jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zaheer Ahmed Dayo | Guosong Jiang | Mohamed A. El-Tayeb | Syed Shoaib Ahmad Shah | Sumaira Naeem
Source: ACS Omega (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Designing and Screening of Polymers with a High Melting Point: Database Visualization and Synthetic Feasibility Assessment In this study, a novel approach leveraging machine learning (ML) techniques for the design and screening of polymers with high melting points is introduced. More than 40 ML models are trained for the prediction of the melting point. One best model is selected for further analysis. 10,000 polymers are generated using an automatic approach. The generated database of polymers is visualized and analyzed to find the hidden trends. Synthetic feasibility assessment is conducted to prioritize candidate polymers for future experimental work. Chemical similarity of chosen polymers is analyzed using cluster analysis and a heatmap. This research contributes to the advancement of polymer design methodologies, offering insights into the development of heat-resistant polymers for a wide range of industrial applications. Polymer ; Similarity (geometry) ; Computer science ; Visualization ; Point (geometry) ; Database ; Machine learning ; Data mining ; Materials science ; Artificial intelligence ; Image (mathematics) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Zhuocheng Xie | Achraf Atila | Julien Guénolé | Sandra Korte‐Kerzel | Talal Al‐Samman | U. Kerzel
Source: arXiv (Cornell University) (preprint, 2024-07-19)
Field: Materials Science — Biomaterials

Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment's flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties. Grain boundary ; Magnesium ; Materials science ; Boundary (topology) ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Nana Zhou | Yaling Zhao | Qingzhang Lv | Yahong Chen
Source: The Journal of Physical Chemistry C (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction The heteroatom-doped metallic compounds supported on conductive substrates are excellent catalysts for the hydrogen evolution reaction (HER) thanks to their tunable properties, e.g., metallic and nonmetallic compositions, especially bimetallic active centers and their synergistic effect, as well as the tunable morphology and interaction between the active centers and substrate. Only the optimal combination between these adjustable properties and other external factors could endow the remarkable HER catalytic activity of the catalysts. Therefore, in this study, the machine learning (ML) database based on plenty of HER catalysts from publicly available data was conducted to train three different ML models, and the various features including electrolyte type, catalyst morphology, compositions (metallic and nonmetallic) and their ratios, additive, and substrate were analyzed to figure out their impacts on overpotential (OP) values to determine the outstanding HER catalysts. According to the feature importance and Spearman coefficient analysis, the optimal combination of metal elements and their ratio were determined to be Pt, Mo and 0.5, and the heteroatoms and substrate were determined to be nitrogen, sulfur, and nickel foam. Finally, the ML model predicts that the foam nickel-supported bimetallic catalyst composed of Pt and Mo2S3 and codoped with nitrogen and sulfur (N, S-doped Pt@Mo2S3) exhibits the admirable HER catalytic performance in alkaline electrolytes with a pretty low OP value of 33 mV. The database-guided ML model provides an alternative for rapid screening and prediction of HER electrocatalysts. Heteroatom ; Substrate (aquarium) ; Doping ; Materials science ; Metal ; Hydrogen ; Nanotechnology ; Chemistry ; Optoelectronics ; Metallurgy ; Organic chemistry ; Oceanography ; Geology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Yulian T. Manchev | Matthew J. Burn | Paul L. A. Popelier
Source: Journal of Computational Chemistry (article, 2024-08-31)
Field: Materials Science — Materials Chemistry

Ichor: A Python library for computational chemistry data management and machine learning force field development We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development. Python (programming language) ; Computer science ; Scripting language ; Software ; Data management ; Artificial intelligence ; Visualization ; Software engineering ; Machine learning ; Programming language ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Wenbo Wei | G. Min | Qingyang Fan | Liang Dong | Ming Ye | Sining Yun
Source: Vacuum (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Exploration of novel boron nitride polymorphs: High-throughput screening combined with multi-task orbital crystal graph convolutional neural network (MT-OCGCN) In response to the limitations of traditional material property prediction methods, we have developed a Multi-task Orbital Crystal Graph Convolutional Neural Network (MT-OCGCN). This innovative model allows for sharing parameters and computational resources, significantly improving prediction efficiency and accuracy. Experimental comparisons show that the model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties, this offers an efficient approach for computational materials screening and design. Combined with high-throughput screening has resulted in the discovery of 13 new boron nitride polymorphs in the P 2 1 / c phase, among which 7 are characterized as wide direct band gap semiconductors. Furthermore, we have comprehensively analyzed the mechanical, thermal, and electronic properties of these new boron nitride polymorphs. It can be asserted that MT-OCGCN has the capability to precisely forecast the physical properties of novel structures that are absent from current databases. • A new deep learning model called MT-OCGCN was introduced for multi-task prediction. • The MT-OCGCN model can achieve multi-task prediction with high accuracy for both strong and weak correlation properties. • 13 new boron nitride polymorphs and their mechanical, thermal, and electronic properties were identified. • 7 wide direct band gap semiconductors were discovered. Convolutional neural network ; Boron nitride ; Graph ; Computer science ; Throughput ; Task (project management) ; Boron ; Crystal (programming language) ; Materials science ; Artificial intelligence ; Chemistry ; Nanotechnology ; Theoretical computer science ; Engineering ; Telecommunications Machine Learning in Materials Science ; Boron Compounds in Chemistry ; Boron and Carbon Nanomaterials Research
Authors: Shijia Wang | Xiutai Zhuo | Haonan Fan | Chengang Cao | Tao Jiang | Bing Yan
Source: Polymer (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Computer-assisted design of CGC catalysts for ethylene/1-octene copolymerization: A combined DFT and artificial neural network approach Polyolefin ; Copolymer ; 1-Octene ; Steric effects ; Catalysis ; Materials science ; Octene ; Ethylene ; Metallocene ; Artificial neural network ; Cyclopentadienyl complex ; Density functional theory ; Post-metallocene catalyst ; Electronic effect ; Elastomer ; Polymerization ; Computational chemistry ; Chemistry ; Computer science ; Organic chemistry ; Polymer ; Nanotechnology ; Composite material ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Shengzhou Li | Ayako Nakata
Source: Chemistry Letters (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

CSIML: a cost-sensitive and iterative machine-learning method for small and imbalanced materials data sets Abstract Materials science research benefits from the powerful machine-learning (ML) surrogate models, but it is also limited by the implicit requirement for sufficiently big and balanced data distribution for ML. In this paper, we propose a model to obtain more credible results for small and imbalanced materials data sets as well as chemical knowledge. Taking 2 bandgaps imbalanced data sets as instances, we demonstrate the usability and performance of our model compared with common ML models with normal sampling and resampling methods. Resampling ; Usability ; Machine learning ; Sampling (signal processing) ; Artificial intelligence ; Chemistry ; Big data ; Computer science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Weiren Zhao | Yang Li
Source: ChemistrySelect (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Predicting the Yield of Pd‐Catalyzed Buchwald–Hartwig Amination Using Machine Learning with Extended Molecular Fingerprints and Selected Physical Parameters Abstract Machine learning has gained attention due to its ongoing advancements and diverse applications. Within the field of homogeneous catalysis, a prominent area of research in machine learning revolves around predicting reaction yield in Pd‐catalyzed Buchwald–Hartwig amination reactions. This study sought to determine the optimal descriptors for representing the both structural and physical information associated with the reaction, particularly focusing on product details. To achieve this, we assessed the utilization of product extended molecular fingerprints (PEMF) and selected physical parameters (SPP). The utilization of a random forest model incorporating these descriptors yielded promising results in the prediction of reaction yields in Pd‐catalyzed Buchwald–Hartwig amination reactions. The model achieved an impressive R 2 value of 0.943. Data preprocessing on PEMF and sorting preprocessing on physical parameters resulted in a significant reduction in data size to 259 bits PEMF+2 SPPs per prediction, much less than the two previous random forest models which utilized 480 physical parameters and 21,073 bits molecular fingerprints. Although establishing definitive correlations between SPPs and reaction yield presented challenges, our findings indicate that the presence of heavier atoms in the aryl halides may have a beneficial impact within the examined Pd‐catalyzed Buchwald–Hartwig amination reactions, as compared to their analogues. Amination ; Yield (engineering) ; Combinatorial chemistry ; Catalysis ; Artificial intelligence ; Chemistry ; Computer science ; Process engineering ; Materials science ; Organic chemistry ; Engineering ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: X. Shi | Xin Zhong | Wei Liu | Songwei Wang | Zhijun Zhang | Lin Li | Yuguo Chen | Kehong Zhang | Jing‐Tai Zhao
Source: Journal of Luminescence (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Prediction and exploration of emission wavelength (or energy) of luminescent materials based on machine learning Luminescence ; Wavelength ; Energy (signal processing) ; Materials science ; Computer science ; Environmental science ; Optoelectronics ; Artificial intelligence ; Engineering physics ; Physics Luminescence Properties of Advanced Materials ; Advanced Chemical Sensor Technologies ; Digital Radiography and Breast Imaging
Authors: Mai Le | Hieu Van Le | Lihong Zhao | Xuqing Wu | Jiefu Chen | Yan Yao
Source:  (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Predicting Ionic Conductivity of Solid-State Battery Cathodes Using Machine Learning Safer and more efficient battery technologies are in soaring demand, with a primary focus on transitioning from flammable lithium-ion batteries to non-flammable solid-state batteries. While solid-state batteries offer enhanced safety features, their power density remains a challenge due to poor ionic conductivity induced by non-optimal cathode microstructures. Laborious experimental processes and time- consuming data analysis algorithms are obstacles to establishing structure-performance correlation and optimizing cathode microstructure. In this paper, we present a machine learning approach to predict the current or resistance of a composite cathode based on scanning electron microscopy (SEM) images, given the inputs as a binary image, a voltage, and a conductivity value. Our results showed that current or resistance can be quickly predicted from input images with high accuracy. Battery (electricity) ; Ionic conductivity ; Cathode ; Conductivity ; Materials science ; Solid-state ; Computer science ; Ionic bonding ; Electrical engineering ; Optoelectronics ; Engineering physics ; Electrode ; Ion ; Engineering ; Chemistry ; Thermodynamics ; Physics ; Power (physics) ; Electrolyte ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Recycling and Waste Management Techniques
Authors: Daniel P. Devore | Kevin L. Shuford
Source: Journal of Chemical Information and Modeling (article, 2024-10-29)
Field: Chemistry — Physical and Theoretical Chemistry

Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding The ability to predict the strength of halogen bonds and properties of halogen bond (XB) donors has significant utility for medicinal chemistry and materials science. XBs are typically calculated through expensive ab initio methods. Thus, the development of tools and techniques for fast, accurate, and efficient property predictions has become increasingly more important. Herein, we employ three machine learning models to classify the XB donors and complexes by their principal halogen atom as well as predict the values of the maximum point on the electrostatic potential surface (<i>V</i><sub>S,max</sub>) and interaction strength of the XB complexes through a molecular fingerprint and data-based analysis. The fingerprint analysis produces a root-mean-square error of ca. 7.5 and ca. 5.5 kcal mol<sup>-1</sup> while predicting the <i>V</i><sub>S,max</sub> for the halobenzene and haloethynylbenzene systems, respectively. However, the prediction of the binding energy between the XB donors and ammonia acceptor is shown to be within 1 kcal mol<sup>-1</sup> of the density functional theory (DFT)-calculated energy. More accurate predictions can be made from the precalculated DFT data when compared to the fingerprint analysis. Fingerprint (computing) ; Halogen bond ; Computer science ; Artificial intelligence ; Machine learning ; Halogen ; Chemistry ; Organic chemistry Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Kajjana Boonpalit | Nongnuch Artrith
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations Electrocatalytic water splitting, which produces hydrogen and oxygen through water electrolysis, is a promising method for generating renewable, carbon-free alternative fuels. However, its widespread adoption is hindered by the high costs of Pt cathodes and IrO$_{x}$/RuO$_{x}$ anode catalysts. In the search for cost-effective alternatives, barium titanate (BaTiO$_{3}$) has emerged as a compelling candidate. This inexpensive, non-toxic perovskite oxide can be synthesized from earth-abundant precursors and has shown potential for catalyzing the oxygen evolution reaction (OER) in recent studies. In this work, we explore the OER activity of pristine and Ni-doped BaTiO$_{3}$ at explicit water interfaces using metadynamics (MetaD) simulations. To enable efficient and practical MetaD for OER, we developed a machine learning interatomic potential based on artificial neural networks (ANN), achieving large-scale and long-time simulations with near-DFT accuracy. Our simulations reveal that Ni-doping enhances the catalytic activity of BaTiO$_{3}$ for OER, consistent with experimental observations, while providing mechanistic insights into this enhancement. Barium titanate ; Nickel ; Barium ; Oxygen ; Oxygen evolution ; Doping ; Computer science ; Materials science ; Chemistry ; Metallurgy ; Physical chemistry ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Fulong Wang | Wei Liu | Yipu Sun | Bo Zhang | Hai Li | Longjun Chen | Bowen Hou | Haoyu Zhang
Source: Corrosion Reviews (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Model construction of corrosion resistance of alloying elements for low alloy steel in marine atmospheric corrosive environment based on machine learning Abstract The study focused on constructing a machine learning model, considering the interaction of alloying elements on corrosion resistance of low alloy steels in the marine atmospheric environment. Spearman’s analysis was applied, and the relationship between alloying element and corrosion rate was evaluated based on random forest (RF) importance and Shapley additive explanation (SHAP) analysis. The prediction performance of the six models (RF, multilayer perceptron (MLP), ridge regression (RR), K-nearest neighbor regression (KNN), logistic regression (LR), and support vector machine (SVM) was compared by using the preferred dominant elements as input variables. Afterwards, a high-precision corrosion rate prediction model based on RF was constructed. Finally, the generalizability of the model was demonstrated using 10 lines of steel corrosion data from several new marine atmospheric environments. Materials science ; Corrosion ; Metallurgy ; Alloy ; Wear resistance ; Materials processing ; Process engineering ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Engineering Diagnostics and Reliability
Authors: Jonathan S. Van Buskirk | Gordon G. C. Peterson | Daniel C. Fredrickson
Source: Journal of the American Chemical Society (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extremes of Intermetallic Complexity Intermetallic phases represent a domain of emergent behavior, in which atoms with packing and electronic preferences can combine into complex geometrical arrangements whose long-range order involves repeat patterns containing thousands of atoms or is incompatible with a 3D unit cell. The formation of such arrangements points to unexplained driving forces within these systems that, if understood, could be harnessed in the design of new metallic materials. DFT-chemical pressure (CP) analysis has emerged as an approach to visualize how atomic packing tensions within simpler crystal structures can drive this complexity and create potential functionality. However, the applications of this method have hitherto been limited in scope by its dependence on resource-intensive electronic structure calculations. In this Article, we develop machine learning (ML)-based implementation of the CP approach, drawing on the collection of DFT-CP schemes in the Intermetallic Reactivity Database. We illustrate the method with comparisons of ML-CP and DFT-CP schemes for a series of examples, before demonstrating its application with an exploration of one of the quintessential instances of complexity in intermetallic chemistry, Mg<sub>2</sub>Al<sub>3</sub>, whose high-temperature unit cell is a 2.8 nm cube containing 1227 atoms. An analysis of its ML-CP-derived interatomic pressures traces the origins of the structure to simple matching rules for the assembly of Frank-Kasper polyhedra. The ML-CP model can be immediately employed on other intermetallic systems, through either its web interface or a command-line version, with just a crystallographic information file. Chemistry ; Intermetallic ; Metallurgy ; Organic chemistry ; Alloy Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; Titanium Alloys Microstructure and Properties
Authors: Yuanyuan Cui | Chengyu Zhang | Ling Niu | Xuchang Tang | Zhaokai Luo | Guomin Le | Yanfeng Gao
Source: Surfaces and Interfaces (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Metal-to-semiconductor transitions and optical properties of ReNiO3 superlattices: An integration of first-principles calculations and machine learning analysis Materials science ; Superlattice ; Semiconductor ; Condensed matter physics ; Metal ; Optoelectronics ; Nanotechnology ; Metallurgy ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials