jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Can Leng | Zhuo Tang | Yi‐Ge Zhou | Zean Tian | Wei‐Qing Huang | Jie Liu | Keqin Li | Kenli Li
Source: Engineering (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Fifth Paradigm in Science: A Case Study of an Intelligence-Driven Material Design Science is entering a new era—the fifth paradigm—that is being heralded as the main character of knowledge integrating into different fields to intelligence-driven work in the computational community based on the omnipresence of machine learning systems. Here, we vividly illuminate the nature of the fifth paradigm by a typical platform case specifically designed for catalytic materials constructed on the Tianhe-1 supercomputer system, aiming to promote the cultivation of the fifth paradigm in other fields. This fifth paradigm platform mainly encompasses automatic model construction (raw data extraction), automatic fingerprint construction (neural network feature selection), and repeated iterations concatenated by the interdisciplinary knowledge (“volcano plot”). Along with the dissection is the performance evaluation of the architecture implemented in iterations. Through the discussion, the intelligence-driven platform of the fifth paradigm can greatly simplify and improve the extremely cumbersome and challenging work in the research, and realize the mutual feedback between numerical calculations and machine learning by compensating for the lack of samples in machine learning and replacing some numerical calculations caused by insufficient computing resources to accelerate the exploration process. It remains a challenging of the synergy of interdisciplinary experts and the dramatic rise in demand for on-the-fly data in data-driven disciplines. We believe that a glimpse of the fifth paradigm platform can pave the way for its application in other fields. Paradigm shift ; Computer science ; Process (computing) ; Artificial intelligence ; Big data ; Data science ; Architecture ; Raw data ; Computational intelligence ; Data mining Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Guanjie Wang | Yuqi Sun | Jian Zhou | Zhimei Sun
Source: The Journal of Physical Chemistry C (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

PotentialMind: Graph Convolutional Machine Learning Potential for Sb–Te Binary Compounds of Multiple Stoichiometries Machine learning potential (MLP) has emerged as a powerful tool in materials research and design. However, most MLP methods rely only on a single descriptor generated by mathematical functions instead of mapping the three-dimensional space of the materials structure, and thus this type of potential is typically limited to specific compositions. In this research, we present graph convolutional machine learning potential (GCMLP) software, termed PotentialMind, which can transform three-dimensional atomic structures into vectors comprising nodes, edges, and weights based on multiple descriptors. Using Sb–Te phase change materials as examples, a model named GCMLP-ST suitable for 12 stoichiometries of Sb–Te compounds has been constructed, whose root-mean-square errors for energy and forces are, respectively, 4.51 and 73.13 meV/Å for training data sets and are, respectively, 4.97 and 76.25 meV/Å for unfamiliar testing data sets. Moreover, for the energy-volume curves and radius distribution function by molecular dynamics, the GCMLP-ST model with 10,000 atoms exhibits good agreement with the ab initio molecular dynamics (AIMD) results across crystalline, liquid, and amorphous phases for the six representative Sb–Te material systems, which also exhibit 50 times the computational efficiency of AIMD. With this framework, the architecture of the machine learning model can be customized by deep and transfer learning, extending to other material systems. In addition, benefiting from the high efficiency of PotentialMind molecular dynamics (PMMD), it can be used for real devices, spanning tens of nanoseconds and comprising millions of atoms under different programming conditions that are impossible with AIMD simulations. Molecular dynamics ; Computer science ; Binary number ; Stoichiometry ; Amorphous solid ; Graph ; Materials science ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Mathematics ; Computational chemistry ; Physical chemistry ; Crystallography Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides
Authors: Timothy Liao | Weiyi Xia | Masahiro Sakurai | Renhai Wang | Chao Zhang | Huaijun Sun | Kai‐Ming Ho | Cai‐Zhuang Wang | James R. Chelikowsky
Source: Physical Review Materials (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Magnetic iron-cobalt silicides discovered using machine-learning We employ machine-learning (ML) combined with first principles calculations to discover different rare-earth-free magnetic iron-cobalt silicide compounds. Deep machine-learning models are used to provide rapid screening of over 350 000 hypothetical structures to select a small fraction of promising structures and compositions for further studies by first-principles calculations. An adaptive genetic algorithm is used to search for lower energy structures based on the promising chemical compositions. Such a ML-guided approach dramatically accelerates the pace of materials discovery. We discover four new ternary Fe-Co-Si compounds, which exhibit desirable properties such as a large magnetic polarization $({J}_{s}&gt;1.0\phantom{\rule{0.28em}{0ex}}\mathrm{T})$, a significant easy-axis magnetic anisotropy $({K}_{1}\ensuremath{\ge}1.0\phantom{\rule{0.28em}{0ex}}\mathrm{MJ}/{\mathrm{m}}^{3})$, and a high Curie temperature $({T}_{\mathrm{C}}&gt;840\phantom{\rule{0.28em}{0ex}}\mathrm{K})$. Moreover, the formation energies of these compounds are all within 70 meV/atom relative to the ternary convex hull, offering the possibility of synthesis. Materials science ; Ternary operation ; Curie temperature ; Energy (signal processing) ; Cobalt ; Atom (system on chip) ; Crystallography ; Machine learning ; Condensed matter physics ; Ferromagnetism ; Physics ; Metallurgy ; Computer science ; Chemistry Machine Learning in Materials Science ; Iron-based superconductors research ; Ferroelectric and Negative Capacitance Devices
Authors: Lukas Pielsticker | Rachel Nicholls | Serena DeBeer | Mark Greiner
Source: Analytica Chimica Acta (article, 2023-05-27)
Field: Materials Science — Surfaces, Coatings and Films

Convolutional neural network framework for the automated analysis of transition metal X-ray photoelectron spectra X-ray photoelectron spectroscopy ; Artificial neural network ; Usability ; Convolutional neural network ; Chemistry ; Spectral line ; Artificial intelligence ; Sample (material) ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Nuclear magnetic resonance ; Human–computer interaction ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Anthony Onwuli | Ashish V. Hegde | Kevin V. T. Nguyen | Keith T. Butler | Aron Walsh
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Element similarity in high-dimensional materials representations Elements can be represented as vectors in a high-dimensional chemical space. We explore the distance and correlation between these vectors for different machine learning models. Similarity (geometry) ; Element (criminal law) ; Space (punctuation) ; High dimensional ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Dazi Li | Yi Ru | Jun Liu
Source: Materials Today Communications (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

GATBoost: Mining graph attention networks-based important substructures of polymers for a better property prediction Polymer ; Property (philosophy) ; Materials science ; Graph ; Predictive modelling ; Artificial neural network ; Computer science ; Machine learning ; Artificial intelligence ; Biological system ; Data mining ; Algorithm ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Lagnajit Pattanaik | Angiras Menon | Volker Settels | Kevin Spiekermann | Zipei Tan | Florence H. Vermeire | Frederik Sandfort | Philipp Eiden | William H. Green
Source: The Journal of Physical Chemistry B (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations. To help address this challenge, we generate a large data set of solution free energies for nearly 44,000 solutes with almost 9 million conformers calculated in 41 different solvents using density functional theory and COSMO-RS and quantify the impact of solute conformers on the solution free energy. We then train a message passing neural network to predict the relative solution free energies of a set of solute conformers, enabling the identification of a small subset of thermodynamically relevant conformers. The model offers substantial computational time savings with predictions usually substantially within 1 kcal/mol of the free energy of the solution calculated by using computational chemical methods. Conformational isomerism ; Gibbs free energy ; Thermodynamics ; Ab initio ; Computational chemistry ; Chemistry ; Basis set ; Statistical physics ; Density functional theory ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Prajakta Prabhune | Yigitcan Comlek | Abhishek Shandilya | Ravishankar Sundararaman | Linda S. Schadler | L. Catherine Brinson | Wei Chen
Source: Nanomaterials (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Design of Polymer Nanodielectrics for Capacitive Energy Storage Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson's equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics. Permittivity ; Materials science ; Capacitive sensing ; Capacitor ; Dielectric ; Space charge ; Composite material ; Mechanics ; Computer science ; Optoelectronics ; Voltage ; Physics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Alan M. Lewis | Paolo Lazzaroni | Mariana Rossi
Source: The Journal of Chemical Physics (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Predicting the electronic density response of condensed-phase systems to electric field perturbations We present a local and transferable machine-learning approach capable of predicting the real-space density response of both molecules and periodic systems to homogeneous electric fields. The new method, Symmetry-Adapted Learning of Three-dimensional Electron Responses (SALTER), builds on the symmetry-adapted Gaussian process regression symmetry-adapted learning of three-dimensional electron densities framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water, and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10% with barely more than 100 training structures. Derived polarizability tensors and even Raman spectra further derived from these tensors show good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. Thus, this method is capable of predicting vector fields in a chemical context and serves as a landmark for further developments. Polarizability ; Symmetry (geometry) ; Context (archaeology) ; Electric field ; Tensor (intrinsic definition) ; Molecule ; Molecular physics ; Statistical physics ; Chemistry ; Chemical physics ; Physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Vahid Nasir | Laurence R. Schimleck | Farshid Abdoli | Maria Rashidi | Farrokh Sassani | Stavros Avramidis
Source: Polymers (article, 2023-10-19)
Field: Chemistry — Organic Chemistry

Quality Control of Thermally Modified Western Hemlock Wood Using Near-Infrared Spectroscopy and Explainable Machine Learning The quality control of thermally modified wood and identifying heat treatment intensity using nondestructive testing methods are critical tasks. This study used near-infrared (NIR) spectroscopy and machine learning modeling to classify thermally modified wood. NIR spectra were collected from the surfaces of untreated and thermally treated (at 170 °C, 212 °C, and 230 °C) western hemlock samples. An explainable machine learning approach was practiced using a TreeNet gradient boosting machine. No dimensionality reduction was performed to better explain the feature ranking results obtained from the model and provide insight into the critical wavelengths contributing to the performance of classification models. NIR spectra in the ranges of 1100-2500 nm, 1400-2500 nm, and 1700-2500 nm were fed into the TreeNet model, which resulted in classification accuracy values (test data) of 94.35%, 89.29%, and 84.52%, respectively. Feature ranking analysis revealed that when using the range of 1100-2500 nm, the changes in wood color resulted in the highest variation in NIR reflectance amongst treatments. As a result, associated features were given higher importance by TreeNet. Limiting the wavelength range increased the significance of features related to water or wood chemistry; however, these predictive models were not as accurate as the one benefiting from the impact of wood color change on the NIR spectra. The developed framework could be applied to different applications in which NIR spectra are used for wood characterization and quality control to provide improved insights into selected NIR wavelengths when developing a machine learning model. Near-infrared spectroscopy ; Ranking (information retrieval) ; Artificial intelligence ; Spectroscopy ; Machine learning ; Materials science ; Feature (linguistics) ; Boosting (machine learning) ; Computer science ; Analytical Chemistry (journal) ; Environmental science ; Pattern recognition (psychology) ; Remote sensing ; Optics ; Chemistry ; Geology ; Physics ; Chromatography Wood and Agarwood Research ; Wood Treatment and Properties ; Cultural Heritage Materials Analysis
Authors: Leilei Gu | Yaojun He | Hongzhan Liu | Zhongchao Wei | Jianping Guo
Source: Optical Materials (article, 2023-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface meta-atoms design based on DNN and LightGBM algorithms Inverse ; Algorithm ; Artificial neural network ; Computer science ; Transmittance ; Inverse problem ; Boosting (machine learning) ; Generalization ; Gradient boosting ; Zoom ; Transmission (telecommunications) ; Artificial intelligence ; Optics ; Mathematics ; Physics ; Geometry ; Telecommunications ; Random forest Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Indoor and Outdoor Localization Technologies
Authors: Yanheng Xu | Shuqian Ye | Xi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

The ScholarNet and Artificial Intelligence (AI) Supervisor in Material Science Research The past decade has witnessed extensive applications of artificial intelligence (AI) and robotics in chemistry and material science. However, the current focus mainly revolves around idea execution, neglecting the significance of idea generation, which plays a pivotal role in determining research novelty and potential breakthroughs. Concurrently, the exponential growth of scientific publications has resulted in overpublishing, making it challenging for researchers to grasp multiple fields effectively. As most opportunities for innovation lie in interdisciplinary realms, there is a risk of missing out on the development of new ideas. To address these challenges, we present a deep learning-based AI supervisor trained on correlation-based ScholarNet data of publications. Primarily tailored for material science, this AI supervisor excels in recommending research ideas, analyzing their novelty, and providing comprehensive guidance to researchers. By offering invaluable support in idea generation and novelty assessment, our AI supervisor has emerged as a promising digital infrastructure for future material science research. Novelty ; Supervisor ; Artificial intelligence ; Computer science ; GRASP ; Data science ; Robotics ; Engineering ethics ; Engineering ; Robot ; Psychology ; Political science ; Software engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Dominik Dold | Derek Aranguren van Egmond
Source: Cell Reports Physical Science (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Differentiable graph-structured models for inverse design of lattice materials Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspiration toward their design is in the irregular micro-architectures of nature. However, the immense design variability unlocked by such irregularity is challenging to probe analytically. Here, we propose a new computational approach using graph-based representation for regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, allowing automatic differentiation with surrogate derivatives to adjust geometric structure and local attributes of individual lattice elements to achieve inversely designed materials with desired properties. We further introduce a graph neural network surrogate model for structural analysis at scale. The methodology is generalizable to any system representable as heterogeneous graphs. Differentiable function ; Computer science ; Theoretical computer science ; Lattice (music) ; Graph ; Personalization ; Inverse ; Algorithm ; Mathematics ; Geometry Machine Learning in Materials Science ; Material Selection and Properties ; Polymer composites and self-healing
Authors: Chunxian Guo | Hongjian Zhang | Yilin Wang | Yunfa Jia | Lu Qi | Yakun Zhu | Haitao Cui
Source: Composite Structures (article, 2023-06-10)
Field: Materials Science — Polymers and Plastics

Parametric modeling of 2.5D woven composites based on computer vision feature extraction Parametric statistics ; Segmentation ; Artificial intelligence ; Feature extraction ; Feature (linguistics) ; Stiffness ; Computer science ; Parametric model ; Pattern recognition (psychology) ; Finite element method ; Voxel ; Artificial neural network ; Woven fabric ; Materials science ; Structural engineering ; Mathematics ; Composite material ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Mechanical Behavior of Composites
Authors: Edoardo Donadoni | Giulia Frigerio | Paulo Siani | Stefano Motta | Jacopo Vertemara | Luca De Gioia | Laura Bonati | Cristiana Di Valentin
Source: ACS Biomaterials Science & Engineering (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Molecular Dynamics for the Optimal Design of Functionalized Nanodevices to Target Folate Receptors on Tumor Cells Atomistic details on the mechanism of targeting activity by biomedical nanodevices of specific receptors are still scarce in the literature, where mostly ligand/receptor pairs are modeled. Here, we use atomistic molecular dynamics (MD) simulations, free energy calculations, and machine learning approaches on the case study of spherical TiO<sub>2</sub> nanoparticles (NPs) functionalized with folic acid (FA) as the targeting ligand of the folate receptor (FR). We consider different FA densities on the surface and different anchoring approaches, i.e., direct covalent bonding of FA γ-carboxylate or through polyethylene glycol spacers. By molecular docking, we first identify the lowest energy conformation of one FA inside the FR binding pocket from the X-ray crystal structure, which becomes the starting point of classical MD simulations in a realistic physiological environment. We estimate the binding free energy to be compared with the existing experimental data. Then, we increase complexity and go from the isolated FA to a nanosystem decorated with several FAs. Within the simulation time framework, we confirm the stability of the ligand-receptor interaction, even in the presence of the NP (with or without a spacer), and no significant modification of the protein secondary structure is observed. Our study highlights the crucial role played by the spacer, FA protonation state, and density, which are parameters that can be controlled during the nanodevice preparation step. Molecular dynamics ; Folate receptor ; Ligand (biochemistry) ; Nanodevice ; Docking (animal) ; Protonation ; Materials science ; Covalent bond ; Receptor ; Nanoparticle ; Nanotechnology ; Biophysics ; Chemistry ; Combinatorial chemistry ; Computational chemistry ; Biochemistry ; Organic chemistry ; Biology Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Nanoparticle-Based Drug Delivery