jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ikram Zaghouni | B. Ben Smida | Wahbi Mankai | Moez Chafra | Ridha Ben Cheikh
Source:  (article, 2024-10-31)
Field: Materials Science — Polymers and Plastics

Lifespan Prediction Using Deep Learning for a Transtibial Socket Made by Natural Fiber Reinforced Composite Material The primary goal of this study is to leverage artificial intelligence, specifically deep learning, to predict the lifespan of trans-tibial prosthetic sockets made from natural fiber (alfa). Prosthetic sockets are vital components that interface directly with the residual limb, and their durability is crucial for the comfort and functionality of the prosthesis. Traditional methods of predicting socket lifespan often rely on empirical data and manual assessment, which can be both time consuming and imprecise. To address this, our study integrates AI with experimental data, employing a deep learning model that analyzes angular deflection cycles, which represent cyclic stress patterns. This model incorporates design and material parameters, and is trained on a comprehensive dataset including deflection cycles, material responses, and failure data. By correlating angular deflection behavior with long term performance, our approach significantly enhances the accuracy of durability predictions. This method not only improves patient outcomes and prosthetic performance but also facilitates better design and manufacturing strategies, leading to more reliable and enduring prosthetic devices. Composite number ; Fiber ; Materials science ; Composite material ; Computer science ; Natural (archaeology) ; Fiber-reinforced composite ; Structural engineering ; Engineering ; Geology Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Rubens C. Souza | Júlio César Duarte | Ronaldo Ribeiro Goldschmidt | Itamar Borges
Source: ChemRxiv (preprint, 2024-12-02)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the most intense peak of the absorption spectra of organic molecules Accurate knowledge of electronic molecular properties of excited states is fundamental for understanding the behavior of functional materials for organic electronics and sensors. In this work, we focus on determining the properties of the most intense peak in the electronic absorption spectra of organic molecules. For this purpose, we employed the quantum chemistry QM-symex dataset, which has approximately 173,000 organic molecules and time-dependent DFT (TD-DFT) data of the first ten electronic absorption transitions. Each one is identified by its Cartesian coordinates. From data in the original QM-symex, we built a new dataset named QM-symex-modif that contains molecules in Simplified Molecular Input Line Entry System (SMILES) format and properties related to the main electronic transition. We then employed twenty machine learning (ML) algorithms to investigate oscillator strengths, excitation energies, transition orbitals, and the highest occupied molecular orbitals (HOMOs). As inputs for the ML algorithms, we used several chemical descriptors for each molecule generated in the RDKit tool employing the corresponding SMILES format. The generated input descriptors significantly improved the accuracy of the ML predictions for these key photophysical properties. Very good mean absolute errors (MAEs) were obtained for the test set composed of 45,056 molecules, namely, an MAE of 0.035 for oscillator strengths, 0.09 eV for excitation energies, 1.24 and 0.62 for the initial and final transition molecular orbital (MO) numbers (i.e., for each molecule, their position in the MO listing) respectively, and 0.014 for HOMO numbers, with R² values consistently exceeding 0.94, thus demonstrating the accuracy of the models. Additionally, a Shapley additive explanation (SHAP) analysis was carried out to evaluate the importance of the input parameters for the investigated ML models. We found several interesting relationships involving the input parameters. In particular, molecular weight holds significant importance in our ML models for determining the target HOMO numbers and the transition orbitals. Molecular orbital ; Atomic orbital ; Excited state ; Chemistry ; Molecule ; Absorption spectroscopy ; Spectral line ; Excitation ; Electronic structure ; Absorption (acoustics) ; Molecular electronic transition ; Atomic physics ; Computational chemistry ; Physics ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Analytical Chemistry and Sensors ; Various Chemistry Research Topics
Authors: Bengü Temizel | Elif Neyran Soylu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Biomaterials

Modeling of Diatom Indices (Bdı, Tdı and Gdı) Based on the Physico-Chemical Structure of the River Ecosystem with Machine Learning and Artificial Intelligence Methods; a Comparative Example Diatom ; Artificial intelligence ; Ecosystem ; River ecosystem ; Computer science ; Ecology ; Oceanography ; Geology ; Biology Diatoms and Algae Research
Authors: Paolo De Angelis | Giovanni Trezza | Giulio Barletta | Pietro Asinari | Eliodoro Chiavazzo
Source: arXiv (Cornell University) (preprint, 2024-11-15)
Field: Materials Science — Materials Chemistry

Energy-GNoME: A Living Database of Selected Materials for Energy Applications Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Learning (ML) and Deep Learning (DL) tools, our protocol mitigates cross-domain data bias using feature spaces to identify potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. Classifiers with both structural and compositional features identify domains of applicability, where we expect enhanced accuracy of the regressors. Such regressors are trained to predict key materials properties like, thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage ($ΔV_c$). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion. Database ; Computer science ; Energy (signal processing) ; World Wide Web ; Physics Advancements in Solid Oxide Fuel Cells ; Molten salt chemistry and electrochemical processes
Authors: Manzar Masud | Aamir Mubashar | Shahid Iqbal | Hassan Ejaz | Saad Abdul Raheem
Source:  (article, 2024-09-24)
Field: Materials Science — Polymers and Plastics

Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing and utilization of tree-based machine learning regression models. Low-velocity impact (LVI) testing was performed on five distinct stacking sequences of carbon/flax bio-HFRP at energies ranging from 15 J to 90 J. For all tests, peak impact force was recorded and compared. Symmetric configurations with a uniform distribution of flax layers across the composite laminate exhibited better impact performance. Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). The performance metrics used to assess and compare the efficiency were the coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). The most accurate model for the prediction of peak impact force was DT with the R2 training and test dataset values of 0.9920 and 0.9045, respectively. Furthermore, lower MSE and MAE values were attained using the DT model as compared to the RF model. The developed methodology and the model serve as powerful tools to predict the damage-induced properties of bio-HFRP composite laminates utilizing minimal resources and saving time as well. Composite material ; Composite number ; Materials science ; Composite laminates ; Carbon fiber reinforced polymer ; Fiber ; Tree (set theory) ; Polymer ; Mathematics Natural Fiber Reinforced Composites ; Structural Behavior of Reinforced Concrete ; Tribology and Wear Analysis
Authors: Mitrovic, Darko
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Combining Evolution and Physics through Machine Learning to Decipher Molecular Mechanisms From E.coli to elephants, the cells of all living organisms are surrounded by a near impenetrable wall of lipids. The windows through the walls are membrane proteins - receptors, transporters and channels that confer communication, information and metabolites through the membrane. Without opening holes in the membrane, it is necessary for these proteins to alter their shapes by cycling between conformational states to transport signals or molecules. Owing to their important role as information bottle-necks, changes in their function can lead to cancer, infectious diseases, or metabolic disorders. Hence, they are important targets for drug discovery, therapeutic research and understanding the human body. Due to the delicate thermodynamic balance of conformational states of these proteins that are modulated by external stimuli, it is difficult to trap them in experimental setups in which their native states are captured. To add to the problematic nature of their molecular mechanisms, they are too fast to kinetically trap in a certain state long enough to observe without breaking the molecular mechanism. Fast moving mechanisms makes them a good target for molecular dynamics (MD) simulations, where the movement of all atoms in the proteins is simulated over time. Although a powerful tool, modern MD simulations are not able to access long enough timescales to accurately measure macroscopic functionally relevant information, leaving a gap between simulations and reality in which many conclusions made with atomic resolution fail to translate into macroscopic phenomena, such as receptor activity, transport efficiency, mutational stability or allosteric signalling. This work presents novel methodology that efficiently discovers and explores functionally relevant conformational states using MD simulations. By combining evolutionary information with physics using machine learning, the methodology accelerates the sampling while retaining the details of the molecular mechanism and the thermodynamic information. Additionally, the work shows how the methodology is capable of bridging the gap in resolution between experiments and simulations through the in-silico measurement of macroscopic phenomena on a microscopic scale. Moreover, it uniquely presents a framework applied to 4 studies on different target proteins of different families in which conformational change occurs, and is able to independently relate them to different types of measurements. Molecular machine ; DECIPHER ; Function (biology) ; Molecular dynamics ; Allosteric regulation ; Conformational ensembles ; Binding affinities ; Living systems ; Affinities Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yuzhou Hao | Yuting Zuo | Jiongzhi Zheng | Wenjie Hou | Hong Gu | Xiaoying Wang | Xuejie Li | Jun Sun | Xiangdong Ding | Zhibin Gao
Source: arXiv (Cornell University) (preprint, 2024-05-20)
Field: Materials Science — Materials Chemistry

Machine learning for predicting ultralow thermal conductivity and high ZT in complex thermoelectric materials Efficient and precise calculations of thermal transport properties and figure of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material Tl$_9$SbTe$_6$ by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors like population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m$^{-1}$ K$^{-1}$ at room temperature, a result that closely correlates with experimental observations. Notably, we observe that the off-diagonal terms of heat flux operators play a significant role in shaping the overall lattice thermal conductivity of Tl$_9$SbTe$_6$, where the ultralow thermal conductivity resembles that of glass due to limited group velocities. Furthermore, we achieve a maximum $ZT$ value of 3.17 in the $c$-axis orientation for \textit{p}-type Tl$_9$SbTe$_6$ at 600 K, and an optimal $ZT$ value of 2.26 in the $a$-axis and $b$-axis direction for \textit{n}-type Tl$_9$SbTe$_6$ at 500 K. The crystalline Tl$_9$SbTe$_6$ not only showcases remarkable thermal insulation but also demonstrates impressive electrical properties owing to the dual-degeneracy phenomenon within its valence band. These results not only elucidate the underlying reasons for the exceptional thermoelectric performance of Tl$_9$SbTe$_6$ but also suggest potential avenues for further experimental exploration. Thermoelectric effect ; Thermal conductivity ; Materials science ; Thermoelectric materials ; Condensed matter physics ; Thermal ; Engineering physics ; Physics ; Thermodynamics ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Jiaxin Lin | Zhe Chen | Yinwu Li | Zhuofeng Ke
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mechanistic Investigation and Machine Learning Exploration of Structure-Activity Relationship of Nhc-Mn Catalyzed the Ester Hydrosilylation Reaction Hydrosilylation ; Catalysis ; Chemistry ; Organic chemistry ; Computer science ; Combinatorial chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Kanchu Srilatha
Source: International Journal of Advanced Chemistry Research (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum chemistry in the 21st century: Innovations and applications Quantum chemistry has experienced transformative advancements from 2010 to 2024, propelled by developments in quantum computing, machine learning, and computational methodologies. These innovations have enabled precise simulations of complex molecular systems, fostering significant progress in materials science, drug discovery, and energy research. The integration of quantum computing has introduced algorithms like the Variational Quantum Eigensolver (VQE), facilitating the calculation of molecular ground-state energies. Machine learning techniques have enhanced predictive modeling, optimizing chemical reactions and material properties. Additionally, the development of user-friendly quantum chemistry software has democratized access to sophisticated computational tools. Despite these advancements, challenges such as scalability, error mitigation in quantum computations, and the need for high-quality data persist. Addressing these issues is crucial for the continued evolution of quantum chemistry, promising transformative impacts on technology and society. Quantum chemistry ; Nanotechnology ; Chemistry ; Engineering physics ; Computer science ; Materials science ; Engineering ; Physical chemistry Various Chemistry Research Topics
Authors: Kevin Garala | Mihir Raval | Priya Patel | Bhupendra G. Prajapati | Bhupendra Prajapati | Abhay Dharamsi
Source: Current Drug Therapy (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Understanding Isoniazid Crystals Agglomeration Through Quality by Design and Artificial Neural Network Analysis Introduction: The aims of this study were to determine and govern critical sources of variability in the process and to investigate the effect of excipients and processing conditions on the critical quality attributes (CQAs) of isoniazid (INZ) crystal agglomerates using a quality-bydesign (QbD) paradigm along with risk-based approach. Methods: The QbD paradigm is thoroughly exercised to determine the CQAs and to, link these CQAs to agglomerate properties and to recognize potentially significant input variables. Considering this, a risk assessment tool was executed with different excipients and processing conditions to define their effect on CQAs of agglomerates. Potential risk factors were recognized using an Ishikawa diagram and then investigated using principal component analysis and an artificial neural network. After that, all agglomerates were optimized using the design of the experiment. Results: The optimized INZ crystal agglomerates were characterized using various tools such as X-ray diffractometry, Fourier transform infrared spectroscopy, scanning electron microscopy, differential scanning calorimetry, gas chromatography, and stability testing. These characterizations confirmed that INZ did not undergo any structural modifications in the agglomerates. Conclusion: The results indicate that the QbD principles and risk assessment tool provide a valuable means to fully understand directly compressible INZ crystal agglomerates, which could be considered an exceptional alternative to the granulation method. Economies of agglomeration ; Artificial neural network ; Quality (philosophy) ; Artificial intelligence ; Computer science ; Engineering ; Chemical engineering ; Physics Crystallization and Solubility Studies
Authors: Yuanzhi Sun | Guangbao Mi | Peijie Li | Liangju He
Source: Research Square (preprint, 2024-11-26)
Field: Materials Science — Materials Chemistry

Prediction of oxidation resistance and mechanism study of Ti-V-Cr burn resistant titanium alloy based on machine learning Mechanism (biology) ; Alloy ; Titanium alloy ; Resistance (ecology) ; Materials science ; Metallurgy ; Titanium ; Physics Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Mohsen Askarishahi | Sahar Karzegar | Mohammad Salmani | Mehran Iazdandost | Sara Jambarsang | Mohammad Miri
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Determination of Effective Molecular Descriptors in Prediction of Octanol-Water Partition Coefficient of Anionic Surfactants by Artificial Neural Network Partition coefficient ; Artificial neural network ; Partition (number theory) ; Octanol ; Chemistry ; Chromatography ; Artificial intelligence ; Biological system ; Computer science ; Mathematics ; Biology Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis
Authors: Anastasiia Tukmakova | Patrizio Graziosi
Source: arXiv (Cornell University) (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Machine learning unveils the materials physical properties driving thermoelectric generators efficiency: half-Heuslers case We report the machine learning (ML)-based approach allowing thermoelectric generator (TEG) efficiency evaluation directly from 5 parameters: 2 physical properties - carriers density and energy gap, and 3 engineering parameters - external load resistance, TEG hot side temperature and leg height. Then, we propose to use genetic algorithm to optimize the proposed parameters in a way to maximize TEG efficiency. To prepare data, physical properties of n- and p-type materials were computed by coupling Density Functional Theory to Boltzmann Transport, and used for Finite Elements simulations. TEG efficiency was evaluated from a finite elements model considering design, radiative heat loss, contacts, external load resistance and different combinations of materials, resulting in 5300 different scenarios. For ML model, physical properties and engineering parameters were used as input features, skipping transport coefficients, while TEG efficiency was a target. Model was built on gradient boosting algorithm, its performance was evaluated using the coefficient of determination that reached a value of 0.98 on test dataset. Features importance analysis revealed the most crucial features for Half-Heusler-based TEG efficiency: carriers density or Fermi level, indicating the predominant role of electrical conductivity and electronic part of electrical conductivity. Features that were less important, but able to increase model performance were: energy gap, lattice thermal conductivity, charge carrier relaxation time and carriers conductivity effective mass. Features showed no impact were: density of states effective mass, heat capacity, density, relative permittivity and leg width. The proposed approach can be applied for the identification of the most important physical properties and their optimal values, the optimization of TEG design and operation conditions in a way to maximize TEG efficiency. Thermoelectric effect ; Thermoelectric materials ; Materials science ; Engineering physics ; Mechanical engineering ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices
Authors: Atsuto Seko
Source: arXiv (Cornell University) (preprint, 2024-03-05)
Field: Materials Science — Materials Chemistry

Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polynomial MLPs, defined by polynomial rotational invariants, have been systematically developed for a wide range of elemental, alloy, and ionic systems. This study introduces a highly efficient and robust methodology for enumerating globally stable and metastable structures utilizing polynomial MLPs. To develop MLPs that are sufficiently robust for structure enumeration, an iterative process involving random structure searches and subsequent updates to the MLPs is employed. This methodology has been systematically applied to elemental systems such as As, Bi, Ga, In, La, P, Sb, Sn, and Te, where numerous local minima present significant competition with the global minimum in terms of energy. The proposed approach facilitates robust global structure searches and structure enumerations by markedly accelerating the search process and expanding the search space. Metastability ; Enumeration ; Polynomial ; Crystal (programming language) ; Crystal structure ; Mathematics ; Physics ; Computer science ; Crystallography ; Materials science ; Combinatorics ; Pure mathematics ; Chemistry ; Mathematical analysis ; Quantum mechanics ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure
Authors: Yidi Shen | Yufeng Huang | Qi An
Source: JOM (article, 2024-07-10)
Field: Materials Science — Biomaterials

Machine Learning-Driven Identification of Favorable Dopants for Activating Non-basal &lt;c + a&gt; Slip in Mg Alloys Slip (aerodynamics) ; Dopant ; Materials science ; Basal (medicine) ; Identification (biology) ; Metallurgy ; Physics ; Optoelectronics ; Thermodynamics ; Doping ; Biology ; Internal medicine ; Medicine Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Metal and Thin Film Mechanics