jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Naoki Nohira | Taichi Ichisawa | Masaki Tahara | Itsuo Kumazawa | Hideki Hosoda
Source: Journal of Materials Research and Technology (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of the mechanical properties of β titanium shape memory alloys This study developed machine learning (ML) models to predict the mechanical properties of Ni-free β-type titanium shape memory alloys (SMAs). Using a dataset of 107 entries derived from both literature and laboratory experiments, we focused on predicting ultimate tensile strength (UTS) and elongation (EL). Key features, including Mo equivalent, bond order, and d-orbital energy level, were selected for the models through Pearson correlation maps and subset selection methods. Four ML algorithms—Linear Regression (LIN), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boosting Regression (GBR)—were employed and evaluated using metrics like mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). The GBR model for EL showed the highest prediction accuracy (R2 = 0.998 for training and R2 = 0.817 for testing), whereas UTS predictions were less accurate (R2 < 0.6 for testing). Although the models were also adapted to predict yield stress (YS), their accuracy was reduced, with improvements seen when incorporating phase constitution information reflecting phase stability. The primary reasons for the discrepancy in this study include the small dataset size and the absence of microstructural features. This research demonstrates the potential of ML models in predicting the mechanical properties of β-type titanium SMAs, highlighting the importance of integrating domain-specific knowledge through feature engineering to overcome the challenge of small data sets, and to enhance accuracy and robustness. Materials science ; Shape-memory alloy ; Titanium ; Titanium alloy ; Metallurgy ; Composite material ; Alloy Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Machine Learning in Materials Science
Authors: Yikun Kang | Yefei Li | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry C (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Unveiling Curvature Effect on Fe Atom Embedded N-Doped Carbon Nanotubes for Electrocatalytic Oxygen Reduction Reactions Using Hybrid Quantum-Mechanics/Machine-Learning Potential The curvature of the catalyst’s surface is a novel dimension of variables that can significantly affect the catalytic activity. Theoretical simulations of the curvature effect on catalytic activity are, however, highly challenging because the catalyst model, being at the mesoscopic scale (nm to μm), is far beyond the current computational power in treating chemical reactions based on first-principles calculations. Here we develop a hybrid QM/ML calculation scheme that combines quantum mechanics (QM) and machine learning (ML) potentials to explore the curvature effect on catalytic activity. With this approach, we are able to establish quantitative curvature–activity relationships in the representative electrocatalytic reactions, namely, oxygen reduction reaction (ORR) on both FeN4 and Fe2N6 moieties embedded in dissimilar carbon substrates (either graphene or carbon nanotubes) with different curvatures (κ) ranging from 0 nm–1 to 2 nm–1. The free energy changes of the potential-determining step (ΔGPDS) decrease linearly with the increase of curvature, and on the Fe2N6 it exhibits a steeper slope with dΔGPDS/dκ = −0.09 eV nm. By analyzing the electronic structures, we find a linear downshift of the energy level of Fe d-orbital as curvature increases, which leads to the change of binding strength of key reaction intermediates, i.e., the enhancement in Fe–OH2 binding. Our results provide new insights into the design of electrocatalysts by tuning the catalyst’s local curvature. Carbon nanotube ; Curvature ; Atom (system on chip) ; Doping ; Materials science ; Oxygen ; Reduction (mathematics) ; Nanotechnology ; Electrocatalyst ; Chemical physics ; Chemistry ; Quantum mechanics ; Physics ; Electrochemistry ; Optoelectronics ; Computer science ; Mathematics ; Electrode ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Qinghe Gao | Tammo Dukker | Artur M. Schweidtmann | Jana M. Weber
Source: Molecular Systems Design & Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Self-supervised graph neural networks for polymer property prediction Self-supervised learning for polymer property prediction in scarce data domains. Property (philosophy) ; Artificial neural network ; Computer science ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Martin Seifrid | Stanley Lo | Dylan G. Choi | Gary Tom | My Linh Le | Kunyu Li | Rahul Sankar | Hoai‐Thanh Vuong | Hiba Wakidi | Ahra Yi | Ziyue Zhu | Nora Schopp | Aaron Peng | Benjamin R. Luginbuhl | Thuc‐Quyen Nguyen | Alán Aspuru-Guzik
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices We assess state of machine learning for organic photovoltaic devices and data availability within the field, discuss best practices in representations and model selection, and release a comprehensive dataset of devices and fabrication conditions. Photovoltaic system ; Computer science ; Field (mathematics) ; Selection (genetic algorithm) ; Nanotechnology ; Systems engineering ; Artificial intelligence ; Materials science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Computational Drug Discovery Methods
Authors: Di Zhang | Qingzhao Chu | Dongping Chen
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting the enthalpy of formation of energetic molecules <i>via</i> conventional machine learning and GNN Machine learning (ML) provides a promising method for efficiently and accurately predicting molecular properties. Using ML models to predict the enthalpy of formation of energetic molecules helps in fast screening of potential high-energy molecules, thereby accelerating the design of energetic materials. A persistent challenge is to determine the optimal featurization methods for molecular representation and use an appropriate ML model. Thus, in our study, we evaluate various featurization methods (CDS, ECFP, SOAP, GNF) and ML models (RF, MLP, GCN, MPNN), dividing them into two groups: conventional ML models and GNN models, to predict the enthalpy of formation of potential high-energy molecules. Our results demonstrate that CDS and SOAP have advantages over the ECFP, while the GNFs in GCN and MPNN models perform better. Furthermore, the MPNN model performs best among all models with a root mean square error (RMSE) as low as 8.42 kcal mol<sup>-1</sup>, surpassing even the best performing CDS-MLP model in conventional ML models. Overall, this study provides a benchmark for ML in predicting enthalpy of formation and emphasizes the tremendous potential of GNN in property prediction. Enthalpy ; Representation (politics) ; Standard enthalpy change of formation ; Feature (linguistics) ; Molecule ; Chemistry ; Thermodynamics ; Statistical physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shuyu Wang | Hongxing Yue | Xiaoming Yuan
Source: Journal of Chemical Information and Modeling (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights Deep learning holds great potential for expediting the discovery of new polymers from the vast chemical space. However, accurately predicting polymer properties for practical applications based on their monomer composition has long been a challenge. The main obstacles include insufficient data, ineffective representation encoding, and lack of explainability. To address these issues, we propose an interpretable model called the Polymer Graph Convolutional Neural Network (PGCNN) that can accurately predict various polymer properties. This model is trained using the RadonPy data set and validated using experimental data samples. By integrating evidential deep learning with the model, we can quantify the uncertainty of predictions and enable sample-efficient training through uncertainty-guided active learning. Additionally, we demonstrate that the global attention of the graph embedding can aid in discovering underlying physical principles by identifying important functional groups within polymers and associating them with specific material attributes. Lastly, we explore the high-throughput screening capability of our model by rapidly identifying thousands of promising candidates with low and high thermal conductivity from a pool of one million hypothetical polymers. In summary, our research not only advances our mechanistic understanding of polymers using explainable AI but also paves the way for data-driven trustworthy discovery of polymer materials. Computer science ; Biochemical engineering ; Data science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shin Kiyohara | Yoyo Hinuma | Fumiyasu Oba
Source: Journal of the American Chemical Society (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning The band alignment of semiconductors, insulators, and dielectrics is relevant to diverse material properties and device structures utilizing their surfaces and interfaces. In particular, the ionization potential and electron affinity are fundamental quantities that describe surface-dependent band-edge positions with respect to the vacuum level. Their accurate and systematic determination, however, demands elaborate experiments or simulations for well-characterized surfaces. Here, we report machine learning for the band alignment of nonmetallic oxides using a high-throughput first-principles calculation data set containing about 3000 oxide surfaces. Our neural network accurately predicts the band positions for relaxed surfaces of binary oxides simply by using the information on bulk structures and surface termination planes. Moreover, we extend the model to naturally include multiple-cation effects and transfer it to ternary oxides. The present approach enables the band alignment of a vast number of solid surfaces, thereby opening the way to a systematic understanding and materials screening. Ternary operation ; Chemistry ; Artificial neural network ; Semiconductor ; Oxide ; Electron affinity (data page) ; Binary number ; Electronic band structure ; Set (abstract data type) ; Dielectric ; Surface (topology) ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Physics ; Condensed matter physics ; Molecule ; Geometry ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: M. Ghorbani | Ziyuan Li | Yao Qiu | Philippe Marcus | John R. Scully | Oumaïma Gharbi | Hong Luo | Rajeev Gupta | Zhuoran Zeng | Hamish L. Fraser | Mitra L. Taheri | N. Birbilis
Source: Metallurgical and Materials Transactions A (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Current Progress in Aqueous Corrosion of Multi-Principal Element Alloys While multi-principal element alloys (MPEAs) remain a promising class of materials owing to several attractive mechanical properties, their corrosion performance is also unique. In this concise review, we present an emerging overview of some of the general features related to MPEA corrosion, following a decade of work in the field. This includes highlighting some of the key aspects related to the electrochemical phenomena in MPEA corrosion, and the relevant future works required for a holistic mechanistic understanding. In addition, a comprehensive database of the reported corrosion performance of MPEAs is presented—based on works reported to date. The database is assembled to also allow users to undertake machine learning or their own data analysis, with a parsed representation of alloy composition, test electrolyte, and corrosion-related parameters. Corrosion ; Principal (computer security) ; Computer science ; Element (criminal law) ; Materials science ; Alloy ; Metallurgy Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors ; High Entropy Alloys Studies
Authors: Chureh Atasi | Joseph Kern | Rampi Ramprasad
Source: Journal of Chemical Information and Modeling (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Design of Recyclable Plastics with Machine Learning and Genetic Algorithm We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual forward synthesis (VFS) to generate almost a million ROP polymers. Machine learning models to predict thermal, thermodynamic, and mechanical properties─crucial for application-specific performance and recyclability─are used to guide the GA toward optimal polymers. We present potential substitute polymers for polystyrene (PS) that achieve all property targets with low estimated synthetic complexity. Polymer ; Genetic algorithm ; Monomer ; Class (philosophy) ; Computer science ; Algorithm ; Polystyrene ; Polymerization ; Materials science ; Artificial intelligence ; Machine learning ; Composite material Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; biodegradable polymer synthesis and properties
Authors: Shin-Yu Kim | Inseong Jeon | Seong-Joo Kang
Source: Journal of Chemical Education (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Integrating Data Science and Machine Learning to Chemistry Education: Predicting Classification and Boiling Point of Compounds Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds and predicts classification and boiling points of compounds from an unknown dataset. Students have experienced data collection and preprocessing, exploratory data analysis, modeling, and prediction. The No-Code-Low-Code Orange3 tool has been used for the process of modeling and prediction so that even beginners can easily perform Machine Learning (ML) analysis. The raw dataset containing 24 characteristics for 277,569 compounds went through data preprocessing process and became a well-refined dataset. The Random Forest model accurately predicted whether the type of compound in the unknown dataset was hydrocarbons, alcohols, or amines and predicted the boiling points of the some arbitrary compounds within the average error range of 4.49K. This activity will provide meaningful implications for how AI/DS technology could be integrated into each domain. Boiling point ; Chemistry ; Science education ; Point (geometry) ; Chemistry education ; Computer science ; Mathematics education ; Organic chemistry ; Mathematics ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Giuseppe Andriani | Gianmaria Pio | Ernesto Salzano | Chiara Vianello | Paolo Mocellin
Source: The Canadian Journal of Chemical Engineering (review, 2024-07-22)
Field: Materials Science — Materials Chemistry

Evaluating the thermal stability of chemicals and systems: A review Abstract In the realm of chemical processing, particularly at the industrial scale, safety is of utmost importance. A predominant factor causing accidents within the chemical industry is runaway phenomena, primarily initiated by uncontrolled exothermic reactions. This review critically examines the often‐overlooked decomposition mechanisms as a significant contributor to thermal energy release, necessitating a comprehensive revision and understanding of both experimental and theoretical strategies for assessing thermal degradation. Key to this discourse is the explication of calorimetry as the principal experimental technique, alongside ab initio quantum chemistry simulations as a robust theoretical framework for quantifying the most relevant properties. However, more than mere cognisance of these methodologies is required for a meticulous thermal stability assessment. The review emphasizes identifying and quantifying fundamental parameters through experimental and theoretical investigations. Only upon acquiring these parameters, including kinetic, thermodynamic, onset, and peak characteristics of the exothermic decomposition reactions, can one effectively mitigate risks and hazards in designing and optimizing chemical processes and apparatus. Furthermore, this review delineates qualitative and quantitative methodologies for hazard assessment, proffering strategies for estimating safe operational conditions and sizing relief devices. The paper culminates in exploring future trajectories in thermal stability assessments, focusing on emerging applications in lithium‐ion batteries, electrolyzers, electrified reactors, ionic liquids, artificial intelligence and machine learning approaches. Thus, the paper underlines the evolving landscape of thermal risk management in contemporary and future chemical industries. Thermal runaway ; Exothermic reaction ; Biochemical engineering ; Computer science ; Process engineering ; Hazard ; Principal (computer security) ; Chemical process ; Risk analysis (engineering) ; Explication ; Management science ; Chemistry ; Engineering ; Thermodynamics ; Physics Thermal and Kinetic Analysis ; Advanced Battery Technologies Research ; Thermochemical Biomass Conversion Processes
Authors: Masashi Ishii | Takuro Ito | Hiroko Sado | Isao Kuwajima
Source: Science and Technology of Advanced Materials Methods (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

NIMS polymer database PoLyInfo (I): an overarching view of half a million data points This paper reviews PoLyInfo, which is a polymer database of National Institute for Materials Science of Japan, containing more than half a million data points, from the perspective of a data editor.In particular, we describe how human-readable information provided by a graphical user interface (GUI) is curated, compiled, and displayed on the GUI, and how it can be used for data retrieval.We also describe the data curation policy of PoLyInfo, which can be observed through search functions and data tables, and show the unique taxonomy of various polymers.The status of polymers and their potential and limitations, as seen through a large dataset, are discussed.With this information, we introduce the capabilities of PoLyInfo and provide guidelines for its use as a general database for polymer chemistry, as well as notes on newer data applications such as machine learning. Database ; Computer science Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Medical Imaging Techniques and Applications
Authors: Manyi Yang | Enrico Trizio | Michele Parrinello
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structure and polymerization of liquid sulfur across the λ-transition We study the lambda liquid–liquid transition between molecular and polymeric phases of sulfur with molecular dynamics simulations boosted by state-of-the-art enhanced sampling techniques and ab initio -quality machine-learning potentials. Polymerization ; Molecular dynamics ; Ab initio ; Sulfur ; Liquid liquid ; Sampling (signal processing) ; Chemical physics ; Quality (philosophy) ; Chemistry ; Materials science ; Transition (genetics) ; Computational chemistry ; Organic chemistry ; Physics ; Polymer ; Chromatography ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Material Dynamics and Properties
Authors: Jyoti Bhattacharjee | Subhasis Roy
Source: Journal of Physics & Optics Sciences (article, 2024-03-31)
Field: Materials Science — Materials Chemistry

Density Functional Waltz: Molecular Theory of Spin Dynamics Spin dynamics in molecular systems lies at the heart of contemporary research, promising revolutionary applications in fields like spintronics and quantum information processing. The paper provides insights into green techniques to improve spin dynamics for utilization in quantum computing and magnetic materials. This dynamic approach describes the temperature-dependent evolution of spin-lattice interactions. Snapshots acquired from various simulations enable additional DFT investigations, providing a more sophisticated understanding of spin dynamics under temperature variations. By seamlessly integrating theoretical insights with computational approaches, the Density Functional Waltz emerges as a potent paradigm for unraveling the mesmerizing choreography of spin dynamics at the molecular scale. This review analyzes the evolution of machine-learning approaches in the field of chemical sciences. The proposed review will highlight the significance of collaboration between quantum catalysis and the chemical physics community. There is an outline of the unique features of electron spin alongside the ingenious integration of macromolecules to control and fine-tune electron spin-based innovation Spintronics ; Spin engineering ; Waltz ; Spin (aerodynamics) ; Computer science ; Molecular dynamics ; Nanotechnology ; Physics ; Spin polarization ; Electron ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures