jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Biswajit Saha | Shyamal Pramanik | Arijit Das | Abhay Sankar Patra | Asim Kumar Mukherjee | Soumitra Maity
Source: Journal of Molecular Structure (article, 2023-12-17)
Field: Materials Science — Materials Chemistry

Molecular level structure development of Indian coal using experimental, ML and DFT techniques Chemistry ; Coal ; Computational chemistry ; Nanotechnology ; Organic chemistry X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Thermal and Kinetic Analysis
Authors: Collin J. Wilkinson | Anthony V. DeCeanne | Marc Dittmer | Christian Ritzberger | Markus Rampf | John C. Mauro
Source: Journal of the American Ceramic Society (article, 2023-02-01)
Field: Materials Science — Ceramics and Composites

Machine learning predictions of Knoop hardness in lithium disilicate glass‐ceramics Abstract Predicting the effects of ceramic microstructures on macroscopic properties, such as the Knoop hardness, has long been a difficult task. This is particularly true in glass–ceramics, where multiple unique crystalline phases can overlap with a background glassy phase. The combination of crystalline and glassy phases makes it difficult to quantify the percent crystallinity and to predict properties that are the result of the chemical composition and microstructure. To overcome this difficulty and take the first step to build a system for characterizing glass‐ceramics, we predict the Knoop hardness based on scanning electron microscopy images using two computational techniques. The first technique is a computer vision algorithm that allows for physical insights into the system because the features used in a predictive model are extracted from the images. The second technique is machine learning with convolutional neural networks that are trained through transfer learning, allowing for more accurate predictions than the first method but with the downside of being a black box. Discussion of the relative merits of the models is included. Knoop hardness test ; Lithium disilicate ; Materials science ; Ceramic ; Microstructure ; Crystallinity ; Glass-ceramic ; Convolutional neural network ; Artificial intelligence ; Black box ; Machine learning ; Mineralogy ; Computer science ; Composite material ; Indentation hardness ; Geology Glass properties and applications ; Metallic Glasses and Amorphous Alloys ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Shanshan Zheng | Wanqian Guo | Chao Li | Yongbin Sun | Qi Zhao | Hao Lü | Qishi Si | Huazhe Wang
Source: Environmental Research (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Application of machine learning and deep learning methods for hydrated electron rate constant prediction Constant (computer programming) ; Reaction rate constant ; Computer science ; Deep learning ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Spectroscopy and Quantum Chemical Studies
Authors: Zhengran Ji | Mike Hu | Huolin L. Xin
Source: Scientific Reports (article, 2023-08-29)
Field: Materials Science — Materials Chemistry

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn<sub>3</sub>O<sub>4</sub>, MnO, Mn<sub>2</sub>O<sub>3</sub>, and MnO<sub>2</sub>. The accurate decomposition of Mn<sub>3</sub>O<sub>4</sub> experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data. X-ray absorption spectroscopy ; Absorption spectroscopy ; Valence (chemistry) ; Oxidation state ; Spectral line ; Spectroscopy ; Electronic structure ; Ground state ; Offset (computer science) ; Chemistry ; Analytical Chemistry (journal) ; Computer science ; Materials science ; Physics ; Atomic physics ; Optics ; Computational chemistry Electronic and Structural Properties of Oxides ; Advancements in Battery Materials ; Electrochemical Analysis and Applications
Authors: Bipeng Wang | Ludwig Winkler | Yifan Wu | Klaus‐Robert Müller | Huziel E. Sauceda | Oleg V. Prezhdo
Source: The Journal of Physical Chemistry Letters (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Bidirectional Long Short-Term Memory Networks Essential for understanding far-from-equilibrium processes, nonadiabatic (NA) molecular dynamics (MD) requires expensive calculations of the excitation energies and NA couplings. Machine learning (ML) can simplify computation; however, the NA Hamiltonian requires complex ML models due to its intricate relationship to atomic geometry. Working directly in the time domain, we employ bidirectional long short-term memory networks (Bi-LSTM) to interpolate the Hamiltonian. Applying this multiscale approach to three metal-halide perovskite systems, we achieve two orders of magnitude computational savings compared to direct ab initio calculation. Reasonable charge trapping and recombination times are obtained with NA Hamiltonian sampling every half a picosecond. The Bi-LSTM-NAMD method outperforms earlier models and captures both slow and fast time scales. In combination with ML force fields, the methodology extends NAMD simulation times from picoseconds to nanoseconds, comparable to charge carrier lifetimes in many materials. Nanosecond sampling is particularly important in systems containing defects, boundaries, interfaces, etc. that can undergo slow rearrangements. Picosecond ; Nanosecond ; Hamiltonian (control theory) ; Molecular dynamics ; Computation ; Statistical physics ; Physics ; Computer science ; Algorithm ; Computational physics ; Quantum mechanics ; Mathematics ; Mathematical optimization ; Laser Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electronic and Structural Properties of Oxides
Authors: Irini Furxhi | Rossella Bengalli | Giulia Motta | Paride Mantecca | Özge Köse | Marie Carrière | Ehtsham Ul Haq | Charlie O’Mahony | Magda Blosi | Davide Gardini | Anna Luisa Costa
Source: ACS Applied Nano Materials (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs. Hazard ; Computer science ; Conceptualization ; Dimension (graph theory) ; Interoperability ; Probabilistic logic ; Sustainability ; Risk analysis (engineering) ; Bayesian network ; Data science ; Artificial intelligence ; Machine learning ; Data mining ; Mathematics ; Business ; Ecology Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Rajesh K. Raju | Saurabh Sivakumar | Xiaoxiao Wang | Zachary W. Ulissi
Source: Journal of Chemical Information and Modeling (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested. Nanoclusters ; Computer science ; Maxima and minima ; Evolutionary algorithm ; Genetic algorithm ; Cluster (spacecraft) ; Python (programming language) ; Algorithm ; Machine learning ; Artificial intelligence ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanowire Synthesis and Applications
Authors: Zhilong Wang | Jing Gao | Kehao Tao | Yanqiang Han | An Chen | Jinjin Li
Source: Energy storage materials (article, 2023-04-15)
Field: Materials Science — Materials Chemistry

IonML: A physically inspired machine learning platform to directed design superionic conductors Chemical space ; Electrical conductor ; Space (punctuation) ; Computer science ; Component (thermodynamics) ; Material Design ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Joseph C. R. Thacker | David J. Bray | Patrick B. Warren | R. L. Anderson
Source: The Journal of Physical Chemistry B (article, 2023-04-12)
Field: Chemistry — Organic Chemistry

Can Machine Learning Predict the Phase Behavior of Surfactants? We explore the prediction of surfactant phase behavior using state-of-the-art machine learning methods, using a data set for twenty-three nonionic surfactants. Most machine learning classifiers we tested are capable of filling in missing data in a partially complete data set. However, strong data bias and a lack of chemical space information generally lead to poorer results for entire de novo phase diagram prediction. Although some machine learning classifiers perform better than others, these observations are largely robust to the particular choice of algorithm. Finally, we explore how de novo phase diagram prediction can be improved by the inclusion of observations from state points sampled by an analogy to commonly used experimental protocols. Our results indicate what factors should be considered when preparing for machine learning prediction of surfactant phase behavior in future studies. Machine learning ; Artificial intelligence ; Computer science ; Set (abstract data type) ; Data set ; Phase (matter) ; Phase diagram ; Missing data ; Chemistry Surfactants and Colloidal Systems ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Elisa Roldán | Neil D. Reeves | Glen Cooper | Kirstie Andrews
Source: Frontiers in Bioengineering and Biotechnology (article, 2023-07-12)
Field: Materials Science — Biomaterials

Can we achieve biomimetic electrospun scaffolds with gelatin alone? <b>Introduction:</b> Gelatin is a natural polymer commonly used in biomedical applications in combination with other materials due to its high biocompatibility, biodegradability, and similarity to collagen, principal protein of the extracellular matrix (ECM). The aim of this study was to evaluate the suitability of gelatin as the sole material to manufacture tissue engineering scaffolds by electrospinning. <b>Methods:</b> Gelatin was electrospun in nine different concentrations onto a rotating collector and the resulting scaffold's mechanical properties, morphology and topography were assessed using mechanical testing, scanning electron microscopy and white light interferometry, respectively. After characterizing the scaffolds, the effects of the concentration of the solvents and crosslinking agent were statistically evaluated with multivariate analysis of variance and linear regressions. <b>Results:</b> Fiber diameter and inter-fiber separation increased significantly when the concentration of the solvents, acetic acid (HAc) and dimethyl sulfoxide (DMSO), increased. The roughness of the scaffolds decreased as the concentration of dimethyl sulfoxide increased. The mechanical properties were significantly affected by the DMSO concentration. Immersed crosslinked scaffolds did not degrade until day 28. The manufactured gelatin-based electrospun scaffolds presented comparable mechanical properties to many human tissues such as trabecular bone, gingiva, nasal periosteum, oesophagus and liver tissue. <b>Discussion:</b> This study revealed for the first time that biomimetic electrospun scaffolds with gelatin alone can be produced for a significant number of human tissues by appropriately setting up the levels of factors and their interactions. These findings also extend statistical relationships to a form that would be an excellent starting point for future research that could optimize factors and interactions using both traditional statistics and machine learning techniques to further develop specific human tissue. Gelatin ; Electrospinning ; Biocompatibility ; Tissue engineering ; Biomedical engineering ; Scaffold ; Dimethyl sulfoxide ; Materials science ; Extracellular matrix ; Chemical engineering ; Polymer ; Composite material ; Chemistry ; Biochemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Periodontal Regeneration and Treatments
Authors: Shixiang Zhao | Yu. V. Petrov | Yuyi Zhang | G. A. Volkov | Zejian Xu | Fenglei Huang
Source: International Journal of Engineering Science (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Modeling of the thermal softening of metals under impact loads and their temperature–time correspondence Materials science ; Strain rate ; Softening ; Constitutive equation ; Phenomenological model ; Adiabatic process ; Plasticity ; Atmospheric temperature range ; Deformation (meteorology) ; Stress relaxation ; Tungsten ; Thermodynamics ; Relaxation (psychology) ; Thermal ; Composite material ; Mechanics ; Metallurgy ; Creep ; Finite element method ; Physics ; Condensed matter physics High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Surface Treatment and Residual Stress
Authors: Jialin Tang | Guotai Li | Qi Wang | Jiongzhi Zheng | Lin Cheng | Ruiqiang Guo
Source: Physical Review Materials (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Competition between phonon-vacancy and four-phonon scattering in cubic boron arsenide by machine learning interatomic potential Point defects can strongly suppress the thermal conductivity $\ensuremath{\kappa}$ of solid materials, which is crucial for a broad range of applications, such as thermal management of electronic devices and thermoelectrics. Understanding thermal transport in materials containing point defects often relies on atomistic simulations based on density functional theory (DFT) or empirical potentials (EPs). However, modeling thermal transport in defective materials using DFT is very computationally expensive or even prohibitive due to the breaking of crystal symmetry while EPs suffer from low accuracy. Recently, machine learning has been applied to the development of interatomic potentials, offering opportunities to model defective systems accurately and efficiently. Here, we present a Gaussian approximation potential (GAP) developed for crystalline cubic boron arsenide (c-BAs) with vacancies, which can achieve DFT-level accuracy in predicting its $\ensuremath{\kappa}$ and phonon transport properties at four orders of magnitude reduced computational cost, especially for phonon-vacancy and four-phonon scatterings. Particularly, we applied the GAP to investigate the effect of vacancies on the $\ensuremath{\kappa}$ of c-BAs by considering both three-phonon and four-phonon scattering. Special attention was paid to the competition between phonon-vacancy and four-phonon scattering, which tend to decrease and increase the temperature dependence of $\ensuremath{\kappa}$, respectively. Specifically, when the vacancy concentration is much lower than $0.07%$ $(2.56\ifmmode\times\else\texttimes\fi{}{10}^{19}\phantom{\rule{4pt}{0ex}}{\mathrm{cm}}^{\ensuremath{-}3})$, four-phonon scattering plays stronger roles in determining the temperature dependence of $\ensuremath{\kappa}$. As the vacancy concentration increases to $0.07%$, the temperature dependence of $\ensuremath{\kappa}$ becomes close to that considering only three-phonon scattering, indicating the comparable effect of phonon-vacancy and four-phonon scattering. As the vacancy concentration further increases, the phonon-vacancy scattering becomes more dominant and pushes the $\ensuremath{\kappa}$ towards a temperature-independent behavior. Our work deepens the understanding of the phonon scattering landscape in c-BAs with vacancies and will be helpful for tailoring its thermal properties. Atomistic simulations combined with machine learning interatomic potentials are expected to be able to greatly advance the understanding of thermal transport in defective materials. Phonon ; Condensed matter physics ; Vacancy defect ; Materials science ; Phonon scattering ; Thermal conductivity ; Scattering ; Physics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices