jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Avery Thompson
Source: Scilight (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Using machine learning to explore the full potential of porous materials Advanced tools can help sift through nearly endless combinations to find materials with desired properties. Porosity ; Scale-invariant feature transform ; Computer science ; Artificial intelligence ; Materials science ; Process engineering ; Engineering ; Composite material ; Feature extraction Pickering emulsions and particle stabilization ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Pan-dong Lin | Jun-feng Nie | Wen-dong Cui | Lei He | Shu-gang Cui | Guochao Gu | Gui‐yong Xiao | Yu‐peng Lu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Metals and Alloys

Understanding Stacking Fault Tetrahedron Formation in Irradiated F321 Stainless Steel: A Fusion of Transmission Electron Microscopy, Molecular Dynamics, and Machine Learning Tetrahedron ; Stacking fault ; Transmission electron microscopy ; Fusion ; Stacking ; Materials science ; Fault (geology) ; Molecular dynamics ; Electron microscope ; Irradiation ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Optics ; Nuclear physics ; Geology ; Nuclear magnetic resonance ; Seismology ; Computational chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Fusion materials and technologies
Authors: Jiali Lei | J. ZhangZhou | Earth Data Group ZJU
Source:  (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Machine learning classification on the chemical compositions of lithospheric diamonds and their inclusions Geology ; Lithosphere ; Paleontology ; Tectonics Diamond and Carbon-based Materials Research ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: Julien Steffen | Amin Alibakhshi
Source: ChemRxiv (preprint, 2024-09-20)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion on Ni(100): A Combined Machine-Learning, Ring Polymer Molecular Dynamics, and Kinetic Monte Carlo Study Kinetic Monte Carlo ; Monte Carlo method ; Kinetic energy ; Diffusion ; Polymer ; Molecular dynamics ; Hydrogen ; Dynamic Monte Carlo method ; Statistical physics ; Ring (chemistry) ; Chemical physics ; Hydrogen bond ; Materials science ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics ; Molecule ; Organic chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science
Authors: Lei Shen | Ziduo Yang | Xian Wang | Yifan Li | Qiujie Lv | Calvin Yu‐Chian Chen
Source: Research Square (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Lightweight equivariant model for efficient interatomic potentialpredictions Equivariant map ; Physics ; Statistical physics ; Computer science ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Limin Zeng | Ang Gao
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

Exploring the Premelting Transition through Molecular Simulations Powered by Neural Network Potentials The system has addressed the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract. Premelting ; Artificial neural network ; Transition (genetics) ; Molecular dynamics ; Statistical physics ; Physics ; Computer science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Data from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;div&gt;AbstractPurpose:&lt;p&gt;We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.&lt;/p&gt;Experimental Design:&lt;p&gt;We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [&lt;i&gt;n&lt;/i&gt; = 40; subset-1 (&lt;i&gt;n&lt;/i&gt; = 31) and subset-2 (&lt;i&gt;n&lt;/i&gt; =9)] and prospectively enrolled cases [prospective validation set (&lt;i&gt;n&lt;/i&gt; = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed.&lt;/p&gt;Results:&lt;p&gt;For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95).&lt;/p&gt;Conclusions:&lt;p&gt;A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.&lt;/p&gt;&lt;/div&gt; Segmentation ; Test set ; Medicine ; Posterior fossa ; Nuclear medicine ; Receiver operating characteristic ; Internal medicine ; Artificial intelligence ; Radiology ; Computer science Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Advanced Electron Microscopy Techniques and Applications
Authors: Akbarjon Baymirzaev | Begijon Tojiboev | Abdujalol Bektemirov | Nodirbek Madaminov | Sayyora Atakhonova | Firyuza Arabbayeva
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The Application of Artificial Intelligence and Machine Learning Technologies in Materials Science Applications of artificial intelligence ; Expert system ; Artificial neural network ; Key (lock) ; Field (mathematics) Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Machine Learning and ELM
Authors: Irvin Séchepée | Paul Paulain | Yuka Nagasaki | Riku Tanaka | Hiroaki Matsumoto | Vincent Velay
Source: Journal of the Japan Institute of Metals and Materials (article, 2024-11-24)
Field: Materials Science — Materials Chemistry

Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti-6Al-2Sn-4Zr-2Mo-Si Alloy Having Duplex α+α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning This research focuses on the systematic study of a Ti-6Al-2Sn-4Zr-2Mo-Si titanium alloy and the characterization of α+β (equiaxed and bimodal) and α+α′ (duplex) microstructures. It provides more insights on the outstanding advantages of the duplex (α+α′) microstructure, especially on its exceptional work hardening and strength-ductility balance. The heat treatment conditions required to form equiaxed, bimodal and duplex microstructures and their effects on the grain size and the phase proportion are discussed. It shows how the microstructural parameters can be controlled thanks to the heat treatment temperatures, the holding times and possible aging processes. The influence of such microstructural factors on the tensile properties of each alloy is investigated, especially on strength (proof stress, ultimate tensile strength), ductility (plastic elongation) and work hardening properties. The duplex (α+α′) microstructure is compared with the equiaxed and bimodal microstructures and its advantages are displayed, highlighting the better strength-ductility balance and superior work hardening properties of the duplex microstructure. Indeed, the deformed microstructure of the duplex (α+α′) microstructure reveals more homogeneous strain partitioning than that of the bimodal (α+β) microstructure. Thus, this work proved the potential of an optimized duplex (α+α′) microstructure for the enhanced tensile properties at room temperature. Finally, a machine learning model using gradient boosting regression trees is used to quantify the importance of the microstructural factors (type of microstructure, grain size and phase ratio) on the mechanical properties. Materials science ; Microstructure ; Ultimate tensile strength ; Alloy ; Duplex (building) ; Ductility (Earth science) ; Titanium alloy ; Metallurgy ; Composite material Titanium Alloys Microstructure and Properties ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties
Authors: Anoush Poursartip | G. Fernlund | K. Gordnian | O. Fernlund
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Enhanced Material Models for Composites Process Simulation Process (computing) ; Composite material ; Materials science ; Computer science ; Process engineering ; Engineering ; Programming language Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Vikram Sudarshan | Warren D. Seider
Source: arXiv (Cornell University) (preprint, 2024-08-31)
Field: Materials Science — Materials Chemistry

Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations. Benchmark (surveying) ; Rare events ; Event (particle physics) ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Geography ; Statistics ; Physics ; Cartography Machine Learning in Materials Science
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Data from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;div&gt;AbstractPurpose:&lt;p&gt;We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.&lt;/p&gt;Experimental Design:&lt;p&gt;We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [&lt;i&gt;n&lt;/i&gt; = 40; subset-1 (&lt;i&gt;n&lt;/i&gt; = 31) and subset-2 (&lt;i&gt;n&lt;/i&gt; =9)] and prospectively enrolled cases [prospective validation set (&lt;i&gt;n&lt;/i&gt; = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed.&lt;/p&gt;Results:&lt;p&gt;For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95).&lt;/p&gt;Conclusions:&lt;p&gt;A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.&lt;/p&gt;&lt;/div&gt; Segmentation ; Test set ; Medicine ; Posterior fossa ; Receiver operating characteristic ; Nuclear medicine ; Internal medicine ; Artificial intelligence ; Radiology ; Computer science Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Advanced Electron Microscopy Techniques and Applications
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: ChemRxiv (preprint, 2024-11-12)
Field: Materials Science — Surfaces, Coatings and Films

Statistical Analysis of HAADF-STEM Images to Determine the Surface Coverage and Distribution of Immobilized Molecular Complexes The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system. Surface (topology) ; Distribution (mathematics) ; Statistical analysis ; Materials science ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Computer science ; Statistics ; Artificial intelligence ; Mathematics ; Geometry ; Mathematical analysis ; Biology Electron and X-Ray Spectroscopy Techniques ; Field-Flow Fractionation Techniques
Authors: Yubing Qian | Xiang Li | Zhe Li | Weiluo Ren | Ji Chen
Source: arXiv (Cornell University) (preprint, 2024-06-30)
Field: Materials Science — Materials Chemistry

Deep learning quantum Monte Carlo for solids Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefunction, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initial designed and tested on small molecules, focusing on comparisons with other state-of-the-art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various of other methodological developments. Applications on computing energy, electron density, electric polarization, force and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potentials and existing challenges of deep learning QMC in materials chemistry and condensed matter physics. Monte Carlo method ; Quantum Monte Carlo ; Statistical physics ; Monte Carlo molecular modeling ; Monte Carlo method in statistical physics ; Computer science ; Hybrid Monte Carlo ; Physics ; Markov chain Monte Carlo ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Takuya Seki | Yudai Shinozaki | Ryosuke Ito | Shunsuke Sato | Jun Takeya | Toshihiro Okamoto | Go Watanabe
Source: ChemRxiv (preprint, 2024-05-30)
Field: Materials Science — Materials Chemistry

WITHDRAWN A proposed machine learning model can determine with high accuracy which of two types of two-dimensional molecular packing, herringbone or brickwork, an organic semiconductor forms. The combination of molecular simulations with the machine learning model has the potential to predict the crystal structure of organic molecules. Organic semiconductor ; Brickwork ; Crystal (programming language) ; Semiconductor ; Materials science ; Artificial intelligence ; Crystal structure prediction ; Molecule ; Organic molecules ; Computer science ; Crystal structure ; Machine learning ; Crystallography ; Chemistry ; Optoelectronics ; Engineering ; Organic chemistry ; Structural engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Ankita Morankar | Siddharth Deshpande | Gaurav Deshmukh | Pushkar Ghanekar | Zhenhua Zeng | Jeffrey Greeley
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Chemistry — Electrochemistry

(Invited) First Principles Treatments of Heterogeneous Electrocatalysis – Reactivity Trends and Electrocatalyst Structure Advances in the theoretical understanding of electrochemical systems have, over the past decade, led to growing use of periodic Density Functional Theory studies to treat a surprisingly large ensemble of electrocatalytic reactions, ranging from carbon dioxide electroreduction to oxygen evolution. Many such studies have employed simplified models of the electrochemical environment to determine reactivity trends across a broad space of catalytic materials, while other efforts have focused on developing detailed descriptions of electrochemical phenomena, such as the structure of electrochemical double layers, on model catalyst structures. An emerging challenge is to combine these approaches to ultimately enable theoretical design of electrocatalysts for reactions of significantly expanded chemical and materials complexity. In this talk, I will begin by discussing how we have applied strategies from computational heterogeneous catalysis to design enhanced electrocatalysts for the classic oxygen reduction reaction. In such treatments, we have effected highly detailed analyses of the catalyst surface structure and have employed machine learning-based methods to describe interactions between the various ORR reaction intermediates that are present at significant surface coverages. I will then explore challenges in obtaining more detailed descriptions of the electrocatalytic reaction environment, including the structure of catalysts with solid/solid interfaces, the distribution of charges in electrochemical double layers, and the structure and entropy of solvents near the electrocatalyst surface. I will conclude with some perspectives on how these complexities may be incorporated into traditional computational catalyst screening approaches to identify improved materials for more complex electrocatalytic systems. Electrocatalyst ; Chemistry ; Electrode ; Electrochemistry ; Physical chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Fei Shuang | Yucheng Ji | Luca Laurenti | Poulumi Dey
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Size-Dependent Strength Superiority in Multi-Principal Element Alloys Versus Constituent Metals: Insights from Machine Learning Potential and Embedded-Atom Model Element (criminal law) ; Atom (system on chip) ; Principal (computer security) ; Principal component analysis ; Materials science ; Artificial intelligence ; Computer science ; Political science ; Parallel computing Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Michell Marufu | Johanna Schwartz | Jayvic C. Jimenez
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Studying-Polymers-on-a-Chip (SPOC): Automated, High-Throughput Screening of Increased Ionic Conductivity Polymer Electrolytes. Advancements of solid polymer electrolytes are critical for the next generation of lithium-ion batteries within our highly energy-dependent society. Research techniques that speed up materials discovery are crucial to meet this need. Towards this end we have invented a Studying-Polymers-On-a-Chip (SPOC) platform that integrates active mixing direct ink-write 3D printing, in-situ impedance characterization and machine learning experimental planning systems. Collectively, this technology enables automated high-throughput screening of increased ionic conductivity polymer electrolytes. We report a threefold increase in ionic conductivity of polyethylene glycol (PEG) based electrolytes with the addition of silica-based fillers. We have begun work on tailoring this platform to screen Anion Exchange Membranes (AEMs) and their degradation behavior. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Polymer electrolytes ; Polymer ; Throughput ; Conductivity ; Ionic conductivity ; Electrolyte ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Electrode ; Telecommunications ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Conducting polymers and applications
Source: Angewandte Chemie International Edition (article, 2024-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphical Abstract: Angew. Chem. Int. Ed. 27/2024 Heterogeneous CatalysisIn their Communication (e202405379), Jun Cheng et al. use machine learning molecular dynamics to demonstrate anomalous entropic effect on catalysis via surface pre-melting of nanoclusters. INT ; Chemistry ; Computer science ; Programming language Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Axial and Atropisomeric Chirality Synthesis