jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moses Olabhele Esangbedo | Bassey Okon Samuel
Source: Oxford Open Materials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture Abstract The rapid expansion of the air transport industry raises significant sustainability concerns due to its substantial carbon emissions and contribution to global climate change. These emissions are closely linked to fuel consumption, which in turn is influenced by the weight of materials used in aircraft systems. This study extensively applied machine learning tools for the optimization of natural fiber-reinforced composite material production parameters for aircraft body application. The Taguchi optimization technique was used to study the effect of sisal fibers, glass fibers, fiber length, and NaOH treatment concentration on the performance of the materials. Multi-objective optimization methods like the grey relational analysis and genetic algorithm (using the MATLAB programming interface) were employed to obtain the best combination of the studied factors for low fuel consumption (low carbon emission) and high-reliability structural applications of aircraft. The models developed from regressional analysis had high accuracy of prediction, with R-Square values all &amp;gt;80%. Optimization of the grey relational analysis of the developed composite using the genetic algorithm showed the best process parameter to achieve low weight material for aircraft application to be 40% sisal, 5% glass fiber at 35 mm fiber length, and 5% NaOH concentration with grey relational analysis at the highest possible level, which is unity. Taguchi methods ; Composite number ; Fiber ; Materials science ; Composite material ; Polymer ; Mechanical engineering ; Computer science ; Engineering Material Selection and Properties ; Quality and Management Systems ; Railway Systems and Materials Science
Authors: Dong Fan | Aydın Özcan | Pengbo Lyu | Guillaume Maurin
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unravelling abnormal in-plane stretchability of two-dimensional metal–organic frameworks by machine learning potential molecular dynamics Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed a highly stable NiF<sub>2</sub>(pyrazine)<sub>2</sub> 2D MOF <i>in silico</i> with a two-dimensional periodic wine-rack architecture. Extensive first-principles calculations and molecular dynamics (MD) simulations based on a newly developed machine learning potential (MLP) revealed that this 2D MOF exhibits huge in-plane Poisson's ratio anisotropy. This results in anomalous negative in-plane stretchability, as evidenced by an uncommon decrease in its in-plane area upon the application of uniaxial tensile strain, which makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications. We further demonstrated the unique capability of MLP to accurately predict the finite-temperature properties of MOFs on a large scale, exemplified by MLP-MD simulations with a dimension of 28.2 × 28.2 nm<sup>2</sup>, relevant to the length scale experimentally attainable for the fabrication of MOF films. Materials science ; Molecular dynamics ; Nanoscopic scale ; Fabrication ; Nanotechnology ; Anisotropy ; Plane (geometry) ; Metal-organic framework ; Chemical physics ; Biological system ; Chemistry ; Computational chemistry ; Optics ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Sungho Jeon | Hannah S. Nedzbala | Brittany L. Huffman | Adam J. Pearce | Carrie L. Donley | Xiaofan Jia | Gabriella P. Bein | Jihoon Choi | Nicolas Durand | Hala Atallah | Felix N. Castellano | Jillian L. Dempsey | James M. Mayer | Nilay Hazari | Eric A. Stach
Source: Matter (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Statistical analysis of HAADF-STEM images to determine the surface coverage and distribution of immobilized molecular complexes Surface (topology) ; Distribution (mathematics) ; Statistical analysis ; Materials science ; Nanotechnology ; Biological system ; Statistics ; Geometry ; Mathematics ; Biology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Field-Flow Fractionation Techniques
Authors: Eder G. Lomeli | Brandi Ransom | Akash Ramdas | Daniel Jost | Brian Moritz | Austin D. Sendek | Evan J. Reed | Thomas Devereaux
Source: ACS Applied Materials & Interfaces (article, 2024-09-15)
Field: Materials Science — Materials Chemistry

Predicting Reactivity and Passivation of Solid-State Battery Interfaces In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from <i>ab initio</i> molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interfaces for 67 different materials. Predicting the reactivity of solid-state interfaces with <i>ab initio</i> techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB<sub>13</sub>C<sub>2</sub> and LiB<sub>12</sub>PC. Materials science ; Passivation ; Reactivity (psychology) ; Battery (electricity) ; Anode ; Electrolyte ; Solid-state ; Work (physics) ; State (computer science) ; Metal ; Nanotechnology ; Engineering physics ; Computer science ; Thermodynamics ; Metallurgy ; Electrode ; Physical chemistry ; Power (physics) ; Layer (electronics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Sihao Yuan | Xu Han | Jun Zhang | Zhi‐Ping Xie | Fan Cheng | Yunlong Xiao | Yi Qin Gao | Yi Yang
Source: The Journal of Physical Chemistry A (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese. Molecular dynamics ; Chemical reaction ; Force field (fiction) ; Quantum chemical ; Computer science ; Computational chemistry ; Biological system ; Chemistry ; Transition state ; Chemical physics ; Molecule ; Artificial intelligence Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Yifeng Tian | Soumendu Bagchi | Liam Myhill | Giacomo Po | Enrique Martínez | Yen Ting Lin | Nithin Mathew | Danny Perez
Source: npj Computational Materials (article, 2024-09-14)
Field: Materials Science — Metals and Alloys

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals. Dislocation ; Computer science ; Artificial intelligence ; Data science ; Machine learning ; Physics ; Condensed matter physics Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Hanchao Zhang | Guoliang Ren | Peng Jia | Xiaofeng Zhao | Na Ni
Source: Ceramics International (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Development of machine learning force field for thermal conductivity analysis in MoAlB: Insights into anisotropic heat transfer mechanisms Anisotropy ; Thermal conductivity ; Phonon ; Materials science ; Molecular dynamics ; Ab initio ; Force field (fiction) ; Field (mathematics) ; Machining ; Work (physics) ; Condensed matter physics ; Heat flux ; Heat transfer ; Chemical physics ; Nanotechnology ; Artificial intelligence ; Thermodynamics ; Computer science ; Physics ; Composite material ; Computational chemistry ; Optics ; Chemistry Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: Zixin Zhuang | Amanda S. Barnard
Source: Journal of Cheminformatics (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Classification of battery compounds using structure-free Mendeleev encodings Machine learning is a valuable tool that can accelerate the discovery and design of materials occupying combinatorial chemical spaces. However, the prerequisite need for vast amounts of training data can be prohibitive when significant resources are needed to characterize or simulate candidate structures. Recent results have shown that structure-free encoding of complex materials, based entirely on chemical compositions, can overcome this impediment and perform well in unsupervised learning tasks. In this study, we extend this exploration to supervised classification, and show how structure-free encoding can accurately predict classes of material compounds for battery applications without time consuming measurement of bonding networks, lattices or densities. SCIENTIFIC CONTRIBUTION: The comprehensive evaluation of structure-free encodings of complex materials in classification tasks, including binary and multi-class separation, inclusive of three classifiers based on different logic function, is measured four metrics and learning curves. The encoding is applied to two data sets from computational and experimental sources, and the outcomes visualised using 5 approaches to confirms the suitability and superiority of Mendeleev encoding. These methods are general and accessible using source software, to provide simple, intuitive and interpretable materials informatics outcomes to accelerate materials design. Computer science ; Encoding (memory) ; Class (philosophy) ; Binary number ; Function (biology) ; Artificial intelligence ; Machine learning ; Simple (philosophy) ; Software ; Data mining ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Saurabh Tiwari | Prathamesh Satpute | Supriyo Ghosh
Source: Computational Materials Science (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Time series forecasting of multiphase microstructure evolution using deep learning Series (stratigraphy) ; Microstructure ; Artificial intelligence ; Computer science ; Materials science ; Statistical physics ; Geology ; Metallurgy ; Physics ; Paleontology Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Microstructure and Mechanical Properties of Steels
Authors: Rokyeon Kim | Young‐Woo Son
Source: Physical review. B./Physical review. B (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Transferable empirical pseudopotenials from machine learning Machine learning is used to generate empirical pseudopotentials that characterize the local screened interactions in the Kohn-Sham Hamiltonian. Our approach incorporates momentum-range-separated rotation-covariant descriptors to capture crystal symmetries as well as crucial directional information of bonds, thus realizing accurate descriptions of anisotropic solids. Trained empirical potentials are shown to be versatile and transferable such that the calculated energy bands and wave functions without cumbersome self-consistency reproduce conventional ab initio results even for semiconductors with defects, thus fostering faster and faithful data-driven material researches. Computer science ; Physics ; Nuclear physics Machine Learning in Materials Science ; Reservoir Engineering and Simulation Methods
Authors: Yaohui Xu | Yang Zhou | Yuting Li | Jiaqi Jia | Yitao Liu | Zhao Ding
Source: Journal of environmental chemical engineering (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Transition metal-engineered magnesium-based materials for advanced hydrogen storage: From multifaceted mechanisms to state-of-the-art systems Hydrogen storage ; Magnesium ; Materials science ; Nanotechnology ; Transition metal ; Metallurgy ; Chemistry Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shuai Li | Bin‐Bin Xie | Bo‐Wen Yin | L.H. Liu | Lin Shen | Wei‐Hai Fang
Source: The Journal of Physical Chemistry A (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets Machine learning is capable of effectively predicting the potential energies of molecules in the presence of high-quality data sets. Its application in the construction of ground- and excited-state potential energy surfaces is attractive to accelerate nonadiabatic molecular dynamics simulations of photochemical reactions. Because of the huge computational cost of excited-state electronic structure calculations, the construction of a high-quality data set becomes a bottleneck. In the present work, we first built two data sets. One was obtained from surface hopping dynamics simulations at the semiempirical OM2/MRCI level. Another was extracted from the dynamics trajectories at the CASSCF level, which was reported previously. The ground- and excited-state potential energy surfaces of ethylene-bridged azobenzene at the CASSCF computational level were constructed based on the former low-level data set. Although non-neural network machine learning methods can achieve good or modest performance during the training process, only neural network models provide reliable predictions on the latter external test data set. The BPNN and SchNet combined with the Δ-ML scheme and the force term in the loss functions are recommended for dynamics simulations. Then, we performed excited-state dynamics simulations of the photoisomerization of ethylene-bridged azobenzene on machine learning potential energy surfaces. Compared with the lifetimes of the first excited state (S<sub>1</sub>) estimated at different computational levels, our results on the <i>E</i> isomer are in good agreement with the high-level estimation. However, the overestimation of the <i>Z</i> isomer is unimproved. It suggests that smaller errors during the training process do not necessarily translate to more accurate predictions on high-level potential energies or better performance on nonadiabatic dynamics simulations, at least in the present case. Excited state ; Dynamics (music) ; Energy (signal processing) ; Computer science ; State (computer science) ; Potential energy ; Statistical physics ; Physics ; Algorithm ; Atomic physics ; Quantum mechanics ; Acoustics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Semiconductor Quantum Structures and Devices
Authors: Yicheng Li | Qian Liu | Tianhao Li | Hao Bi | Zhurui Shen
Source: Chinese Chemical Letters (review, 2024-11-28)
Field: Materials Science — Materials Chemistry

Recent achievements in rare earth modified metal oxides for environmental and energy applications: A review Rare earth ; Earth (classical element) ; Astrobiology ; Metal ; Energy (signal processing) ; Nanotechnology ; Environmental chemistry ; Engineering physics ; Environmental science ; Materials science ; Chemistry ; Engineering ; Physics ; Metallurgy ; Astronomy Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Cédric Bourgès | G. Lambard | Naoki Sato | Makoto Tachibana | Satoshi Ishii | Takao Mori
Source: Acta Materialia (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development The thermal process parameters are crucial in metal-sulfides ceramics as they affect significantly the resulting physico-chemical properties. In the present work, we investigated the sintering effect in the kesterite Cu 2.125 Zn 0.875 SnS 4 on its structural, microstructural, and thermoelectric (TE) properties to highlight the non-negligible contribution of the thermal process often ignored in metal-sulfide ceramics. For this purpose, we developed an approach combining data science with the conventional material experiment/theory approach which can be used as a tool to shortcut the time-consuming steps of TE material optimization. We confirmed that the optimization and control of the densification process is critical in unravelling the highest potential on metal sulfide TE ceramics with a non-negligible increase of its z T up to 60 %. We propose a scientific tool, the synergic combination of active machine learning with conventional chemistry/theory approaches, to either identify the most proficient sintering process as well as the process to avoid the degradation of the metal-sulfide ceramic properties and thus in a shorten number of experiments. This approach can be extended not only to other metal-sulfide ceramics for thermoelectricity but also to other research fields. Materials science ; Ceramic ; Sulfide ; Thermoelectric effect ; Kesterite ; Metallurgy ; Process (computing) ; Metal ; Thermoelectric materials ; Nanotechnology ; Composite material ; Computer science ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Phase-change materials and chalcogenides
Authors: Gaoyang Luo | Feicheng Huan | Yuwei Sun | Feng Shi | Shengwei Deng | Jianguo Wang
Source: Industrial & Engineering Chemistry Research (article, 2024-11-19)
Field: Materials Science — Polymers and Plastics

Machine Learning-Based High-Throughput Screening for High-Stability Polyimides High-stability polyimides exhibit tremendous potential for applications in flexible electronics, fibers, and membrane materials. However, screening polyimide structures with superior performance remains a significant challenge. In this study, we combined literature data, machine learning, and molecular dynamics simulations to identify key factors influencing the stability of polyimide structures and screen for high-stability polyimide candidates. Specifically, we utilized interpretable machine learning methods to analyze polyimide systems documented in the literature, aiming to identify crucial substructures that impact polyimide stability. This approach offers valuable insights for the development of high-stability polymers. By integrating diamine and dianhydride structures from both the PubChem database and the literature, we generated a data set containing over 15 million hypothetical polyimides. Using appropriate machine learning models, we conducted high-throughput screening to discover polyimides that simultaneously exhibit high thermal stability and excellent mechanical properties. The selected machine learning models demonstrated strong predictive capability in forecasting four key properties: glass transition temperature (Tg), Young’s modulus (Ym), tensile strength (Ts), and elongation at break (Eg). Based on the predictions from the optimal models and synthetic accessibility scores, we ultimately identified eight polyimide copolymer structures with outstanding stability, with some of their properties validated through all-atom molecular dynamics simulations. Throughput ; High-throughput screening ; Computer science ; Stability (learning theory) ; Process engineering ; Chemistry ; Machine learning ; Telecommunications ; Engineering Synthesis and properties of polymers ; Fuel Cells and Related Materials ; Tribology and Wear Analysis