jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hamid Ghasemi | Hessam Yazdani
Source: Computational Materials Science (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Atomistic simulation and machine learning predictions of mechanical response in nanotube-polymer composites considering filler morphology and aggregation Composite material ; Materials science ; Morphology (biology) ; Filler (materials) ; Nanotube ; Polymer ; Carbon nanotube Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Hirotoshi Hirai
Source: Scientific Reports (article, 2024-04-13)
Field: Materials Science — Materials Chemistry

Practical application of quantum neural network to materials informatics Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The "linear" entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI. Overfitting ; Computer science ; Generalization ; Autoencoder ; Artificial intelligence ; Machine learning ; Multivariate statistics ; Artificial neural network ; Construct (python library) ; Data mining ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Andrew Grieder | Kwangnam Kim | Liwen F. Wan | James Chapman | Brandon C. Wood | Nicole Adelstein
Source: The Journal of Physical Chemistry C (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li7La3Zr2O12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. We find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures. Non-equilibrium thermodynamics ; Thermal diffusivity ; Ionic bonding ; Molecular dynamics ; Chemical physics ; Statistical physics ; Dynamics (music) ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Phase Equilibria and Thermodynamics
Authors: Isaiah A. Moses | Chengyin Wu | Wesley F. Reinhart
Source: Materials Today Advances (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Crystal growth characterization of WSe2 thin film using machine learning Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has become an essential tool in materials science, including for materials characterization. In this study, we perform an in-depth analysis of the prediction of crystal coverage in WSe2 thin film atomic force microscopy (AFM) height maps with supervised regression and segmentation models. Regression models were trained from scratch and through transfer learning from a ResNet pretrained on ImageNet and MicroNet to predict monolayer crystal coverage. Models trained from scratch outperformed those using features extracted from pretrained models, but fine-tuning yielded the best performance, with an impressive 0.99 R2 value on a diverse set of held-out test micrographs. Notably, features extracted from MicroNet showed significantly better performance than those from ImageNet, but fine-tuning on ImageNet demonstrated the reverse. As the problem is natively a segmentation task, the segmentation models excelled in determining crystal coverage on image patches. However, when applied to full images rather than patches, the performance of segmentation models degraded considerably, while the regressors did not, suggesting that regression models may be more robust to scale and dimension changes compared to segmentation models. Our results demonstrate the efficacy of computer vision models for automating sample characterization in 2D materials while providing important practical considerations for their use in the development of chalcogenide thin films. Characterization (materials science) ; Materials science ; Crystal (programming language) ; Crystal growth ; Thin film ; Computer science ; Nanotechnology ; Crystallography ; Chemistry ; Programming language Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Luis Casillas‐Trujillo | Abhijith S. Parackal | Rickard Armiento | Björn Alling
Source: Physical Review Materials (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pretrained machine learning potentials The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of calculations. Recently, several pretrained universal machine learned interatomic potentials (UPMLIPs) have been published, i.e., potentials distributed with a single set of weights trained to target systems across a very wide range of chemistries and atomic arrangements. These potentials raise the hope of reducing the computational cost and methodological complexity of performing simulations compared to models that require for-purpose training. However, the application of these models needs critical evaluation to assess their usability across material types and properties. In this work, we investigate the application of the following UPMLIPs: MACE, CHGNET, and M3GNET to the context of alloy theory. We calculate the mixing enthalpies and volumes of 21 binary alloy systems and compare the results with DFT calculations to assess the performance of these potentials over different properties and types of materials. We find that the small relative energies necessary to correctly predict mixing energies are generally not reproduced by these methods with sufficient accuracy to describe correct mixing behaviors. However, the performance can be significantly improved by supplementing the training data with relevant training data. The potentials can also be used to partially accelerate these calculations by replacing the structural relaxation step. Published by the American Physical Society 2024 Materials science ; Mixing (physics) ; Machine learning ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Yuncheng Du | Cristina Sánchez‐González | Dongping Du
Source: Materials Today Communications (article, 2024-11-30)
Field: Chemistry — Inorganic Chemistry

Machine learning based insights for metal-organic frameworks synthesis: A comparative and explainable analysis of ZIF-8 morphology Materials science ; Metal-organic framework ; Morphology (biology) ; Nanotechnology ; Metal ; Chemical engineering ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhengxin Chen | Hange Wang | Bowen Liu | Hairui Zhou | Yingjie Cao | Yanan Wang | Peng Lin | Xiaolin Liu | Jia Lin | Xianfeng Chen | Jiang Wu
Source: Physical review. B./Physical review. B (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Cationic perturbation strategy to solve the information drought in material explainable machine learning In the field of materials research, machine learning (ML) techniques have emerged as indispensable tools. However, the opaqueness in decision making by models can compromise the trustworthiness of results, underscoring the crucial need for model interpretability. Explainable machine learning (XML) strives to augment researchers' comprehension of material properties and performance. Yet, reliance on high-quality datasets and scarcity of prior knowledge pose challenges for XML research, particularly when dealing with smaller datasets. In this study, using spinel as a representative example, we successfully addressed the data challenges in XML through a cationic perturbation strategy. We demonstrate an effective approach for handling information scarcity in small datasets, thus offering a feasible method for material research and broadening the scope of XML applications in materials science. Furthermore, our investigation successfully uncovered potential causal relationships underlying material properties and validated their consistency with physical cognition. These causal relationships can serve as experimental guides, facilitating the design and optimization of new materials. Consequently, this research holds significant scientific merit in advancing XML in the realm of materials science, while providing profound insights into material properties and fostering the development of reliable ML-based materials research. Perturbation (astronomy) ; Cationic polymerization ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Yuankai Zhao | Roger J. Mulder | Daniel J. Eyckens | Shadi Houshyar | Tu C. Le
Source: Polymer Chemistry (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Advancing antimicrobial polymer development: a novel database and accelerated design <i>via</i> machine learning Design principles for antimicrobial polymers using machine learning. Antimicrobial ; Computer science ; Polymer ; Chemistry ; Organic chemistry Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: John Isaac Enriquez | Harry Handoko Halim | Takahiro Yamasaki | M. Michiuchi | Kouji Inagaki | Masaaki Geshi | Ikutaro Hamada | Yoshitada Morikawa
Source: Carbon (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Origin of the surface facet dependence in the thermal degradation of the diamond (111) and (100) surfaces in vacuum investigated by machine learning molecular dynamics simulations We perform machine learning molecular dynamics simulations to gain an atomic-level understanding of the dependence of the graphitization and thermal degradation behavior of diamond to the (111) and (100) surface facets. The interatomic potential is constructed using graph neural network model, trained using energies and forces from spin-polarized van der Walls-corrected density functional theory calculations. Our results show that the C(111) surface is more susceptible to thermal degradation, which occurs from 2850 K through synchronized bilayer exfoliation mechanism. In comparison, the C(100) surface thermally degrade from a higher temperature of 3680 K through the formation of sp1 carbon chains and amorphous sp2-sp3 carbon network. Due to the dangling bonds at the step edges, the stepped surfaces are more susceptible to thermal degradation compared to the corresponding flat surfaces, with the stepped C(111) and C(100) surfaces thermally degrading from 1810 K and 3070 K, respectively. We propose potential applications of this study in diamond tool wear suppression, diamond polishing, and production of graphene directly from the diamond surface. Diamond ; Dangling bond ; Materials science ; Molecular dynamics ; Thermal ; Graphene ; Facet (psychology) ; Chemical physics ; Carbon fibers ; Degradation (telecommunications) ; Surface (topology) ; Nanotechnology ; Composite material ; Silicon ; Computational chemistry ; Chemistry ; Thermodynamics ; Composite number ; Optoelectronics ; Physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Machine Learning in Materials Science
Authors: Hongyu Yu | Boyu Liu | Yang Zhong | Liangliang Hong | Junyi Ji | Changsong Xu | Xingao Gong | Hongjun Xiang
Source: Physical review. B./Physical review. B (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Physics-informed time-reversal equivariant neural network potential for magnetic materials Magnetic potential energy surface is crucial for understanding magnetic materials. This study introduces a time-reversal $E(3)$-equivariant neural network and physics-informed SpinGNN++ framework for constructing interatomic potentials for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments. SpinGNN++ integrates multitask spin equivariant neural network with explicit spin-lattice terms and time-reversal equivariant neural network to learn high-order spin-lattice interactions using time-reversal $E(3)$-equivariant convolutions. A complex magnetic model data set is introduced as a benchmark and employed to demonstrate its capabilities. SpinGNN++ provides accurate descriptions of the complex spin-lattice coupling in monolayer ${\mathrm{CrI}}_{3}$ and ${\mathrm{CrTe}}_{2}$, achieving sub-meV errors and facilitates large-scale parallel spin-lattice dynamics, thereby enabling the exploration of associated properties, including magnetic ground state and phase transition. Remarkably, SpinGNN++ identifies a differentferrimagnetic state as the ground state for monolayer ${\mathrm{CrTe}}_{2}$, thereby enriching its phase diagram and providing deeper insights into the distinct magnetic signals observed in various experiments. Equivariant map ; Biology ; Artificial neural network ; Cognitive science ; Neuroscience ; Physics ; Computer science ; Artificial intelligence ; Psychology ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Non-Destructive Testing Techniques
Authors: Guanjian Cheng | Xin-Gao Gong | Wan‐Jian Yin
Source: Science Bulletin (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

An approach for full space inverse materials design by combining universal machine learning potential, universal property model, and optimization algorithm Inverse ; Algorithm ; Modulus ; Computer science ; Bulk modulus ; Material properties ; Mechanical engineering ; Materials science ; Mathematics ; Engineering ; Composite material ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ferroelectric and Negative Capacitance Devices
Authors: Shuo Wang | Wu Yan | Qian Xu | Yanman Ma | Tianzhu Wang
Source: Materials Today Communications (article, 2024-07-09)
Field: Materials Science — Biomaterials

Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning Formability ; Materials science ; Stamping ; Magnesium alloy ; Blank ; Deep drawing ; Hot stamping ; Metallurgy ; Composite material ; Alloy Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Rasool Mohsenzadeh | Behzad Hashemi Soudmand | Amirhossein Najafi | Fawwaz Hazzazi | Mehdi Fattahi
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites. Materials science ; Composite material ; Dispersion (optics) ; Polymer ; Nanocomposite ; Nanoparticle ; Polymer nanocomposite ; Particle (ecology) ; Segmentation ; Nanotechnology ; Computer science ; Artificial intelligence ; Optics Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Polymer crystallization and properties
Authors: Chengcheng Liu | Hang Su
Source: Materials Today Communications (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Machine learning aided prediction of martensite transformation temperature of NiTi-based shape memory alloy Materials science ; Shape-memory alloy ; Nickel titanium ; Martensite ; Metallurgy ; Transformation (genetics) ; Alloy ; Diffusionless transformation ; Composite material ; Microstructure Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience
Authors: Yanxia Wang | Yanjuan Sun | Xinyan Liu | Fan Dong
Source: PNAS Nexus (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Predicting and understanding photocatalytic CO2 reduction reaction with IR spectroscopy-based interpretable machine learning framework The highly selective conversion of carbon dioxide into value-added products is extremely valuable. However, even with the aid of in situ characterization techniques, it remains challenging to directly correlate extensive spectral data carrying microscopic information with macroscopic performance. Herein, we adopted advanced machine learning (ML) approaches to establish an accurate and interpretable relationship between vibrational spectral signals and catalytic performances to uncover hidden physical insights. Focusing on photocatalytic CO<sub>2</sub> reduction, our model is shown to effectively and accurately predict the CO production activity and selectivity based solely on the infrared (IR) spectral signals, the generalizability of which is additionally demonstrated with a new Bi<sub>5</sub>O<sub>7</sub>I photocatalytic system. More importantly, further model analysis has revealed a novel strategy to steer CO selectivity, the physical sanity of which is verified by a detailed reaction mechanism analysis. This work demonstrates the tremendous potential of machine-learned spectroscopy to efficiently identify reaction control factors, which can further lay the foundation for targeted optimization and reverse design. Photocatalysis ; Generalizability theory ; Selectivity ; Computer science ; Artificial intelligence ; Materials science ; Biological system ; Reduction (mathematics) ; Spectroscopy ; Machine learning ; Catalysis ; Process engineering ; Nanotechnology ; Chemistry ; Mathematics ; Engineering ; Physics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Yafeng Ji | Shimin Ma | Peng Wen | Zhihui Cai
Source: Tribology International (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Research on alloy composition-process-wear properties of medium manganese steel based on machine learning Manganese ; Metallurgy ; Materials science ; Alloy ; Process (computing) ; Composition (language) ; Computer science Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming