jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rahul Kumar Sharma | Milan Kumar Jena | Harpriya Minhas | Biswarup Pathak
Source: ACS Applied Materials & Interfaces (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction In computational heterogeneous catalysis, Sabatier's principle-based activity volcano plots provide an intuitive guide to catalyst design but impose a fundamental constraint on the maximum achievable catalytic performance. Recently, subnano clusters have emerged as an exciting platform, offering high noble metal utilization and superior performance for various reactions compared to extended surfaces, reflecting a complex structure-activity relationship in the non-scalable regime. However, understanding their non-monotonic catalytic activity, attributed to the large configurational space and their fluxional identity, poses a formidable challenge. Here, we present a machine learning (ML) framework that captures the non-monotonic trends in oxygen reduction reaction (ORR) activity at the subnanometer scale, attributed to their dynamic fluxional characteristics. We demonstrate a size-dependent shifting and reshaping of the ORR activity volcano, with Au replacing Pt at the peak. Leveraging only upon the non-<i>ab initio</i> geometric and electronic properties, our trained ML model accurately captures the site-specific adsorption energies of intermediates at the subnanometer regime. To account for the inconsistent trend in activity, we analyzed the correlation between electronic and geometric properties. Our findings reveal that the d-filling and coupling matrix of the neighboring metal atom significantly influences the intermediate adsorption on the local chemical environment compared to the d-band center. Following this analysis, we utilized ML to map the catalyst distribution in the activity volcano and identified the five best sub-nano electrocatalysts, demonstrating overpotential values lower than or comparable to the Pt(111) surface for the ORR. This study provides intuitive guidelines for the rational designing of highly efficient electrocatalysts for fuel cell applications while modifying the activity volcano plots for electrocatalysts at the subnanometer regime. Oxygen reduction reaction ; Materials science ; Oxygen reduction ; Nanotechnology ; Volcano ; Reduction (mathematics) ; Oxygen evolution ; Oxygen ; Electrode ; Electrochemistry ; Physical chemistry ; Organic chemistry ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Ran Wang | Teng Fu | Yajie Yang | Xiuli Wang | Yu-Zhong Wang
Source: Polymer Degradation and Stability (article, 2024-09-14)
Field: Materials Science — Polymers and Plastics

Deeper insights into flame retardancy of polymers by interpretable, quantifiable, yet accurate machine-learning model Polymer ; Polymer science ; Artificial intelligence ; Materials science ; Computer science ; Biochemical engineering ; Machine learning ; Composite material ; Engineering Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: Yifei Zhu | Jiawei Peng | Chao Xu | Zhenggang Lan
Source: The Journal of Physical Chemistry Letters (review, 2024-09-13)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges. Molecular dynamics ; Dynamics (music) ; Unsupervised learning ; Computer science ; Artificial intelligence ; Statistical physics ; Machine learning ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kalagotla Sai Chandana | Reddi Kamesh
Source: International Journal of Hydrogen Energy (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Machine learning insights into prediction of H2 gravimetric capacity in Mg-based pure metal alloys Gravimetric analysis ; Materials science ; Metal ; Hydrogen storage ; Computer science ; Metallurgy ; Chemistry ; Alloy ; Organic chemistry Hydrogen Storage and Materials ; Spacecraft and Cryogenic Technologies ; Superconducting Materials and Applications
Authors: Zhuoyi Wei | Kai Wei | Xujing Yang
Source: Composite Structures (article, 2024-05-14)
Field: Materials Science — Polymers and Plastics

Inverse design of irregular architected materials with programmable stiffness based on deep learning Stiffness ; Inverse ; Bending stiffness ; Isotropy ; Computer science ; Orthotropic material ; Inverse problem ; Function (biology) ; Structural engineering ; Engineering ; Mathematics ; Finite element method ; Geometry ; Physics ; Mathematical analysis ; Optics Polymer composites and self-healing ; Cellular and Composite Structures ; Advanced Materials and Mechanics
Authors: Cong Nie | Kuo Wang | Haixin Zhou | Jiahao Deng | Ziye Chen | Kang Zhang | Lingjiao Chen | Di Huang | Jiaojiao Liang | Ling Zhao
Source: ACS Applied Materials & Interfaces (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Combination of Transfer Learning and Chemprop Interpreter with Support of Deep Learning for the Energy Levels of Organic Photovoltaic Materials Prediction and Regulation It is challenging to build a deep learning predictive model using traditional data mining methods due to the scarcity of available data, and the model's internal decision-making process is often nonintuitive and difficult to explain. In this work, a directed message passing neural network model with transfer learning (TL) and chemprop interpreter is proposed to improve energy levels prediction and visualization for organic photovoltaic materials. The established model shows the best performance, with coefficient of determination reaching 0.787 for HOMO and 0.822 for LUMO in a small testing set after TL, compared to the other four models. Then, the chemprop interpreter analyzes local and global effects of 12 molecular structures on the energy levels for organic materials. After a comprehensive analysis of the energy level effects of nonfullerene Y-series, IT-series, and other organic materials, 12 new IT-series derivatives are designed. 1,1-dicyano-methylene-3-indanone (IC) end group halogenation can reduce HOMO and LUMO energy levels to varying degrees, while IC end group modified by electron-withdrawing aromatic groups can increase HOMO and LUMO energy levels and obtain relatively smaller electrostatic potential (ESP) to reducing intermolecular interactions. The influence of side-chain modification on energy levels is limited. It is worth mentioning that the predicted results of IT-series derivatives match density functional theory calculations. The model also shows good generalization and transferability for predicting the energy levels of other organic electronic materials. This work not only provides a cost-effective model for predicting the energy levels of organic photovoltaic materials but also explains the potential bridge between molecular structure and electronic properties. Photovoltaic system ; Materials science ; Transfer of learning ; Energy transfer ; Interpreter ; Energy (signal processing) ; Engineering physics ; Nanotechnology ; Artificial intelligence ; Computer science ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Yuhao Guo | Shichao Wang | Gang Liu
Source: Ocean Engineering (article, 2024-07-30)
Field: Materials Science — Metals and Alloys

Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model Creep ; Titanium alloy ; Artificial neural network ; Damage mechanics ; Continuum mechanics ; Materials science ; Engineering ; Structural engineering ; Alloy ; Geology ; Marine engineering ; Mechanics ; Computer science ; Metallurgy ; Finite element method ; Artificial intelligence ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques
Authors: Ryan Jacobs | Lane E. Schultz | Aristana Scourtas | K. J. Schmidt | Owen Price-Skelly | Will Engler | Ian Foster | Ben Blaiszik | Paul M. Voyles | Dane Morgan
Source: Machine Learning Science and Technology (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility Abstract One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials. Domain (mathematical analysis) ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Sajid Mannan | Vaibhav Bihani | N. M. Anoop Krishnan | John C. Mauro
Source: Materials Genome Engineering Advances (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning Abstract The energy landscape represents a high‐dimensional mapping of the configurational states of an atomic system with their respective energies. Under isobaric conditions, enthalpy landscapes can be used to account for volumetric changes of the system. Understanding the energy or enthalpy landscape holds the key for discovering materials with targeted properties, since the landscape encapsulates the complete thermodynamic and kinetic behavior of a system, including relaxation, metastable phases, and reactivity. However, the curse of dimensionality prohibits one from enumerating and visualizing the energy landscape—the energy landscape of an N‐atom system has 3N dimensions. Here, we outline the emerging computational techniques that allow the exploration of complex energy landscapes of materials in three distinct categories: the classical, metaheuristic, and machine learning approaches. We discuss the advantages and disadvantages associated with each of these methods, with a focus on the nature of problems where they can provide excellent solutions (and vice versa). Altogether, in addition to giving an overview of existing approaches, we hope the review provides an impetus to develop novel methods to explore the energy landscapes that can, in turn, provide both a fundamental understanding of the physics of materials and accelerate the discovery of novel materials. Energy landscape ; Fitness landscape ; Curse of dimensionality ; Computer science ; Metastability ; Energy (signal processing) ; Data science ; Statistical physics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Aurelio Bifulco | Immacolata Climaco | Angelo Casciello | Jessica Passaro | Daniele Battegazzore | Viviana Nebbioso | Pietro Russo | Claudio Imparato | Antonio Aronne | Giulio Malucelli
Source: Journal of Materials Science (article, 2024-12-28)
Field: Materials Science — Biomaterials

Prediction and validation of fire parameters for a self-extinguishing and smoke suppressant electrospun PVP-based multilayer material through machine learning models Electrospinning is a technology largely employed to obtain polymer fibers with different functionalities. The electrospinning of polyvinylpyrrolidone (PVP) in the presence of silica nanoparticles, and the subsequent thermal treatment of these electrospun PVP-silica fibers, allows for the manufacturing of a self-extinguishing material stable in polar solvents. However, this material lacks consistency and does not sustain any load: this strongly limits its application in many industrial fields (e.g., the aerospace sector). Herein, we used cross-linked electrospun PVP-silica blankets and TiO2 nanoparticles to coat hemp blankets, producing a multilayer material (MM) by surface charge interaction. The MM exhibited lower stiffness than the original hemp fabric but still good mechanical behavior, V0 class at the UL 94 vertical burning test, and good stretchability even after direct flame exposure. Further, burn-through and cone calorimetry tests revealed that MM is an excellent smoke suppressant and fireproof fabric, with very low total smoke release values (as low as 4.9 vs. − 33.3 m2/m2 measured for hemp) and its structure remained intact for at least − 1 min. Finally, as all the aforementioned experimental activity, though necessary and unsubstantial, is usually quite time-consuming, two Machine Learning models were developed and exploited to predict the fire performances related to the multilayer material. Despite the incomplete starting datasets, the implemented models accounted for a successful prediction of the target parameters (namely, Time to Ignition and peak of Heat Release Rate), thanks to the assistance of ChatGPT and the exploitation of made-on-purpose decision trees. Materials science ; Smoke ; Composite material ; Solid mechanics ; Process engineering ; Waste management ; Engineering Electrospun Nanofibers in Biomedical Applications ; Flame retardant materials and properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Hisham Alghamdi | Chika Maduabuchi | Aminu Yusuf | Sameer Al‐Dahidi | Sedat Ballıkaya | Abdullah Albaker | Ahmed S. Alsafran | Mohammed A. Alghassab | Emad Makki | Mohammad Alkhedher
Source: Results in Engineering (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Semiconductors for enhanced solar photovoltaic-thermoelectric 4E performance optimization: Multi-objective genetic algorithm and machine learning approach In this study, a groundbreaking exploration of a concentrated photovoltaic-thermoelectric (CPV-TE) module employing an unprecedented selection of six thermoelectric materials across diverse temperature ranges is presented. This work innovatively employs a multi-objective optimization framework that combines genetic algorithms and goal attainment methods, aiming to optimize energy, exergy, environmental, and economic (4 E) performance. Notably, this study is the first to construct and evaluate five regression-based machine learning models with an emphasis on minimal root mean squared error and mean absolute error for rapid CPV-TE performance predictions, essential for real-world applications. Our analysis unveils that among the tested materials, the CPV-TE module incorporating lead telluride (PbTe) achieves the highest 4 E performance, demonstrating a peak exergy efficiency of 14.2 %, and annual energy savings and carbon reduction of 13.5 kWh and 6.4 kg, respectively. Furthermore, the Gaussian Process Regression model is identified as the most effective among the machine learning models for forecasting performance across the materials. This study significantly advances the field by providing novel insights into thermoelectric material selection and optimization for CPV-TE modules, and establishing pioneering forecasting tools that catalyze the efficient deployment of these systems. Photovoltaic system ; Thermoelectric effect ; Computer science ; Exergy ; Algorithm ; Machine learning ; Thermoelectric generator ; Kriging ; Genetic algorithm ; Artificial intelligence ; Process engineering ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Environmental Education and Sustainability
Authors: Tomoya Shiota | Kenji Ishihara | Wataru Mizukami
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Universal neural network potentials as descriptors: towards scalable chemical property prediction using quantum and classical computers Using outputs from a pre-trained universal neural network potential's graph layer as descriptors enables efficient and accurate predictions of molecular properties. These descriptors are compact yet perform as well as the best current descriptors. Scalability ; Artificial neural network ; Computer science ; Property (philosophy) ; Graph ; Artificial intelligence ; Quantum chemical ; Theoretical computer science ; Pattern recognition (psychology) ; Machine learning ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jaejung Park | Minseon Kim | Heekyu Kim | Jaejun Lee | Inhyo Lee | Haesun Park | Anna Lee | Kyoungmin Min | Seung‐Chul Lee
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the large chemical space in search of thermodynamically stable and mechanically robust MXenes <i>via</i> machine learning To effectively utilize MXenes, a family of two-dimensional materials, in various applications that include thermoelectric devices, semiconductors, and transistors, their thermodynamic and mechanical properties, which are closely related to their stability, must be understood. However, exploring the large chemical space of MXenes and verifying their stability using first-principles calculations are computationally expensive and inefficient. Therefore, this study proposes a machine learning (ML)-based high-throughput MXene screening framework to identify thermodynamically stable MXenes and determine their mechanical properties. A dataset of 23 857 MXenes with various compositions was used to validate this framework, and 48 MXenes were predicted to be stable by ML models in terms of heat of formation and energy above the convex hull. Among them, 45 MXenes were validated using density functional theory calculations, of which 23 MXenes, including Ti<sub>2</sub>CClBr and Zr<sub>2</sub>NCl<sub>2</sub>, have not been previously known for their stability, confirming the effectiveness of this framework. The in-plane stiffness, shear moduli, and Poisson's ratio of the 45 MXenes were observed to vary widely according to their constituent elements, ranging from 90.11 to 198.02 N m<sup>-1</sup>, 64.00 to 163.40 N m<sup>-1</sup>, and 0.19 to 0.58, respectively. MXenes with Group-4 transition metals and halogen surface terminations were shown to be both thermodynamically stable and mechanically robust, highlighting the importance of electronegativity difference between constituent elements. Structurally, a smaller volume per atom and minimum bond length were determined to be preferable for obtaining mechanically robust MXenes. The proposed framework, along with an analysis of these two properties of MXenes, demonstrates immense potential for expediting the discovery of stable and robust MXenes. MXenes ; Chemical space ; Density functional theory ; Stability (learning theory) ; Space (punctuation) ; Throughput ; Chemical stability ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Nanotechnology ; Engineering ; Chemistry ; Computational chemistry ; Chemical engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Rajeshkannan Radhamani | B. Muralidharan
Source: Materials Today Communications (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Predictive modeling of phase transformation temperatures in NiTiCu shape memory alloys: Integrating electronic factors through artificial neural network Materials science ; Artificial neural network ; Transformation (genetics) ; Shape-memory alloy ; Phase (matter) ; Biological system ; Artificial intelligence ; Metallurgy ; Computer science Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials