jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nima Zoghipour | Ferhat Celik | Emre Taşcıoğlu | Yusuf Kaynak
Source: Procedia CIRP (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Development of an artificial neural network model for criticizing the burr formation during flat bottom drilling of CuZn38As brass alloy considering cutting tool geometry The approved laws and regulations on restricting the use of Lead in the products’ chemical composition, have made the industries to come up with development of low-lead brass alloys as an innovative solution during the last years. These alloys are the most predominantly utilized materials in pumping, drinking water industry. However, as alternatives for the conventional brass alloys, they contain lower machinability. Most of the components need to be aesthetic and compact. Therefore, the designed components have complex geometries. Flat bottom drilling is one of the new processes which is generally taken advantage in machining of these components. In this study, artificial neural networks (ANN) modelling, have been deployed to investigate the effects of the cutting tool geometries including axial rake, radial rake angles and edge radius as well as plunging angle to the work material focusing on the burr formation. A multilayer feed-forward ANN using error back-propagation training algorithm has been employed for this purpose. The results revealed possible reduction of burr formation on both the entry and exit sides of the low-lead brass alloy. Brass ; Machinability ; Rake angle ; Drilling ; Artificial neural network ; Machining ; Enhanced Data Rates for GSM Evolution ; Rake ; Mechanical engineering ; Alloy ; RADIUS ; Engineering ; Metallurgy ; Materials science ; Computer science ; Artificial intelligence Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Bauxite Residue and Utilization
Authors: Junhyeong Lee | Donggeun Park | Hugon Lee | Kundo Park | Seunghwa Ryu
Source:  (review, 2023-02-22)
Field: Materials Science — Materials Chemistry

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review In the last few decades, the influence of machine learning has permeated many areas of science and technology, including the field of material science.This toolkit of statistical methods accelerated the discovery and production of new materials by accurately predicting the complicated physical processes and mechanisms that are not fully described by existing material theories.However, the availability of a growing number of increasingly complex machine learning models confronts us with the question of "which machine learning algorithm to employ.".In this review, we provide a comprehensive review of common machine learning algorithms used for materials design, as well as a guideline for selecting the most appropriate model considering the nature of the design problem.To this end, we classify the material design problems into four categories of: (i) the training data set being sufficiently large to capture the trend of design space (interpolation problem), (ii) a vast design space that cannot be explored thoroughly with the initial training data set alone (extrapolation problem), (iii) multi-fidelity datasets (small accurate dataset and large approximate dataset), and (iv) only a small dataset available.The most successful machine learning-based surrogate models and design approaches will be discussed for each case along with pertinent literature.This review focuses mostly on the use of ML algorithms for the inverse design of complicated composite structures, a topic that has received a lot of attention recently with the rise of additive manufacturing. Inverse ; Space (punctuation) ; Industrial engineering ; Manufacturing engineering ; Computer science ; Engineering ; Engineering drawing ; Mathematics ; Geometry Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: Parham Valipoorsalimi | Yuksel Asli Sari | Mihriban Pekguleryuz
Source: arXiv (Cornell University) (preprint, 2023-05-20)
Field: Materials Science — Biomaterials

Mechanical Property Design of Bio-compatible Mg alloys using Machine-Learning Algorithms Magnesium alloys are attractive options for temporary bio-implants because of their biocompatibility, controlled corrosion rate, and similarity to natural bone in terms of stiffness and density. Nevertheless, their low mechanical strength hinders their use as cardiovascular stents and bone substitutes. While it is possible to engineer alloys with the desired mechanical strength, optimizing the mechanical properties of biocompatible magnesium alloys using conventional experimental methods is time-consuming and expensive. Therefore, Artificial Intelligence (AI) can be leveraged to streamline the alloy design process and reduce the required time. In this study, a machine learning model was developed to predict the yield strength (YS) of biocompatible magnesium alloys with an $R^2$ accuracy of 91\%. The predictive model was then validated using the CALPHAD technique and thermodynamics calculations. Next, the predictive model was employed as the fitness function of a genetic algorithm to optimize the alloy composition for high-strength biocompatible magnesium implants. As a result, two alloys were proposed and synthesized, exhibiting YS values of 108 and 113 MPa, respectively. These values were substantially higher than those of conventional magnesium biocompatible alloys and closer to the YS and compressive strength of natural bone. Finally, the synthesized alloys were subjected to microstructure analysis and mechanical property testing to validate and evaluate the performance of the proposed AI-based alloy design approach for creating alloys with specific properties suitable for diverse applications. Materials science ; Biocompatible material ; Alloy ; Biocompatibility ; Microstructure ; Magnesium ; Magnesium alloy ; Compressive strength ; Mechanical engineering ; Biomedical engineering ; Composite material ; Metallurgy ; Engineering Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Aluminum Alloys Composites Properties
Authors: Liwen Li | Huixian Liu | Yuyao Qin | Hua Wang | Jinyu Han | Xinli Zhu | Qingfeng Ge
Source: The Journal of Physical Chemistry C (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Tracking Atomic Diffusion in Surface and Bulk CuO via Neural Network-Based Molecular Dynamics Understanding the atomic diffusion process of a metal oxide helps with the design of more effective oxygen carriers for chemical looping with oxygen uncoupling (CLOU). Herein we investigated the atomic diffusion process in both slab and bulk CuO with varying oxygen vacancy (VO) under CLOU conditions using density functional theory (DFT) calculations and molecular dynamic (MD) simulation based on the neural network (NN) potential. Although the diffusions of both the Cu and the O atoms are important for the slab model, the O atoms’ outward diffusion to fill surface VO dominates the interlayer diffusion. In contrast, Cu atoms barely diffuse in the bulk model, and the diffusion process is dominated by O atoms through the VO. The MD-estimated chemical diffusion coefficients (10–9–10–13 m2/s) and activation barriers (1.14–1.34 eV) of both models are consistent with those experimentally measured values. The diffusion coefficient increases with increasing VO, and the values of the slab model are two orders of magnitude higher than those of the bulk model with a similar VO. These results indicate that the chemical diffusion is dominated by the surface instead of the bulk, and the diffusion in the bulk only becomes important when it becomes the frontier of oxygen uncoupling, i.e., grain boundary between CuO and Cu2O. The majority of diffusion species (O atom or vacancy) and process revealed in this study provide atomic insight into the diffusion in CuO-based materials. Diffusion ; Vacancy defect ; Chemical physics ; Grain boundary diffusion coefficient ; Molecular dynamics ; Effective diffusion coefficient ; Atomic diffusion ; Density functional theory ; Oxygen ; Atom (system on chip) ; Surface diffusion ; Chemistry ; Slab ; Diffusion process ; Materials science ; Grain boundary ; Physical chemistry ; Computational chemistry ; Thermodynamics ; Crystallography ; Microstructure ; Physics Copper-based nanomaterials and applications ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Dishant Beniwal | Pratik K. Ray
Source: Physical Review Materials (article, 2023-04-21)
Field: Materials Science — Materials Chemistry

CoSMoR: Decoding decision-making process along continuous composition pathways in machine learning models trained for material properties A key challenge in materials informatics is to decode the decision-making process of machine learning (ML) models that have been trained to predict material properties. The existing methods usually rank alloy features based on importance metrics and do not provide material-specific fundamental insights. Here, we present the Compositional Stimulus and Model Response (CoSMoR) framework that can be applied to any composition-based ML model (irrespective of the algorithm used) to calculate the exact contribution of each feature towards the manifestation of target material property along a continuous compositional pathway. CoSMoR utilizes the local partial dependencies of target property with respect to each feature and combines it with feature variations associated with discretized compositional variations to measure exact feature contributions. We showcase the importance of CoSMoR through implementation on phase-selection problem in multiprincipal element alloys (MPEAs), wherein it leads to physical insights into phase transitions. A detailed overview of the framework, along with the codes and step-by-step implementation of the algorithm, has been provided to enable extension to new or preexistent models. Discretization ; Computer science ; Feature (linguistics) ; Property (philosophy) ; Feature selection ; Artificial intelligence ; Process (computing) ; Materials science ; Algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrocatalysts for Energy Conversion
Authors: Philippe Jarry | N. Jakse
Source: IOP Conference Series Materials Science and Engineering (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification Abstract Ab initio molecular dynamic simulations of liquid Al93Cr7 and Al83Zn10Cr7 alloy have evidenced the presence of an icosahedral short range order (iSRO) which develops into icosahedral medium range order (iMRO) as the melt is undercooled. This atomic arrangement accounts for the presence of Dynamic Heterogeneities characterized by Al fast-dynamics regions and Cr-rich slow-dynamics regions. Characterisation of the medium range order was carried out by a direct connectivity approach. However, given the small size of the simulation (256 atoms), such characterisation remains partial. In order to better describe both iMRO formation and more dilute alloys closer to industrial compositions, a new modelling strategy has been initiated to allow in the long term for large-scale atomic-level simulations. Molecular Dynamics (MD) of million to billion atoms may indeed lead to meaningful results. Exploitation of such large amounts of MD-generated big data can be carried out by means of Machine Learning (ML) tools which provide relevant and powerful analysis methods. An unsupervised ML approach based on topological descriptors using persistent homology concepts is proposed to reveal the structural features of atomic arrangements without a priori knowledge on the studied system. This approach has been applied so far to pure Al melts. Both translational and orientational orderings are thus evidenced together with nucleation pathways, whose revealed features are beyond the hypotheses of the Classical Nucleation Theory. Icosahedral symmetry ; Nucleation ; Molecular dynamics ; Alloy ; Range (aeronautics) ; Short range order ; Statistical physics ; Atomic units ; Materials science ; Chemical physics ; Order (exchange) ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Crystallography ; Computational chemistry ; Metallurgy Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: John P. Stoppelman | Angus P. Wilkinson | Jesse G. McDaniel
Source: The Journal of Chemical Physics (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Equation of state predictions for ScF3 and CaZrF6 with neural network-driven molecular dynamics In silico property prediction based on density functional theory (DFT) is increasingly performed for crystalline materials. Whether quantitative agreement with experiment can be achieved with current methods is often an unresolved question, and may require detailed examination of physical effects such as electron correlation, reciprocal space sampling, phonon anharmonicity, and nuclear quantum effects (NQE), among others. In this work, we attempt first-principles equation of state prediction for the crystalline materials ScF3 and CaZrF6, which are known to exhibit negative thermal expansion (NTE) over a broad temperature range. We develop neural network (NN) potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and conduct direct molecular dynamics prediction of the equation(s) of state over a broad temperature/pressure range. The NN potentials serve as surrogates of the DFT Hamiltonian with enhanced computational efficiency allowing for simulations with larger supercells and inclusion of NQE utilizing path integral approaches. The conclusion of the study is mixed: while some equation of state behavior is predicted in semiquantitative agreement with experiment, the pressure-induced softening phenomenon observed for ScF3 is not captured in our simulations. We show that NQE have a moderate effect on NTE at low temperature but does not significantly contribute to equation of state predictions at increasing temperature. Overall, while the NN potentials are valuable for property prediction of these NTE (and related) materials, we infer that a higher level of electron correlation, beyond the generalized gradient approximation density functional employed here, is necessary for achieving quantitative agreement with experiment. Anharmonicity ; Hamiltonian (control theory) ; Molecular dynamics ; Statistical physics ; Negative thermal expansion ; Density functional theory ; Equation of state ; Chemistry ; Physics ; Thermodynamics ; Quantum mechanics ; Computational chemistry ; Mathematics ; Thermal expansion ; Mathematical optimization Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Martin Bäker
Source: Materials Research Express (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Understanding element solution energies in nickelbase alloys using machine learning Abstract The design of nickelbase superalloys requires to tune the content of different phases with a composition of Ni 3 A, either to strengthen the alloy ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mi>γ</mml:mi><mml:mo accent="false">′</mml:mo></mml:math> -phase, Ni 3 Al, γ ″-phase, Ni 3 Nb) or to influence its grain size and avoid embrittlement ( δ -phase, Ni 3 Nb, η -phase, Ni 3 Ti). Here, we use a machine-learning-inspired approach to understand the influence of elemental properties on the energy of an alloying element in the respective phases. It is shown that the energy in γ ″, δ , and η can be fitted well using the Bader charge and the volume of the element in a nickel matrix. In the case of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"><mml:mi>γ</mml:mi><mml:mo accent="false">′</mml:mo></mml:math> , the small lattice mismatch requires a fit not involving the volume, but the bond order instead. We also show that the frequently used M d -parameter can be predicted from the properties of an element in a pure nickel matrix. Finally, the physical basis of the results is discussed in detail. Element (criminal law) ; Materials science ; Artificial intelligence ; Computer science ; Political science Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; X-ray Diffraction in Crystallography
Authors: Pavel Sidorov | Nobuya Tsuji
Source: Chemistry - A European Journal (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models. Workflow ; Biochemical engineering ; Steric effects ; Computer science ; Catalysis ; Field (mathematics) ; Chemistry ; Nanotechnology ; Computational chemistry ; Materials science ; Mathematics ; Organic chemistry ; Engineering ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; History and advancements in chemistry
Authors: Zhuo Li | Changquan Zhao | Hai‐Kun Wang | Yanqing Ding | Yechao Chen | Philippe Schwaller | Ke Yang | Hua Cheng | Yulian He
Source: ChemRxiv (preprint, 2023-11-08)
Field: Materials Science — Materials Chemistry

Interpreting Chemisorption Strength with AutoML-based Feature Deletion Experiments The chemisorption energy of reactants on a catalyst surface, E_ads, is among the most informative characters of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining Eads. In response to this, the study proposes a novel methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining E_ads, compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention. Chemisorption ; Feature (linguistics) ; Periodic table ; Consistency (knowledge bases) ; Artificial neural network ; Computer science ; Chemical space ; Artificial intelligence ; Adsorption ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Physics ; Physical chemistry ; Bioinformatics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Eunjin Koh | Panithan Sriboriboon | Seunghun Kang | Choongseop Jeon | Seungyong Lee | Jinbok Shin | Chenxi Wang | Jeong-Ryeol Kim | Jungwon Lee | Donghoon Kim | Yunseok Kim
Source: Journal of Alloys and Compounds (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Understanding local physical properties of barium titanate ceramics depending on the synthesis conditions assisted by a machine learning algorithm Materials science ; Ceramic capacitor ; Barium titanate ; Dielectric ; Ceramic ; Piezoelectricity ; Nanoscopic scale ; Capacitor ; Surface roughness ; Nanotechnology ; Composite material ; Optoelectronics ; Voltage ; Electrical engineering Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Ludwig Schneider | Dylan J. Walsh | Bradley D. Olsen | Juan Pablo
Source: ChemRxiv (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Generative BigSMILES: An Extension for Polymer Informatics, Computer Simulations &amp; ML/AI The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is designed for generative workflows, and is complemented by tailored software tools for ease of use. This extension integrates additional data, including reactivity ratios (or connection probabilities among repeat units), molecular weight distributions, and ensemble size. An algorithm, interpretable as a generative graph is devised that utilizes these data, enabling molecule generation from defined polymer ensembles. Consequently, the G-BigSMILES notation allows for efficient specification of complex molecular ensembles via a streamlined line notation, thereby providing a foundational tool for automated polymeric materials design. In addition, the graph interpretation of the G-BigSMILES notation sets the stage for robust machine learning methods capable of encapsulating intricate polymeric ensembles. The combination of G-BigSMILES with advanced machine learning techniques will facilitate straightforward property determination and in-silico polymeric material synthesis automation. This integration has the potential to significantly accelerate materials design processes and advance the field of polymer science. Notation ; Computer science ; Workflow ; Generative grammar ; Graph ; Software ; Extension (predicate logic) ; Theoretical computer science ; Automation ; Artificial intelligence ; Programming language ; Engineering ; Database ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Business Process Modeling and Analysis