jablonkagroup/rise_ai_scientists huggingface dataset:
Source:  (article, 2024-03-20)
Field: Materials Science — Materials Chemistry

Thermodynamic Equilibrium State Prediction by Deep Learning Method The method of calculation of phase diagrams (CALPHAD) is a calculation method that searches for a state of providing minimal Gibbs energy as an equilibrium state.To perform a thermodynamic equilibrium calculation for a single material composition and to predict a phase diagram, we can complete the CALPHAD method calculation within a realistic time.However, screening many material compositions associated with predicting the corresponding phase diagrams takes much time.For alloy materials, for example, it would take 161 hours to calculate phase diagrams of all alloy compositions to screen 10,000 sets of explanatory variables, i.e., compositions and manufacturing conditions, since it takes 58 seconds to calculate each set.The present study aims to provide a calculation device, method, and program for quickly predicting the thermodynamic equilibrium state.We developed a deep learning model based on the Transformer architecture to achieve this objective, primarily used for various natural language processing tasks, such as machine translation, text summarization, question answering, and sentiment analysis.The encoder part of our developed model extracts the necessary features for phase diagram prediction from the inputted alloying elements.In contrast, the decoder part predicts a phase diagram for each temperature based on the results from the encoder.We calculated 800,000 species using the CALPHAD method and employed these data to train our developed model.Our trained model can calculate thermodynamic equilibrium states more than 100 times faster than the CALPHAD method and correctly reproduce the phase diagrams of ground truths.Based on the present result, we could invent a calculation device, a calculation method, and a calculation program for predicting the thermodynamic equilibrium state in a short time. Thermodynamic equilibrium ; State (computer science) ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics ; Algorithm Machine Learning in Materials Science
Authors: H. R. Mahalingegowda | B. K. Narendra
Source: Journal of The Institution of Engineers (India) Series D (article, 2024-04-17)
Field: Materials Science — Polymers and Plastics

Strength Prediction of Agro Waste Mixed Composites Using a Neural Network Regression Model Artificial neural network ; Regression analysis ; Composite material ; Regression ; Materials science ; Computer science ; Machine learning ; Mathematics ; Statistics Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Innovations in Concrete and Construction Materials
Authors: Abraham J. Jordan | Zhuolei Zhang | Ellie Bennett | Leslie Guadalupe Castro | Dejun Han | Iva Rreza | Ayelet Teitelboim | Brian Theobald | Bereket Zekarias | Emory M. Chan | Jonathan S. Owen
Source: The Journal of Physical Chemistry C (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

One Pot Synthesis of Cyan Emitting CdZnSSe Quantum Dots for Human Centric Lighting A one pot synthesis of blue and green emissive CdZnSSe quantum dots (QDs) from thio- and selenoureas and Cd and Zn carboxylates is optimized using high throughput robotic optimization. A large set of spectral data (N = 192) is used to train machine learning models that accurately predict the photoluminescence emission wavelength (λmax) and full-width half-maximum, and the relative photoluminescence quantum yield (PLQY) from the S:Se and Zn:Cd stoichiometries and reaction time. ZnS shells are deposited on the crude QD heterostructures using 4-tert-butylbenzyl mercaptan, a more reactive source of sulfide that enables shell growth below the temperature where ion diffusion in the QD can broaden its optical spectrum (≤275 °C). These optimized procedures provide gram quantities of blue-green emitting QDs (PLQY = 85–99%) in a single reaction vessel. A solid state lighting device (4260 K) that incorporates cyan emissive QDs achieved a higher luminous efficacy of 179 lm/W and melanopic daylight efficiency ratio (0.71) than existing commercial human centric lighting devices. Cyan ; Quantum dot ; LED lamp ; Light-emitting diode ; Computer science ; Optoelectronics ; Computer graphics (images) ; Materials science ; Physics ; Optics Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Gia Khanh Pham | Kerim Yalcin | Alvin Wan | Anh Son Nguyen | Van Trung Trinh
Source: Key engineering materials (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Development of an Open-Source Software Tool for Microstructure Analysis of Materials Using Artificial Intelligence Investigating the microstructures of materials with microscopy is a key task in quality assurance, the development of new materials, and the optimization of manufacturing processes. However, conventional image analysis often demands significant time for analysis and a large volume of images, and the predictions produced are commonly constrained. Applying deep learning, models can be trained to analyze material microstructures quickly and with greater accuracy. The objective of this study is to provide a method for the automatic segmentation of microstructural images obtained from microscopes or scanning electron microscopes using Convolutional Neural Networks. For this purpose, two software scripts were developed in Python employing OpenCV and the fastai library. The first script is designed to generate reference images, while the second is utilized for training a model and predicting the microstructure in an image. The test of the microstructural analysis using the developed software tools demonstrates that robust prediction results are attainable by using high-quality reference images. This tool has been made available as an open-source on GitHub for public use in materials analysis and can be enhanced and further developed if required. Microstructure ; Open source ; Open source software ; Software ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Operating system Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Vision Systems and Defect Detection
Authors: Karim Elgammal | Marc Maußner
Source: arXiv (Cornell University) (preprint, 2024-12-01)
Field: Materials Science — Surfaces, Coatings and Films

A Quantum Computing Approach to Simulating Corrosion Inhibition This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies. Corrosion ; Quantum computer ; Computer science ; Quantum ; Computational science ; Distributed computing ; Physics ; Materials science ; Quantum mechanics ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Chuhang Hong | Xiaopei Wu | Jian Huang | Honglian Dai
Source: Materials Horizons (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Biomimetic fusion: Platyper's dual vision for predicting protein–surface interactions Predicting protein binding with the material surface still remains a challenge. Here, a novel approach, platypus dual perception neural network (Platyper), was developed to describe the interactions in protein-surface systems involving bioceramics with BMPs. The resulting model integrates a graph convolutional neural network (GCN) based on interatomic potentials with a convolutional neural network (CNN) model based on images of molecular structures. This dual-vision approach, inspired by the platypus's adaptive sensory system, addresses the challenge of accurately predicting the complex binding and unbinding dynamics in steered molecular dynamics (SMD) simulations. The model's effectiveness is demonstrated through its application in predicting surface interactions in protein-ligand systems. Notably, Platyper improves computational efficiency compared to classical SMD-based methods and overcomes the limitations of GNN-based methods for large-scale atomic simulations. The incorporation of heat maps enhances model's interpretability, providing valuable insights into its predictive capabilities. Overall, Platyper represents a promising advancement in the accurate and efficient prediction of protein-surface interactions in the context of bioceramics and growth factors. Dual (grammatical number) ; Mechanism (biology) ; Fusion ; Nanotechnology ; Surface (topology) ; Underwater ; Materials science ; Biological system ; Biophysics ; Biology ; Physics ; Geology ; Mathematics ; Geometry Machine Learning in Materials Science ; Lipid Membrane Structure and Behavior ; Force Microscopy Techniques and Applications
Authors: Gaoyuan Zhang | Thomas Borgert | Carmen Stoffelen | Christian Schmitz
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput and Explainable Machine Learning for Lacquer Formulations: Enhancing Coating Development by Interpretable Models Lacquer ; Throughput ; Coating ; Computer science ; Artificial intelligence ; Process engineering ; Machine learning ; Biochemical engineering ; Manufacturing engineering ; Materials science ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Adversarial Robustness in Machine Learning
Authors: Sachin Pradhan | Jaya Sharma Bhattarai | Muthuchamy Murugavel | Om P. Sharma
Source: ChemRxiv (preprint, 2024-08-15)
Field: Chemistry — Analytical Chemistry

Application of an Electronic Eye to Address the Limitations of Beer-Lambert Law Many scientific and industrial applications depend on the precise measurement of chemical concentrations. The current study demonstrates how an inventive method of combining photographic images with a machine learning (ML) model successfully estimates the concentration of potassium dichromate (K2Cr2O7) in solution. A predictive model is created by taking photographs of K2Cr2O7 solutions and evaluating the color intensities of those photographs using a ridge regression model. The pre-diction accuracy of the model was evaluated using MAE, MSE, and RMSE, and a high correlation between the actual and predicted concentrations of K2Cr2O7 was obtained with MAE, MSE, and RMSE values of 0.4%, 0.003%, and 0.5%, respectively. The ridge regression model is also extended to predict the concentration of KMnO4 and highlights the potential of integrating machine learning techniques with image analysis to accurately quantify the concentration of any chemical species in the solution state. As this model solely depends on the color intensity of the sample without any molecular interactions, it surpasses the limitations of the Beer-Lambert law. The created machine learning model also minimizes the requirement of substantial expertise and training, hence bridging the gap between the experienced and novice analysts. Mean squared error ; Artificial intelligence ; Linear regression ; Computer science ; Machine learning ; Regression ; Diction ; Law ; Mathematics ; Statistics ; Art Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Russlan Jaafreh | Surjeet Kumar | Kotiba Hamad | Jung‐Gu Kim
Source: Computational Materials Science (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Introducing Materials Fingerprint (MatPrint): A novel method in graphical material representation and features compression Representation (politics) ; Fingerprint (computing) ; Compression (physics) ; Computer science ; Materials science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Handwritten Text Recognition Techniques
Authors: Xingzi Yang | Wei Gao | Xiaodu Wang | Xiaowei Zeng
Source: Computer Modeling in Engineering & Sciences (article, 2024-01-01)
Field: Materials Science — Biomaterials

Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning The bioinspired nacre or bone structure represents a remarkable example of tough, strong, lightweight, and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials. The bioinspired structure consists of hard grains and soft material interfaces. While the material interface has a very low volume percentage, its property has the ability to determine the bulk material response. Machine learning technology nowadays is widely used in material science. A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite. This model was trained on a comprehensive dataset of material response and interface properties, allowing it to make accurate predictions. The results of this study demonstrate the efficiency and high accuracy of the machine learning model. The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process. Materials science ; Interface (matter) ; Nanocomposite ; Process (computing) ; Property (philosophy) ; Material properties ; Biomimetics ; Computer science ; Material Design ; Nanotechnology ; Mechanical engineering ; Machine learning ; Artificial intelligence ; Engineering ; Composite material Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Bone Tissue Engineering Materials
Authors: Yukun Li | Wenyuan Yang | Yan Zheng | Zhanqi Xing | Kaifeng Lu | Weiwei Zhang | Xiaohu Lin | Xiaoliang Wang | Gi Xue | Shaochuan Luo | Dongshan Zhou
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Efficiently Preparing Structure-Controllable High Entropy Anodes Via Computation-Guided Sintering in an Optimized Flash Furnace Download ; Computation ; Computer science ; Flash (photography) ; Sintering ; Operating system ; Materials science ; Metallurgy ; Algorithm Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Kazutaka Hayakawa | Isao Matsui | Takashi Hamaguchi | Takaharu Maeguchi
Source: Journal of the Japan Institute of Metals and Materials (article, 2024-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Algorithm for Prediction of Iron Loss of Electrical Steel under Stress Machine learning is a powerful tool that can predict the iron loss of electromagnetic steels with high accuracy. However, there is no theoretical explanation as to why machine learning can make such accurate predictions; it is a black box. Therefore, it is unclear how to select an algorithm for predicting iron loss behaviors among many machine learning algorithms available. In this study, we used approximately 10000 iron loss data for electromagnetic steels under stress and 19 machine learning algorithms to evaluate and compare the learning algorithms in terms of prediction accuracy, robustness, the number of data required, avoidance of overfitting, and agreement with experimental results. As a result, we conclude that LightGBM is the best algorithm for predicting the iron loss properties of electromagnetic steels. Although the discussion in this study has not yet led to a theoretical breakthrough in the field of machine learning, we hope that it will provide an effective guideline for selecting the learning algorithm in materials science. Stress (linguistics) ; Computer science ; Algorithm ; Machine learning ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Prakriti Kayastha | Erik Fransson | Paul Erhart | Lucy D. Whalley
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

Octahedral tilt-driven phase transitions in BaZrS$_3$ chalcogenide perovskite Chalcogenide perovskites are lead-free materials for potential photovoltaic or thermoelectric applications. BaZrS$_3$ is the most studied member of this family due to its superior thermal and chemical stability, desirable optoelectronic properties, and low thermal conductivity. Phase transitions of BaZrS$_3$ remain underexplored in the literature, as most experimental characterizations of this material have been performed at ambient conditions where the orthorhombic Pnma phase is reported to be stable. In this work, we study the dynamics of BaZrS$_3$ across a range of temperatures and pressures using an accurate machine-learning interatomic potential trained with data from hybrid density functional theory calculations. At 0 Pa, we find a first-order phase transition from the orthorhombic to tetragonal I4/mcm phase at 610 K, and a second-order transition from the tetragonal to the cubic Pm-3m phase at 880 K. The tetragonal phase is stable over a larger temperature range at higher pressures. To confirm the validity of our model we compare our results with a range of published experimental data and report a prediction for the X-ray diffraction pattern as a function of temperature. Chalcogenide ; Tilt (camera) ; Perovskite (structure) ; Materials science ; Octahedron ; Phase (matter) ; Phase transition ; Crystallography ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Crystal structure ; Geometry ; Mathematics Solid-state spectroscopy and crystallography ; Optical and Acousto-Optic Technologies ; Phase-change materials and chalcogenides
Authors: Samy Yousef | Justas Eimontas | Nerijus Striūgas | Marius Praspaliauskas | Mohammed Ali Abdelnaby
Source: Journal of Analytical and Applied Pyrolysis (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Pyrolysis behavior of non-textile components (buttons) and their kinetic analysis using artificial neural network Pyrolysis ; Textile ; Artificial neural network ; Kinetic energy ; Process engineering ; Materials science ; Pulp and paper industry ; Chemical engineering ; Waste management ; Biological system ; Chemistry ; Composite material ; Computer science ; Engineering ; Artificial intelligence ; Biology Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Flame retardant materials and properties
Authors: Carina T Cai | Amanda Parker | Amanda S. Barnard
Source: Journal of Physics Materials (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Graph representation of multi-dimensional materials Abstract The integration of graph-based representations with machine learning methodologies is transforming the landscape of material discovery, offering a flexible approach for modelling a variety of materials, from molecules and nanomaterials to expansive three-dimensional bulk materials. Nonetheless, the literature often lacks a systematic exploration from the perspective of material dimensionality. While it is important to design representations and algorithms that are universally applicable across species, it is intuitive for material scientists to align the underlying patterns between dimensionality and the characteristics of the employed graph descriptors. In this review, we provide an overview of the graph representations as inputs to machine learning models and navigate the recent applications, spanning the diverse range of material dimensions. This review highlights both persistent gaps and innovative solutions to these challenges, emphasising the pressing need for larger benchmark datasets and leveraging graphical patterns. As graph-based machine learning techniques evolve, they present a promising frontier for accurate, scalable, and interpretable material applications. Computer science ; Graph ; Curse of dimensionality ; Expansive ; Scalability ; Theoretical computer science ; Data science ; Benchmark (surveying) ; Representation (politics) ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Gabe Guo | Tristan Luca Saidi | Maxwell W. Terban | Valsecchi, Michele | Simon J. L. Billinge | Hod Lipson
Source: arXiv (Cornell University) (preprint, 2024-06-16)
Field: Materials Science — Materials Chemistry

Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known structures. The model factors both the measured diffraction pattern as well as relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-size broadened powder diffraction pattern, we find that our model, PXRDnet, can successfully solve simulated nanocrystals as small as 10 angstroms across 200 materials of varying symmetry and complexity, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with average error among these candidates being only 7% (as measured by post-Rietveld refinement R-factor). Furthermore, PXRDnet is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nano-materials. Nanocrystalline material ; Ab initio ; Diffusion ; Powder diffraction ; Materials science ; Diffraction ; Ab initio quantum chemistry methods ; Thermodynamics ; Crystallography ; Condensed matter physics ; Nanotechnology ; Chemistry ; Physics ; Optics ; Organic chemistry X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Crystal Structures and Properties