jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier
Source: ACS Applied Electronic Materials (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Machine Learning Surrogate Model for Acceleration of Ferroelectric Phase-Field Modeling Phase-field modeling is a powerful technique for predicting domain structure evolution and electromechanical properties of ferroelectric materials. However, it remains computationally very expensive, thus demanding high computing resources and restraining its use for exploring large systems. Some machine learning approaches have already been proposed to accelerate general phase-field simulations. Here, we present a specifically neural-network-trained model for ferroelectric phase-field modeling, including supervised and nonsupervised learning from Landau energy. A surrogate model predicts the microstructural polarization field evolution determining the electrostatic and mechanical equilibrium at each time step. The model produces accurate and stable rollout predictions, up to hundreds of frames even when starting from the beginning of the simulation. With a relative error contained below 5% compared to a high-fidelity phase field, we show that our model can be used instead of real solvers to conduct full simulations. While being at least 685 times faster than the classical phase-field computation, our approach opens a path to explore ferroelectric materials at a larger scale with fewer computing resources. Ferroelectricity ; Computer science ; Field (mathematics) ; Computation ; Artificial intelligence ; Polarization (electrochemistry) ; Artificial neural network ; Phase (matter) ; Machine learning ; Acceleration ; Phase field models ; Algorithm ; Statistical physics ; Physics ; Engineering ; Mathematics ; Dielectric ; Electrical engineering ; Classical mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Hakan Demir | Seda Keskın
Source: Macromolecular Materials and Engineering (article, 2023-09-21)
Field: Chemistry — Inorganic Chemistry

A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science Abstract Membrane‐based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks‐ (MOFs) are considered as next‐generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF‐based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF‐based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data‐driven approaches to extract useful structure–property relations of MOF‐based membranes and to produce design principles for the high‐performing MOF‐based membranes are discussed. Membrane ; Gas separation ; Metal-organic framework ; Materials science ; Nanotechnology ; Separation (statistics) ; Biochemical engineering ; Computer science ; Separation of concerns ; Field (mathematics) ; Process engineering ; Systems engineering ; Engineering ; Chemistry ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; MXene and MAX Phase Materials
Authors: Qi Lei | Lanyu Li | Honghao Chen | Xiaonan Wang
Source: Environmental Science & Technology (review, 2023-11-02)
Field: Materials Science — Materials Chemistry

Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical calculations, referred as SMART here, is an interdisciplinary approach that combines high-throughput calculation and data-driven modeling with expertise from chemical, materials, environmental, computer and data science and engineering, leading to the development of advanced capabilities in simulating and optimizing carbon capture processes. This perspective discusses the state-of-the-art material discovery research enabled by high-throughput calculation and data-driven modeling. Further, we propose a framework for material discovery, and illustrate the synergies among deep learning models, pretrained models, and comprehensive data sets, emerging as a robust framework for data-driven design and development in carbon capture. In essence, the adoption of the SMART approach promises a revolutionary impact on efforts in energy transition and industrial decarbonization. Scalability ; Throughput ; Computer science ; Data science ; Systems engineering ; Artificial intelligence ; Engineering ; Database Machine Learning in Materials Science ; CO2 Sequestration and Geologic Interactions ; Carbon Dioxide Capture Technologies
Authors: Ruihuan Cheng | Xingchen Shen | Stefan Klotz | Zezhu Zeng | Zehua Li | A. Ivanov | Yu Xiao | Li‐Dong Zhao | F. Weber | Yue Chen
Source: Physical review. B./Physical review. B (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Lattice dynamics and thermal transport of PbTe under high pressure Understanding the high-pressure lattice dynamics is crucial to modulate the thermal transport in thermoelectric materials beyond the ambient environment. Herein, using molecular dynamics simulations in combination with an accurate machine-learning interatomic potential, we find the well-known double-peak feature of the transverse-optical (TO) mode in PbTe gradually vanishes when pressure is enhanced. An anomalous nonmonotonic pressure dependence of the frequency of the transverse-acoustic phonon in PbTe is computationally reproduced. The longitudinal-acoustic, longitudinal-optical, and TO phonons harden as expected when pressure increases. The theoretical results are compared with inelastic neutron scattering experimental data. We have also calculated the pressure-dependent lattice thermal conductivity and revealed the phonon transport mechanisms. Phonon ; Condensed matter physics ; Transverse plane ; Inelastic neutron scattering ; Thermal conductivity ; Lattice (music) ; Thermal ; Materials science ; Ambient pressure ; Thermoelectric effect ; Molecular dynamics ; Scattering ; Phonon scattering ; Neutron scattering ; Physics ; Thermodynamics ; Optics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Ailing Tan | Yunxin Wang | Yong Zhao | Ya-Jie Zuo
Source: Infrared Physics & Technology (article, 2023-01-11)
Field: Chemistry — Analytical Chemistry

1D-inception-resnet for NIR quantitative analysis and its transferability between different spectrometers Spectrometer ; Computer science ; Near-infrared spectroscopy ; Transfer of learning ; Transferability ; Biological system ; Chemometrics ; Pattern recognition (psychology) ; Artificial neural network ; Artificial intelligence ; Deep learning ; Remote sensing ; Optics ; Machine learning ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Yang Zhong | Hongyu Yu | Xin-Gao Gong | Hongjun Xiang
Source: The Journal of Physical Chemistry Letters (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

A General Tensor Prediction Framework Based on Graph Neural Networks Graph neural networks (GNNs) have been shown to be extremely flexible and accurate in predicting the physical properties of molecules and crystals. However, traditional invariant GNNs are not compatible with directional properties, which currently limits their usage to the prediction of only invariant scalar properties. To address this issue, here we propose a general framework, i.e., an edge-based tensor prediction graph neural network, in which a tensor is expressed as the linear combination of the local spatial components projected on the edge directions of clusters with varying sizes. This tensor decomposition is rotationally equivariant and exactly satisfies the symmetry of the local structures. The accuracy and universality of our new framework are demonstrated by the successful prediction of various tensor properties from first to third order. The framework proposed in this work will enable GNNs to step into the broad field of prediction of directional properties. Universality (dynamical systems) ; Computer science ; Tensor (intrinsic definition) ; Artificial neural network ; Invariant (physics) ; Tensor decomposition ; Graph ; Theoretical computer science ; Mathematics ; Topology (electrical circuits) ; Artificial intelligence ; Algorithm ; Pure mathematics ; Physics ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kiarash Farajzadehahary | Xabier Telleria‐Allika | José M. Asúa | Nicholas Ballard
Source: Polymer Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An artificial neural network to predict reactivity ratios in radical copolymerization An artificial neural network model is described which is capable of predicting reactivity ratios in radical copolymerization solely based on monomer chemical structures. It outperforms classical approaches, enabling prediction without kinetic data. Copolymer ; Reactivity (psychology) ; Artificial neural network ; Monomer ; Computer science ; Chemistry ; Biological system ; Artificial intelligence ; Organic chemistry ; Polymer ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Jun Li | Kun Luo | Qi An
Source: Physical Review Letters (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Activating Mobile Dislocation in Boron Carbide at Room Temperature via Al Doping Dislocation glide, deformation twinning, and phase transition are critical mechanisms resulting in irreversible plastic deformations of materials. Because of the lack of dislocation movement, superhard ceramics generally exhibit brittle failure at room temperature. Here, by employing molecular dynamics simulations using a machine-learning force field, we reveal several plastic deformation mechanisms in superhard boron carbide as a small amount of aluminum (Al) is doped. Under shear deformation, dislocation nucleation and glide occur in Al-doped boron carbide (B_{12}-CAlC) due to the breakage of weakened chain bonds rather than the disintegration of icosahedral clusters. The dislocation activities then cause twin boundaries to migrate, thereby mitigating amorphization and enhancing ductility. Furthermore, the mobile dislocation with the Burgers vector of b=⟨11[over ¯]0⟩{111} is observed in the tensile nanopillar, which is well consistent with the experiment. This Letter demonstrates that mobile dislocation could be activated in superstrong covalent materials through a simple doping strategy. Materials science ; Dislocation ; Boron carbide ; Crystal twinning ; Nucleation ; Deformation (meteorology) ; Deformation mechanism ; Burgers vector ; Condensed matter physics ; Doping ; Composite material ; Microstructure ; Thermodynamics Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis ; Thermal properties of materials
Authors: Yasin Ahmed Waday | Ermias Girma Aklilu
Source: International Journal of Polymer Science (article, 2023-06-16)
Field: Materials Science — Biomaterials

ANN and RSM Modeling for the Synthesis of Avocado Seed Starch Combined Orange Peel Extract Antimicrobial Packaging Film Cooperation of essential oil into film formation results in active packaging materials, which can improve the quality and freshness of the foods and extend the shelf life. In the present study, extraction of starch and essential oil was performed. The active films were developed through the solvent casting method. The influence of avocado seed starch and orange peel essential oil was investigated and optimized using response surface methodology and an artificial neural network on the tensile strength, water vapor permeability, and antimicrobial properties of active films. The results showed that both models performed reasonably well, but trained artificial neural networks have more modeling capability rather than the response surface method. The optimum conditions were found to be orange peel oil of 0.57 g and 30% w/w of avocado seed starch with the values of the corresponding responses of 3.94 MPa, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mn>3.098</a:mn> <a:mo>×</a:mo> <a:msup> <a:mrow> <a:mn>10</a:mn> </a:mrow> <a:mrow> <a:mo>−</a:mo> <a:mn>10</a:mn> </a:mrow> </a:msup> </a:math> g/ms Pa, and 17.273 mm for tensile strength, water vapor permeability, and inhibition zone, respectively. Generally, the orange peel extract had an effective and promising alternative for the commercial production of antimicrobial packaging films. Ultimate tensile strength ; Orange (colour) ; Starch ; Materials science ; Antimicrobial ; Response surface methodology ; Food science ; Active packaging ; Shelf life ; Chemical engineering ; Composite material ; Chemistry ; Food packaging ; Organic chemistry ; Chromatography Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Phytochemicals and Antioxidant Activities
Authors: Joshua Ojih | Alejandro Rodriguez | Jianjun Hu | Ming Hu
Source: Energy and AI (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage. However, the characterization of these properties via high throughput screening at the quantum level, although highly accurate, is inefficient and very time- and resource-consuming. In contrast, prediction at the classical level is highly efficient but less accurate. We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level. Using 10,158 elastic constants as training data, we trained the models on 5 mechanical properties, namely bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and hardness. With the trained model, we predicted 775,947 data in search of materials with ultrahigh hardness. We further verify the recommended ultrahigh hardness materials by high precision first principles calculations, and we finally identify 20 structures with extreme hardness close to diamond, the hardest material in nature. Among those, two super hard materials are completely new and have not been reported in literature so far. We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity, and we verify the thermal conductivity of 338 structures with first principles. Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures (only 0.04%) in the large-scale materials pool. Materials science ; Thermal conductivity ; Bulk modulus ; Shear modulus ; Modulus ; Poisson's ratio ; Artificial neural network ; Scalability ; Statistical physics ; Computer science ; Composite material ; Machine learning ; Poisson distribution ; Mathematics ; Physics Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Thermal properties of materials
Authors: Yuqi Sun | Guanjie Wang | Kaiqi Li | Liyu Peng | Jian Zhou | Zhimei Sun
Source: ACS Applied Materials & Interfaces (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of Transition Metal Borides by Machine Learning on Small Data Sets Accurate and efficient prediction of the stability and structure-stability relationship is important to discover materials; however, it requires tremendous efforts via traditional trial-and-error schemes. Here, we presented a small-data set machine learning (ML) method to accelerate the discovery of promising ternary transition metal boride (MAB) candidates. Based on data sets obtained by ab initio calculations, we developed three robust neural networks to predict the decomposition energy (Δ<i>H</i><sub>d</sub>) and assess the thermodynamic stability of 212-typed MABs (M<sub>2</sub>AB<sub>2</sub>). The quantitative relation between Δ<i>H</i><sub>d</sub> and stability was unraveled by several composition-and-structure descriptors. Three hexagonal M<sub>2</sub>AB<sub>2</sub>, i.e., Nb<sub>2</sub>PB<sub>2</sub>, Nb<sub>2</sub>AsB<sub>2</sub>, and Zr<sub>2</sub>SB<sub>2</sub>, were discovered to be stable with negative Δ<i>H</i><sub>d</sub>, and 75 metastable MABs were identified with Δ<i>H</i><sub>d</sub> less than 70 meV/atom. Finally, the dynamical stability and mechanical properties of MABs were investigated by ab initio calculations, whose results further verified the reliability of our ML models. This work provided a ML approach on small data sets to accelerate the discovery of compounds and expanded the MAB phase family to V<sub>A</sub> and VI<sub>A</sub> groups. Stability (learning theory) ; Metastability ; Materials science ; Ternary operation ; Ab initio ; Atom (system on chip) ; Reliability (semiconductor) ; Ab initio quantum chemistry methods ; Boride ; Work (physics) ; Artificial neural network ; Thermodynamics ; Machine learning ; Computer science ; Physics ; Molecule ; Quantum mechanics MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Jiangtao Lv | Ruizhe Zhang | Qiongchan Gu | Md Hemayet Uddin | Xiaoxiao Jiang | Junqiao Qi | Guangyuan Si | Qingdong Ou
Source: Materials & Design (article, 2023-12-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces and their intelligent advances Metasurfaces exhibit excellent performance in the regulation of optical field parameters, such as phase, amplitude, and polarization, by using nanounit structures. The characteristics of metasurface miniaturization and its combination with advanced semiconductor processes have attracted significant attention, and their design and applications have become the focus of researchers recently. The design of traditional metasurfaces often requires professional designers. Consequently, design work has become difficult and time-consuming with the increase in the complexity of metasurface design. The introduction of artificial intelligence (AI) in metasurface design work has resulted in its rapid development and simplification. Furthermore, metasurfaces have begun to play the role of hidden layers in optical neural networks, and their intelligent properties are gradually being identified. Based on the introduction of the metasurface control mechanism, this paper reviews its intelligent designs and progress. Additionally, the feasibility, necessity, and broad prospects of the combination of AI with metasurfaces are clarified. Miniaturization ; Computer science ; Nanotechnology ; Materials science ; Systems engineering ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Memory and Neural Computing ; Advanced Wireless Communication Technologies
Authors: Xiaofei Cheng | Sara Hakem Al-Khafaji | Mohammad Hashemian | Mariem Ahmed | S. Ali Eftekhari | Ali Ihsan Alanssari | Nabaa Muhammad Diaa | Manal Morad Karim | Davood Toghraie | Ahmed Alawadi
Source: Engineering Applications of Artificial Intelligence (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Statistical analysis and Neural Network Modeling of functionally graded porous nanobeams vibration in an elastic medium by considering the surface effects Materials science ; Length scale ; Natural frequency ; Porosity ; Vibration ; Mechanics ; Material properties ; Dependency (UML) ; Porous medium ; Wavelength ; Composite material ; Physics ; Acoustics ; Computer science Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Guangyu Yang | Yanxiao Hu | Zhanjun Qiu | Bo-Lin Li | Ping Zhou | Dengfeng Li | Gang Zhang
Source: Applied Physics Letters (article, 2023-02-20)
Field: Materials Science — Materials Chemistry

Abnormal strain-dependent thermal conductivity in biphenylene monolayer using machine learning interatomic potential Applying tensile strain on an intrinsic lattice always results in the reduction in thermal conductivity due to the red-shift of phonon frequency and enhanced phonon anharmonicity. However, in this work, we explored an unexpected strain-enhanced thermal conductivity of a planar biphenylene network (BPN) in the frame of a Boltzmann transport equation combined with the machine learning interatomic potential. Under 5% biaxial tensile strain, the room temperature thermal conductivity of BPN reaches to about 4–5 times of that in an intrinsic sample. This phenomenon can be understood by considering a mirror symmetry induced phonon selection rule. This work highlights the significant effect of the selection rule on thermal transport and enriches the understanding of the thermal conductivity regulation in strained two-dimensional materials. Thermal conductivity ; Phonon ; Materials science ; Anharmonicity ; Condensed matter physics ; Thermal conduction ; Boltzmann equation ; Work (physics) ; Thermodynamics ; Composite material ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices