3D WGAN acting as a soft constraint to regulate features that are hard to define by the traditional method, our framework has proven to be capable of tuning the properties of the generated structures in multiple aspects while keeping the selected structural features. The feasibility of a small dataset and the scalability of the objectives of other properties make our work an effective approach to provide fast and automated structure designs for various functional materials. Generative grammar ; Computer science ; Generative Design ; Constraint (computer-aided design) ; Generative model ; Scalability ; Artificial neural network ; Isotropy ; Artificial intelligence ; Engineering ; Mechanical engineering ; Compatibility (geochemistry) Machine Learning in Materials Science ; Composite Material Mechanics ; Cellular and Composite Structures
Authors: Liwen Li | Huixian Liu | Yuyao Qin | Hua Wang | Jinyu Han | Xinli Zhu | Qingfeng Ge
Source: The Journal of Chemical Physics (article, 2023-01-17)
Field: Materials Science — Materials Chemistry

Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuO<sub>x</sub> with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(O<sub>V</sub>)] < 50% and Cu-Cu at C(O<sub>V</sub>) > 50% for CuO<sub>x</sub> formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu<sub>2</sub>O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes. Oxygen ; Desorption ; Catalysis ; Oxide ; Density functional theory ; Reduction (mathematics) ; Copper ; Transformation (genetics) ; Chemistry ; Phase (matter) ; Materials science ; Chemical physics ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Adsorption ; Metallurgy ; Geometry ; Mathematics Catalytic Processes in Materials Science ; Copper-based nanomaterials and applications ; Electronic and Structural Properties of Oxides
Authors: S. E. Tan | Dao‐Guang He | Y.C. Lin | Bing-Kun Zheng | Heyi Wu
Source: Materials (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Dislocation Substructures Evolution and an Informer Constitutive Model for a Ti-55511 Alloy in Two-Stages High-Temperature Forming with Variant Strain Rates in β Region The high-temperature compression characteristics of a Ti-55511 alloy are explored through adopting two-stage high-temperature compressed experiments with step-like strain rates. The evolving features of dislocation substructures over hot, compressed parameters are revealed by transmission electron microscopy (TEM). The experiment results suggest that the dislocations annihilation through the rearrangement/interaction of dislocations is aggravated with the increase in forming temperature. Notwithstanding, the generation/interlacing of dislocations exhibit an enhanced trend with the increase in strain in the first stage of forming, or in strain rates at first/second stages of a high-temperature compressed process. According to the testing data, an Informer deep learning model is proposed for reconstructing the stress-strain behavior of the researched Ti-55511 alloy. The input series of the established Informer deep learning model are compression parameters (compressed temperature, strain, as well as strain rate), and the output series are true stresses. The optimal input batch size and sequence length are 64 and 2, respectively. Eventually, the predicted results of the proposed Informer deep learning model are more accordant with the tested true stresses compared to those of the previously established physical mechanism model, demonstrating that the Informer deep learning model enjoys an outstanding forecasted capability for precisely reconstructing the high-temperature compressed features of the Ti-55511 alloy. Materials science ; Dislocation ; Strain rate ; Compression (physics) ; Strain (injury) ; Alloy ; Series (stratigraphy) ; Stress (linguistics) ; Transmission electron microscopy ; Crystallography ; Composite material ; Nanotechnology ; Geology Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Nuclear Materials and Properties
Authors: Sourav Deb | Anik Sahoo | Srikanta Karmakar | Sujoy Baitalik
Source: Inorganica Chimica Acta (article, 2023-02-25)
Field: Chemistry — Spectroscopy

Multi-channel anion sensing behaviour of a Ru(II)-bipyridine complex based on benzothiazolyl pyrazole ligand: Experimental and implication of machine learning tools for data prediction Chemistry ; Pyrazole ; Bipyridine ; Acetonitrile ; Ion ; 2,2'-Bipyridine ; Ligand (biochemistry) ; Absorption (acoustics) ; Stereochemistry ; Organic chemistry Molecular Sensors and Ion Detection ; Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Anamika Prasad | Vikas Varshney | Dhriti Nepal | Geoffrey J. Frank
Source: Biomimetics (review, 2023-10-20)
Field: Materials Science — Materials Chemistry

Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure-function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field. Waviness ; Interlocking ; Materials science ; Nanotechnology ; Image stitching ; Composite number ; Computer science ; Mechanical engineering ; Engineering ; Composite material MXene and MAX Phase Materials ; Bone Tissue Engineering Materials ; Calcium Carbonate Crystallization and Inhibition
Authors: Е. Г. Комышев | М. А. Генаев | I. D. Busov | Mikhail V. Kozhekin | N. V. Artemenko | Anastasiya Y. Glagoleva | V. S. Koval | Д. А. Афонников
Source: Vavilov Journal of Genetics and Breeding (article, 2023-12-11)
Field: Chemistry — Analytical Chemistry

Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods The pigment composition of plant seed coat affects important properties such as resistance to pathogens, pre-harvest sprouting, and mechanical hardness. The dark color of barley (Hordeum vulgare L.) grain can be attributed to the synthesis and accumulation of two groups of pigments. Blue and purple grain color is associated with the biosynthesis of anthocyanins. Gray and black grain color is caused by melanin. These pigments may accumulate in the grain shells both individually and together. Therefore, it is difficult to visually distinguish which pigments are responsible for the dark color of the grain. Chemical methods are used to accurately determine the presence/ absence of pigments; however, they are expensive and labor-intensive. Therefore, the development of a new method for quickly assessing the presence of pigments in the grain would help in investigating the mechanisms of genetic control of the pigment composition of barley grains. In this work, we developed a method for assessing the presence or absence of anthocyanins and melanin in the barley grain shell based on digital image analysis using computer vision and machine learning algorithms. A protocol was developed to obtain digital RGB images of barley grains. Using this protocol, a total of 972 images were acquired for 108 barley accessions. Seed coat from these accessions may contain anthocyanins, melanins, or pigments of both types. Chemical methods were used to accurately determine the pigment content of the grains. Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. The U-Net network model based on the EfficientNetB0 topology showed the best performance in the holdout set (the value of the "accuracy" parameter was 0.821). Pigment ; Anthocyanin ; Hordeum vulgare ; RGB color model ; Melanin ; Coat ; Biological system ; Artificial intelligence ; Materials science ; Composition (language) ; Biology ; Food science ; Chemistry ; Botany ; Computer science ; Biochemistry ; Poaceae ; Composite material Spectroscopy and Chemometric Analyses ; Plant Gene Expression Analysis ; Smart Agriculture and AI