jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thaís Cavalcante de Souza | Júlia Didier Pedrosa de Amorim | Cláudio José Galdino da Silva | Alexandre D’Lamare Maia de Medeiros | Andréa Fernanda de Santana Costa | Glória Maria Vinhas | Leonie Asfora Sarubbo
Source: Polymers (review, 2023-02-09)
Field: Materials Science — Biomaterials

Magnetic Bacterial Cellulose Biopolymers: Production and Potential Applications in the Electronics Sector Bacterial cellulose (BC) is a biopolymer that has been widely investigated due to its useful characteristics, such as nanometric structure, simple production and biocompatibility, enabling the creation of novel materials made from additive BC in situ and/or ex situ. The literature also describes the magnetization of BC biopolymers by the addition of particles such as magnetite and ferrites. The processing of BC with these materials can be performed in different ways to adapt to the availability of materials and the objectives of a given application. There is considerable interest in the electronics field for novel materials and devices as well as non-polluting, sustainable solutions. This sector influences the development of others, including the production and optimization of new equipment, medical devices, sensors, transformers and motors. Thus, magnetic BC has considerable potential in applied research, such as the production of materials for biotechnological electronic devices. Magnetic BC also enables a reduction in the use of polluting materials commonly found in electronic devices. This review article highlights the production of this biomaterial and its applications in the field of electronics. Electronics ; Bacterial cellulose ; Biopolymer ; Materials science ; Biocompatibility ; Nanotechnology ; Biomaterial ; Cellulose ; Process engineering ; Electrical engineering ; Engineering ; Composite material ; Chemical engineering Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Nanocomposite Films for Food Packaging
Authors: Tiandong Zhang | Huiduo Xu | Chuanxian Dai | Changhai Zhang | Yongquan Zhang | Qingguo Chi
Source: High Voltage (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Carbon nanotubes and hexagonal boron nitride nanosheets co‐filled ethylene propylene diene monomer composites: Improved electrical property for cable accessory applications Abstract Rubber‐based composites based on ethylene propylene diene monomer (EPDM) with excellent non‐linear electrical conductivity are preferred to serve as reinforced insulation in cable accessories, which can self‐adaptively regulate electric field distribution and avoid electric field concentration due to the non‐linear conductivity. The conductive carbon nanotubes (CNT) are filled into EPDM to improve the non‐linear conductivity, while the insulating hexagonal boron nitride nanosheets (h‐BN) are used to reconcile the electric breakdown strength. The results show that with the increase of CNT loading content, the non‐linear conductivity of CNT/h‐BN/EPDM composites becomes more prominent, accompanying the decrease of threshold field strength and increase of non‐linear coefficient. However, the electric breakdown strength of CNT/h‐BN/EPDM composites seriously deteriorates due to the increase of CNT content and temperature. By incorporating 10 wt.% h‐BN into the composites, the reduction percentage of breakdown strength can be significantly lowered, which is 19.95% of neat EPDM and 13.74% of CNT/h‐BN/EPDM composites at 70°C, respectively. The COMSOL Multiphysics simulation results demonstrate that using the CNT/h‐BN/EPDM composite as the reinforced insulation can eliminate the electric field concentration of the cable accessory as well as enable the cable accessory with good lightning shock resistance. Materials science ; Composite material ; Carbon nanotube ; Boron nitride ; Electrical resistivity and conductivity ; Zinc borate ; Conductivity ; Electric field ; Composite number ; Electrical conductor ; Fire retardant High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Cameron Gruich | Varun Madhavan | Zhaoran Wang | Bryan R. Goldsmith
Source: Machine Learning Science and Technology (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification Abstract It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k -fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties. Uncertainty quantification ; Computer science ; Monte Carlo method ; Trustworthiness ; Calibration ; Artificial neural network ; Convolutional neural network ; Heterogeneous catalysis ; Artificial intelligence ; Catalysis ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Jaime Carracedo-Cosme | Carlos Romero‐Muñiz | Pablo Jauralde Pou | Rúben Pérez
Source: ACS Applied Materials & Interfaces (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks Spectroscopic methods─like nuclear magnetic resonance, mass spectrometry, X-ray diffraction, and UV/visible spectroscopies─applied to molecular ensembles have so far been the workhorse for molecular identification. Here, we propose a radically different chemical characterization approach, based on the ability of noncontact atomic force microscopy with metal tips functionalized with a CO molecule at the tip apex (referred as HR-AFM) to resolve the internal structure of individual molecules. Our work demonstrates that a stack of constant-height HR-AFM images carries enough chemical information for a complete identification (structure and composition) of quasiplanar organic molecules, and that this information can be retrieved using machine learning techniques that are able to disentangle the contribution of chemical composition, bond topology, and internal torsion of the molecule to the HR-AFM contrast. In particular, we exploit multimodal recurrent neural networks (M-RNN) that combine convolutional neural networks for image analysis and recurrent neural networks to deal with language processing, to formulate the molecular identification as an imaging captioning problem. The algorithm is trained using a data set─which contains almost 700,000 molecules and 165 million theoretical AFM images─to produce as final output the IUPAC name of the imaged molecule. Our extensive test with theoretical images and a few experimental ones shows the potential of deep learning algorithms in the automatic identification of molecular compounds by AFM. This achievement supports the development of on-surface synthesis and overcomes some limitations of spectroscopic methods in traditional solution-based synthesis. Convolutional neural network ; Molecule ; Chemical nomenclature ; Materials science ; Identification (biology) ; Artificial neural network ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Biological system ; Nanotechnology ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Surface Chemistry and Catalysis
Authors: Shaoping Xiao | John Li | Stéphane Bordas | Tae‐Yeon Kim
Source: Advances in applied mechanics (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Artificial neural networks and their applications in computational materials science: A review and a case study Artificial neural network ; Artificial intelligence ; Computer science ; Convolutional neural network ; Deep learning ; Machine learning Machine Learning in Materials Science ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Jianjun Hu | Yong Zhao | Qin Li | Yuqi Song | Rongzhi Dong | Wenhui Yang | Edirisuriya M. Dilanga Siriwardane
Source: ACS Omega (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first-principles free energy calculations to predict the ground-state crystal structure of a given material composition. These ab initio algorithms are currently too slow for predicting complex material structures. Inspired by the AlphaFold algorithm for protein structure prediction, herein, we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model for predicting the atomic contact map of a target material followed by three-dimensional (3D) structure reconstruction using genetic algorithms. Extensive experiments on 20 benchmark structures showed that our AlphaCrystal algorithm can predict structures close to the ground truth structures, which can significantly speed up the crystal structure prediction and handle relatively large systems. Crystal structure prediction ; Benchmark (surveying) ; Algorithm ; Crystal structure ; Computer science ; Residual ; Ab initio ; Crystal (programming language) ; Artificial neural network ; Protein structure prediction ; Artificial intelligence ; Energy minimization ; Genetic algorithm ; Machine learning ; Chemistry ; Computational chemistry ; Protein structure ; Crystallography ; Geology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Marltan O. Wilson | David M. Huang
Source: The Journal of Chemical Physics (article, 2023-07-10)
Field: Materials Science — Materials Chemistry

Anisotropic molecular coarse-graining by force and torque matching with neural networks We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We demonstrate the flexibility of the method by parametrizing single-site coarse-grained models of a rigid small molecule (benzene) and a semi-flexible organic semiconductor (sexithiophene), attaining structural accuracy close to the all-atom models for both molecules at a considerably lower computational expense. The machine-learning method of constructing the coarse-grained potential is shown to be straightforward and sufficiently robust to capture anisotropic interactions and many-body effects. The method is validated through its ability to reproduce the structural properties of the small molecule's liquid phase and the phase transitions of the semi-flexible molecule over a wide temperature range. Anisotropy ; Granularity ; Artificial neural network ; Statistical physics ; Torque ; Flexibility (engineering) ; Phase (matter) ; Molecule ; Chemical physics ; Range (aeronautics) ; Molecular dynamics ; Atom (system on chip) ; Materials science ; Physics ; Computer science ; Biological system ; Chemistry ; Artificial intelligence ; Computational chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Force Microscopy Techniques and Applications
Authors: Grace M. Lu | Matthew Witman | Sapan Agarwal | Vitalie Stavila | Dallas R. Trinkle
Source: Physical Review Materials (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Explainable machine learning for hydrogen diffusion in metals and random binary alloys Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98--119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion. Binary number ; Random forest ; Materials science ; Diffusion ; Feature (linguistics) ; Thermal diffusivity ; Machine learning ; Hydrogen ; Artificial intelligence ; Computer science ; Statistical physics ; Thermodynamics ; Chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Rebecca Betts | Ingo Dierking
Source: Soft Matter (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning classification of polar sub-phases in liquid crystal MHPOBC Experimental polarising microscopy texture images of the fluid smectic phases and sub-phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*), ferroelectric (SmC*), ferrielectric (SmC<sub>1/3</sub>*), and antiferroelectric (SmC<sub>A</sub>*) using convolutional neural networks, CNNs. Two neural network architectures were tested, a sequential convolutional neural network with varying numbers of layers and a simplified inception model with varying number of inception blocks. Both models are successful in binary classifications between different phases as well as classification between all four phases. Optimised architectures for the multi-phase classification achieved accuracies of (84 ± 2)% and (93 ± 1)% for sequential convolutional and inception networks, respectively. The results of this study contribute to the understanding of how CNNs may be used in classifying liquid crystal phases. Especially the inception model is of sufficient accuracy to allow automated characterization of liquid crystal phase sequences and thus opens a path towards an additional method to determine the phases of novel liquid crystals for applications in electro-optics, photonics or sensors. The outlined procedure of supervised machine learning can be applied to practically all liquid crystal phases and materials, provided the infrastructure of training data and computational power is provided. Antiferroelectricity ; Artificial intelligence ; Liquid crystal ; Convolutional neural network ; Artificial neural network ; Phase (matter) ; Computer science ; Texture (cosmology) ; Materials science ; Ferroelectricity ; Binary number ; Crystal (programming language) ; Machine learning ; Pattern recognition (psychology) ; Dielectric ; Physics ; Mathematics ; Optoelectronics ; Image (mathematics) Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Neurobiology and Insect Physiology Research
Authors: Aparna Ashok | Anjana S. Desai | Rajesh Mahadeva | Shashikant P. Patole | Brajesh Pandey | Neeru Bhagat
Source: Engineered Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Research Network Analysis and Machine Learning on Heusler Alloys Heusler alloys are an incredible class of inter-metallic materials with different compositions and over 1500 members.Though discovered a century back, they are an active area of physics and material science research.Novel properties and potential fields of applications materialize constantly.Even the alloy system is extensively investigated owing to its shape memory behavior and prospective relevance in the development of actuator devices, where strains are controlled by applying an external magnetic field.Heusler alloys are currently the material of interest due to their properties leading to their use as shape memory alloys and topological insulators.Hence, predicting and determining their composition and structure is imperative before synthesis.Utilizing the conventional method in determining the possible changes in the properties and the structure of the proposed compositions is tedious and time-consuming.In the current consumerism-driven environment, we require a faster method to predict the structure of the proposed alloy or compound or other parameters for the desired application.Once the prediction is made, it must be tested experimentally by synthesizing the material and characterizing its behavior.This analysis is focusing on network analysis with a supervised machine learning approach to study the properties of Heusler alloys with their application as shape memory alloys. Shape-memory alloy ; Materials science ; Relevance (law) ; Alloy ; Field (mathematics) ; Actuator ; Computer science ; Mechanical engineering ; Artificial intelligence ; Nanotechnology ; Metallurgy ; Engineering ; Mathematics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Shape Memory Alloy Transformations
Authors: Shiyan Jiang | Jeanne McClure | Hongjing Mao | Jiahui Chen | Yunshu Liu | Yang Zhang
Source: Journal of Chemical Education (article, 2023-12-07)
Field: Chemistry — Physical and Theoretical Chemistry

Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving Artificial intelligence (AI) is rapidly transforming our world, making it imperative to educate the next generation about both the potential benefits and challenges associated with AI. This study presents a cross-disciplinary curriculum that connects AI and chemistry disciplines in the high school classroom. Particularly, we leverage machine learning (ML), an important and simple application of AI to instruct students to build an ML-based virtual pH meter for high-precision pH read-outs. We used a "codeless" and free ML neural network building software - Orange, along with a simple chemical topic of pH to show the connection between AI and chemistry for high-schoolers who might have rudimentary backgrounds in both disciplines. The goal of this curriculum is to promote student interest and drive in the analytical chemistry domain and offer insights into how the interconnection between chemistry and ML can benefit high-school students in science learning. The activity involves students using pH strips to measure the pH of various solutions with local relevancy and then building an ML neural network model to predict the pH value based on color changes of pH strips. The integrated curriculum increased student interest in chemistry and ML and demonstrated the relevance of science to their daily lives and global issues. This approach is transformative in developing a broad spectrum of integration topics between chemistry and ML and understanding their global impacts. Curriculum ; Chemistry ; Computer science ; Artificial intelligence ; Mathematics education ; Pedagogy ; Mathematics ; Psychology Various Chemistry Research Topics ; Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research
Authors: Hao‐Bing Yu | Balaganesh Muthiah | Shih‐Cheng Li | Wenyuan Yu | Yi‐Pei Li
Source: Materials Today Sustainability (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO Cerium oxide ; Cerium ; Characterization (materials science) ; Adsorption ; Density functional theory ; Molecule ; Infrared spectroscopy ; Catalysis ; Infrared ; Oxide ; Materials science ; Spectroscopy ; Chemical physics ; Chemistry ; Physical chemistry ; Nanotechnology ; Inorganic chemistry ; Computational chemistry ; Physics ; Organic chemistry ; Optics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Tao-Tao Shi | Gao-Yong Liu | Zhao‐Xu Chen
Source: The Journal of Physical Chemistry C (article, 2023-05-12)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of CO Adsorption Energies and Properties of Layered Alloys Using an Improved Feature Selection Algorithm Layered alloys are widely studied as designable catalysts. As a surface probe molecule, CO adsorption energy is not only employed to characterize surface properties but also used as the catalytic activity descriptor in various reactions. With the aid of high-throughput computing technology, we calculated CO adsorption energies on 3729layered alloy surfaces. To obtain CO adsorption energies, the d-band center and d-band skewness, and the stability of all the remaining layered alloys (8415) of 23 transition metals, we collected 91 features that do not require time-consuming quantum chemistry calculations (non-QC features) and 40 features from quantum chemistry calculations (QC features). To reduce the feature dimension and overcome overfitting problems, we proposed a modified sequential feature selection (SFS) wrapper method to identify (sub)optimal subsets. Two supervised light gradient boosting machine regression (LGBMR) machine learning (ML) regression models were established using the identified subsets. It is demonstrated that the size of the feature subset converges rapidly, and the performance of the model with size nine is already quite satisfactory. The ML model of the non-QC features outperforms that of QC features. Using the ML models established with non-QC features, we predicted the CO adsorption energies and the electronic structure–properties (d-band center, d-band skewness) and stability of 8415 layered alloys. Based on the four conditions (CO adsorption energy, stability, price, and surface segregation), potential alloy catalysts for CO2 to methanol were screened out of 12144 layered alloys. Adsorption ; Overfitting ; Feature selection ; Stability (learning theory) ; Materials science ; Feature (linguistics) ; MNIST database ; Band gap ; Algorithm ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Physical chemistry ; Artificial neural network ; Optoelectronics Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts