jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Idelfonso B. R. Nogueira | Rafael O. M. Dias | Carine Menezes Rebello | Érbet Almeida Costa | Vinícius V. Santana | Alírio E. Rodrigues‬ | Alexandre Ferreira | Ana M. Ribeiro
Source: Process Safety and Environmental Protection (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

A novel nested loop optimization problem based on deep neural networks and feasible operation regions definition for simultaneous material screening and process optimization The present work proposes a novel strategy for simultaneous material screening and process optimization. This strategy is based on the capacities of deep neural networks to extract knowledge of a database. It makes use of a nested optimization loop which is designed to couple the process and material points of view simultaneously. The optimization problem results are analyzed by a Fisher–Snedecor test, designed to assess the optimal points uncertainties, building the process’s feasible operating regions. This methodology describes the processes’ possible operating points that lead to optimal conditions, considering the material type as a decision variable. The methodology shows that this complex problem can be better understood when the uncertainties are taken into consideration. On the other hand, the proposed optimization problem can provide a way to address the issues related to optimizing adsorption processes considering the material screening. Process (computing) ; Computer science ; Artificial neural network ; Mathematical optimization ; Nested loop join ; Optimization problem ; Test functions for optimization ; Process optimization ; Engineering optimization ; Engineering ; Artificial intelligence ; Multi-swarm optimization ; Data mining ; Mathematics ; Algorithm Machine Learning in Materials Science ; Mineral Processing and Grinding ; Fault Detection and Control Systems
Authors: Mostafa Yaghoobi | Mojtaba Alaei
Source: Computational Materials Science (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

Machine learning for compositional disorder: A comparison between different descriptors and machine learning frameworks Artificial neural network ; Computer science ; Ab initio ; Artificial intelligence ; Convolutional neural network ; Kernel (algebra) ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Minghao Guo | Veronika Thost | Beichen Li | Payel Das | Jie Chen | Wojciech Matusik
Source: arXiv (Cornell University) (preprint, 2022-03-15)
Field: Materials Science — Materials Chemistry

Data-Efficient Graph Grammar Learning for Molecular Generation The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. This presents a considerable challenge for the deep learning generative models to comprehensively describe the molecular design space. Another major challenge is to generate only physically synthesizable molecules. This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data. In this work, we propose a data-efficient generative model that can be learned from datasets with orders of magnitude smaller sizes than common benchmarks. At the heart of this method is a learnable graph grammar that generates molecules from a sequence of production rules. Without any human assistance, these production rules are automatically constructed from training data. Furthermore, additional chemical knowledge can be incorporated in the model by further grammar optimization. Our learned graph grammar yields state-of-the-art results on generating high-quality molecules for three monomer datasets that contain only ${\sim}20$ samples each. Our approach also achieves remarkable performance in a challenging polymer generation task with only $117$ training samples and is competitive against existing methods using $81$k data points. Code is available at https://github.com/gmh14/data_efficient_grammar. Computer science ; Generative grammar ; Grammar ; Chemical space ; Artificial intelligence ; Graph ; Machine learning ; Artificial neural network ; Task (project management) ; Natural language processing ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Florian Göltl
Source: The Journal of Physical Chemistry C (article, 2022-02-10)
Field: Materials Science — Materials Chemistry

Three Grand Challenges for the Computational Design of Heterogeneous Catalysts Over the past decades, first-principles-based modeling has transformed heterogeneous catalysis. Initially, computational modeling has provided insights into the nature of the active sites and catalytic mechanisms, and soon the potential to predict materials with desirable catalytic properties was recognized. As of today, the computational design of heterogeneous catalysts is a mature field. At the same time continuous developments in computational methodology and increased availability of computational resources indicate a significant potential for growth in the future. In this Perspective, I outline three potential directions for the development of the computational design of heterogeneous catalysts that directly address the shortcomings of today’s catalyst design paradigms. These three grand challenges are (i) machine learning and AI for the automated discovery of heterogeneous catalysts, (ii) the selective modification of potential energy surfaces, and (iii) the holistic design of heterogeneous catalysts. The motivation for progress in each area is given; before the challenges, the current state of the art and a long-term goal to aspire to are discussed. If these long-term goals are achieved, the three grand challenges described in this contribution have the potential to transform materials discovery for heterogeneous catalysis and will lead to a shift from an experimentally based process today to a computationally driven future. Biochemical engineering ; Computational model ; Computational simulation ; Computer science ; Catalysis ; Nanotechnology ; Field (mathematics) ; Rational design ; Heterogeneous catalysis ; Chemistry ; Artificial intelligence ; Materials science ; Engineering ; Computational science ; Mathematics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Laisheng Zhang | Zhong Zhuang | Q.F. Fang | Xianping Wang
Source: Materials (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Study on the Automatic Identification of ABX3 Perovskite Crystal Structure Based on the Bond-Valence Vector Sum Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory (DFT) and experimental data from X-ray diffraction (XRD) curve fitting. These two techniques cannot be utilized to identify materials on a wide scale in businesses since they require expensive equipment and take a lot of time. Machine learning (ML), which is based on big data statistics and nonlinear modeling, has advanced significantly in recent years and is now capable of swiftly and reliably predicting the structures of materials with known chemical ratios based on a few key material-specific factors. A dataset encompassing 1647 perovskite compounds in seven crystal systems was obtained from the Materials Project database for this study, which used the ABX3 perovskite system as its research object. A descriptor called the bond-valence vector sum (BVVS) is presented to describe the intricate geometry of perovskites in addition to information on the usual chemical composition of the elements. Additionally, a model for the automatic identification of perovskite structures was built through a comparison of various ML techniques. It is possible to identify the space group and crystal system using just a small dataset of 10 feature descriptors. The highest accuracy is 0.955 and 0.974, and the highest correlation coefficient (R2) value of the lattice constant can reach 0.887, making this a quick and efficient method for determining the crystal structure. Valence (chemistry) ; Crystal structure ; Perovskite (structure) ; Lattice constant ; Materials science ; Crystal system ; Artificial intelligence ; Computer science ; Algorithm ; Crystallography ; Diffraction ; Chemistry ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yanxian Tao | Xiongzhi Zeng | Yi Fan | Jie Liu | Zhenyu Li | Jinlong Yang
Source: The Journal of Physical Chemistry Letters (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Exploring Accurate Potential Energy Surfaces via Integrating Variational Quantum Eigensolver with Machine Learning The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an appealing application of quantum computing, we show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques as a promising scheme for exploring accurate PESs. Different from using a ML model to represent the potential energy, we encode the molecular geometry information into a deep neural network (DNN) to represent parameters of the variational quantum eigensolver (VQE), leaving the PES to the wave function ansatz. Once the DNN model is trained, the variational optimization procedure that hinders the application of the VQE to complex systems is avoided, and thus the evaluation of PESs is significantly accelerated. Numerical results demonstrate that a simple DNN model is able to reproduce accurate PESs for small molecules. Ansatz ; Computer science ; Quantum ; Wave function ; Quantum computer ; Artificial neural network ; Potential energy surface ; Potential energy ; Artificial intelligence ; Statistical physics ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Sterling G. Baird | Kevin Maik Jablonka | Michael Alverson | Hasan M. Sayeed | Mohammed Faris Khan | Colton C. Seegmiller | Berend Smit | Taylor D. Sparks
Source: The Journal of Open Source Software (article, 2022-08-03)
Field: Materials Science — Materials Chemistry

xtal2png: A Python package for representing crystalstructure as PNG files The latest advances in machine learning are often in natural language processing such as with long short-term memory networks (LSTMs) and Transformers, or image processing such as with generative adversarial networks (GANs), variational autoencoders (VAEs), and guided diffusion models. xtal2png encodes and decodes crystal structures via PNG images (see e.g. Figure This is akin to making/reading a QR code for crystal structures, where the xtal2png representation is an invertible representation. The ability to feed these images directly into image-based pipelines allows you, as a materials informatics practitioner, to get streamlined results for new state-of-the-art image-based machine learning models applied to crystal structures. Python (programming language) ; Computer science ; Invertible matrix ; Artificial intelligence ; Representation (politics) ; Crystal (programming language) ; Theoretical computer science ; Programming language ; Computer graphics (images) ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Ivan Izonin | Roman Tkachenko | Michal Greguš | Zoia Duriagina | Nataliya Shakhovska
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder’s alloys. It provides new possibilities for the efficient production of biocompatible implants for various functional purposes. However, the accuracy of the methods and models used should be as high as possible. In this paper we designed a hybrid PNN-SVM (Probabilistic Neural Network-Support Vector Machine) high-precision approach for the intelligent evaluation of alloy properties for additive manufacturing of biomedical implants. We have proposed a new approach for extending the dimensionality of input data space by the outputs of the summation layer of the modified PNN topology. Subsequent classification based on the expanded dataset is performed using SVM. We conducted experimental modeling of the proposed approach using a data set on the properties of titanium alloys Ti-6Al-4V and Ti-Al-V-Zr. We have demonstrated a significant increase in the accuracy of the PNN-SVM scheme compared to the single classifiers that form it and other machine learning methods. Support vector machine ; Materials science ; Artificial neural network ; Computer science ; Process (computing) ; Artificial intelligence ; Machine learning Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Bone Tissue Engineering Materials
Authors: Amit Kumar Gope | Yu-Shu Liao | Chung‐Feng Jeffrey Kuo
Source: Polymers (article, 2022-07-04)
Field: Materials Science — Polymers and Plastics

Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine's processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality. Polypropylene ; Artificial intelligence ; Melt spinning ; Machine learning ; Fiber ; Quality (philosophy) ; Deep learning ; Identification (biology) ; Spinning ; Algorithm ; Computer science ; Materials science ; Pattern recognition (psychology) ; Composite material ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Xue Jiang | Yu Yan | Yanjing Su
Source: npj Materials Degradation (article, 2022-11-11)
Field: Materials Science — Metals and Alloys

Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis Abstract Corrosion initiation and propagation are a time-series problem, evolving continuously with corrosion time, and future pitting behavior depends closely on the past. Predicting localized corrosion for corrosion-resistant alloys remains a great challenge, as macroscopic experiments and microscopic theoretical simulations cannot couple internal and external factors to describe the pitting evolution from a time dimension. In this work, a data-driven method based on time-series analysis was explored. Taking cobalt-based alloys and duplex stainless steels as the case scenario, a corrosion propagation model was built to predict the free corrosion potential (E corr ) using a long short-term memory neural network (LSTM) based on 150 days of immersion testing in saline solution. Compared to traditional machine learning methods, the time-series analysis method was more consistent with the evolution of ground truth in the E corr prediction of the subsequent 70 days’ immersion, illustrating that time-series dependency of pitting propagation could be captured and utilized. Corrosion ; Pitting corrosion ; Materials science ; Duplex (building) ; Immersion (mathematics) ; Series (stratigraphy) ; Time series ; Metallurgy ; Time evolution ; Computer science ; Mathematics ; Machine learning ; Geology ; Chemistry ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Joshua Ojih | Mohammed Al‐Fahdi | Alejandro Rodriguez | Kamal Choudhary | Ming Hu
Source: npj Computational Materials (article, 2022-07-04)
Field: Materials Science — Materials Chemistry

Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations Abstract Despite the machine learning (ML) methods have been largely used recently, the predicted materials properties usually cannot exceed the range of original training data. We deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory (DFT) in searching extreme material properties. This combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry, but also yields properties beyond original training range. We use Stein novelty to recommend outliers and then verify using DFT. Validated data are then added into the training dataset for next round iteration. We test the loop of training-recommendation-validation in mechanical property space. By screening 85,707 crystal structures, we identify 21 ultrahigh hardness structures and 11 negative Poisson’s ratio structures. The algorithm is very promising for future materials discovery that can push materials properties to the limit with minimal DFT calculations on only ~1% of the structures in the screening pool. Outlier ; Novelty ; Range (aeronautics) ; Density functional theory ; Computer science ; Space (punctuation) ; Limit (mathematics) ; Property (philosophy) ; Poisson distribution ; Algorithm ; Artificial intelligence ; Mathematics ; Materials science ; Computational chemistry ; Statistics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Force Microscopy Techniques and Applications
Authors: Lulu Xu | Yao Xiong | Ruimei Wu | Xiang Geng | Minghui Li | Hang Yao | Xu Wang | Yangping Wen | Shirong Ai
Source: Journal of The Electrochemical Society (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

An Emerging Machine Learning Strategy for the Fabrication of Nanozyme Sensor and Voltametric Determination of Benomyl In Agro-Products An emerging machine learning (ML) strategy for the fabrication of nanozyme sensor based on multi-walled carbon nanotubes (MWCNTs)/graphene oxide (GO)/dendritic silver nanoparticles (AgNPs) nanohybrid and the voltametric determination of benomyl (BN) residues in tea and cucumber samples is proposed. Nanohybrid is prepared by the electrodeposition of dendritic AgNPs on the surface of MWCNTs/GO obtained by a simple mixed-strategy. The orthogonal experiment design combined with back propagation artificial neural network with genetic algorithm is used to solve multi-factor problems caused by the fabrication of nanohybrid sensor for BN. Both support vector machine (SVM) algorithm and least square support vector machine (LS-SVM) algorithm are used to realize the intelligent sensing of BN compared with the traditional method. The as-fabricated electrochemical sensor displays high electrocatalytic capacity (excellent voltammetric response), unique oxidase-like characteristic (nanozyme), wide working range (0.2–122.2 μ M), good practicability (satisfactory recovery). It is feasible and practical that ML guides the fabrication of nanozyme sensor and the intelligent sensing of BN compared with the traditional method. This work will open a new avenue for guiding the synthesis of sensing materials, the fabrication of sensing devices and the intelligent sensing of target analytes in the future. Fabrication ; Support vector machine ; Nanotechnology ; Materials science ; Graphene ; Computer science ; Nanoparticle ; Machine learning Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques