jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liang Zhao | Yachun Wang | Fei Xu
Source: Research Square (preprint, 2025-06-12)
Field: Chemistry — Analytical Chemistry

Accurate Segmentation of Localized Fuel Cladding Chemical Interaction Layers in SEM Micrographs with Deep Learning Method Cladding (metalworking) ; Materials science ; Micrograph ; Segmentation ; Artificial intelligence ; Electron micrographs ; Composite material ; Computer science ; Optics ; Scanning electron microscope ; Physics ; Electron microscope Petroleum Processing and Analysis ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Karol Neftali Camarena-Gómez | Juan de Anda-Suárez | J. Ramirez | María Guadalupe Ordaz Cervantes
Source: Journal of Technological Prototypes (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Intelligent Thermoelectric Power Generation System based on Semiconductors and Deep Learning for Electromobility and Environmental Sustainability Today’s energy requirements for socio-industrial sectors have been compromised due to the global warming issue, one of the renewal energy most profitable is solar energy, this energy has levels of estimated peta watts per second (PW/s) which is impressive and highly usable, the most used device for its obtainable energy are solar panels because of the technique it needs, from converting photon energy to electrical usable energy, but today we need more alternatives to get and take advantage of transforming the energy, the development of a new semiconductor has been the principal alternative to helping us getting all the energy possible from the sun, this aims for the designing and simulating of this power generation system, the físic model of the semiconductor material is BixSb2-dTe3, the principal way of optimize our Artificial Neural Network (ANN) for its correctly function is enabling optimization of the 𝛼(𝑇), � �(𝑇), and 𝜅(𝑇) parameters to match the output values of the electrical generation. USable ; Electricity generation ; Solar energy ; Artificial neural network ; Energy (signal processing) ; Principal (computer security) ; Function (biology) ; Power (physics) ; Photovoltaic system Advanced Thermoelectric Materials and Devices ; Internet of Things and AI ; Advanced Technologies and Applied Computing
Authors: Ruotong Chen | Zhihua Hu | Hong‐Jian Feng
Source: Journal of Applied Physics (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Predicting efficient and stable inorganic photovoltaic materials using interpretable machine learning combined with DFT calculations based on band edge orbital engineering The discovery of new materials with high power conversion efficiency (PCE) is critical for the advancement of solar energy technologies. In this study, we combine traditional machine learning (ML) methods and deep learning methods to predict and categorize the inorganic photovoltaic materials, which are more stable than the organic counterpart. By employing gradient boosting trees, extremely randomized trees, random forests, backpropagation neural network, and convolutional neural network, we classify materials by bandgap (zero or non-zero) and predict their values with regression model. The stability, bandgaps, optical absorption, and PCE of these materials are validated through density functional theory (DFT) calculations, leading to the identification of promising candidates: Li2Bi4Se7, Na2Bi4Se7, and Mo2Ba5N7. The Shapley Additive Explanation method is applied to analyze feature interactions, intuitively establishing the relationship between band-edge orbitals and material properties, while uncovering hidden connections between structural and electronic properties. The results reveal that the delocalization of valence electrons, along with variations in atomic coordination environments, modifies the charge density distribution at the band edges, affecting the transition probabilities and atomic orbital connectivity. Among these, Li2Bi4Se7 and Na2Bi4Se7 stand out as the most promising materials for solar cell applications. Our findings provide a novel framework for accelerating the discovery of efficient photovoltaic materials using ML and DFT methods. Boosting (machine learning) ; Photovoltaic system ; Band gap ; Density functional theory ; Convolutional neural network ; Organic solar cell ; Artificial neural network ; Gradient boosting ; Backpropagation ; Ensemble learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Chalcogenide Semiconductor Thin Films
Authors: Guixin Chen | Yonggang Yan | Yi Zhao | Hui Li | Dong Wang | Xunxiang Hu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A machine learning framework for high-strength Mo-Re alloy design Ultimate tensile strength ; Alloy ; Gaussian process ; Process (computing) ; Space (punctuation) ; Fabrication ; RADIUS ; Mixing (physics) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Shin Kiyohara | Chisa Shibui | Soungmin Bae | Yu Kumagai
Source: Physical Review Letters (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of Charged-Defect Formation Energies from Crystal Structures Recent advances in materials informatics have expanded the number of synthesizable materials. However, screening promising candidates, such as semiconductors, based on defect properties remains challenging. This is primarily due to the lack of a general framework for predicting defect formation energies in multiple charge states from structural data. In this Letter, we present a protocol, namely data normalization, Fermi level alignment, and treatment of perturbed host states, and validate it by accurately predicting oxygen vacancy formation energies in three charge states using a single model. We also introduce a joint machine-learning model that integrates defect formation energies and band-edge predictions for virtual screening. Using this framework, we identify 89 hole-dopable oxides, including BaGaSbO, a potential ambipolar photovoltaic material. Our protocol is expected to become a standard approach for machine-learning studies on point defect formation energies. Ambipolar diffusion ; Charge (physics) ; Crystallographic defect ; Vacancy defect ; Fermi level ; Crystal structure ; Electronic structure Machine Learning in Materials Science ; Ga2O3 and related materials ; Explainable Artificial Intelligence (XAI)
Authors: Javed Akram | Kanwal Ranian | Sohail Nadeem | Mohammed T. Alotaibi
Source: Journal of Fluorescence (article, 2025-06-13)
Field: Materials Science — Polymers and Plastics

Predicting the Experimental Emission Spectra of Fluorescent Organic Semiconductors by Ensemble Machine Learning Analysis Chemistry ; Fluorescence ; Semiconductor ; Ensemble learning ; Spectral line ; Analytical Chemistry (journal) ; Nanotechnology ; Biological system ; Optoelectronics ; Environmental chemistry ; Artificial intelligence ; Optics ; Quantum mechanics Conducting polymers and applications ; Advanced Computing and Algorithms ; Machine Learning in Materials Science
Authors: Tzu‐Hsuan Chao | Alexander Foncerrada | Patrick J. Shamberger | Daniel P. Tabor
Source: ChemRxiv (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

Low-Data Machine Learning Models for Predicting Thermodynamic Properties of Solid-Solid Phase Transformations in Plastic Crystals Plastic crystals, many of which are globular small molecules that exhibit transitions between rotationally ordered and rotationally disordered states, represent an important subclass of colossal barocaloric effect materials. The known set of plastic crystals is notably sparse, which presents a challenge to developing predictive thermodynamic models to describe new molecular structures. To predict the transformation entropy of plastic crystals, we developed a comprehensive database of tetrahedral plastic crystal molecules (neopentane analogs) and used several types of features, including chemical functional groups, molecular symmetry, DFT-calculated vibrational entropy, and energy decomposition analysis to train a machine learning model. To select the most relevant features, we used a correlation matrix to screen out highly correlated features and ran sure independence screening and sparsifying operator (SISSO) regression on the remaining features. The SISSO regression samples over combinatorial spaces, including operations and features, to find the relation- ship between material properties. Using a dataset of 49 plastic crystals and 37 non-plastic crystals based on a common tetrahedral geometry, we have demonstrated the effectiveness of this strategy. Furthermore, we applied this strategy to develop a regression model to predict transition entropy and enthalpy. The top 100 models from the operation space showed that the overall distribution of performance became narrower, sacrificing the top-performing model but avoiding the worst models. Using this approach, we identified the top-performing descriptors to further clarify the underlying mechanisms of the plastic crystal transformation. Phase (matter) ; Materials science ; Solid solution ; Thermodynamics ; Artificial intelligence ; Computer science ; Chemistry ; Metallurgy ; Physics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Kazuya Kohira | Shota Nakamura | Hiroki Fukuda | Kazuhiro Kyotani | Hirotsugu Ogi
Source: Scripta Materialia (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Two-step deep learning for decoding elastic constants of hexagonal-symmetry materials from resonant-spectrum image • High image-recognition ability of CNN is adopted for accurate estimation of hexagonal materials elastic constants. • The average Young’s modulus is found to serve as a primary parameter for the initial classification of the material elasticity. • We introduce the Blackmann-diagram like relationships for hexagonal materials. • Based on the hexagonal Blackmann-diagram strategy, the second step CNN allows prediction of the principal elastic constants with 5 % accuracy. • Introduction of missing modes in the dataset maintains the high accuracy up to 10 missing modes. We developed a deep learning (DL) framework based on convolutional neural networks (CNNs) to predict elastic constants of hexagonal materials by leveraging high image-recognition capability of CNNs. Resonant frequency data were converted into three-channel RGB images, referred to as ”elasticity images” for CNN training. Without mode identification, the trained models accurately predicted all five independent elastic constants. We reveal that the average Young modulus is a critical for classification of hexagonal materials based on their elasticity images. Furthermore, we extended the Blackman diagram, originally developed for cubic crystals, to hexagonal systems, enabling a substantial reduction of five-dimensional elastic-constant space. We then established a two-step DL scheme: first, classification using the average Young modulus, followed by regression of the five elastic constants in the classified average-Young-modulus class. The prediction error was approximately 5 % for the principal elastic constants and 1.5 % for the average Young modulus. Hexagonal crystal system ; Convolutional neural network ; Elastic modulus ; Deep learning ; RGB color model ; Elastic net regularization ; Elasticity (physics) ; Decoding methods ; Artificial neural network Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Composite Material Mechanics
Authors: Dishant Beniwal | Ashish Kaushik | Abhishek Tiwari | Pratik K. Ray
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine learning driven insights into yield strength of multi-principal element alloys Abstract In this work, we present a machine learning (ML) model for predicting the compressive yield strength of multi-principal element alloys (MPEAs). The model uses physical and thermodynamic alloy descriptors that capture various aspects of the alloying process such as lattice distortion, chemical interaction and intermetallic formation, and was used to explore yield strength variations in a variety of pseudo-ternary alloy systems viz. Zr x -(NbTa) y -(MoW) 1− x − y , W x -Ti y -(HfNbTaZr) 1− x − y and (MoNb) x -(AlCr) y -(FeNi) 1− x − y wherein it was validated along continuous composition pathways through comparison with experimentally observed yield strength values. The Compositional-Stimulus and Model-Response framework was used to decode the decision-making process of the ML model for obtaining alloy specific insights. Further, the model predictions were compared with hardness and phase predictions from other pre-trained ML models to establish consistency between different ML models. The model enables high-throughput screening for targeted yield strength and identifies key features for capturing yield strength variations in MPEAs. Graphical abstract Intermetallic ; Alloy ; Yield (engineering) ; Consistency (knowledge bases) ; Compressive strength ; Process (computing) ; Titanium alloy Machine Learning in Materials Science ; High Entropy Alloys Studies ; High Temperature Alloys and Creep
Authors: Prince K. Devadas | Balasankar Thirunavukkarasu | S. Natarajan
Source: International Journal of Pharmaceutics (article, 2025-12-17)
Field: Chemistry — Physical and Theoretical Chemistry

Combining crystal engineering and surface engineering to estimate the structure -functions relationship of Tafamidis solid state forms with the aid of machine learning Cocrystal ; Crystal engineering ; Hydrogen bond ; Crystal (programming language) ; Contact angle ; Intermolecular force ; Solubility ; Molecular recognition Crystallography and molecular interactions ; Polymer Surface Interaction Studies ; Drug Solubulity and Delivery Systems
Authors: Ahsan Javed | Sajid Ali
Source: Next research. (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

Machine learning-driven insights into excitonic effects in 2D materials Understanding excitonic effects in two-dimensional (2D) materials is critical for advancing their potential in next-generation electronic and photonic devices. In this study, we introduce a machine learning (ML)-based framework to predict exciton binding energies in 2D materials, offering a computationally efficient alternative to traditional methods such as many-body perturbation theory (GW) and the Bethe-Salpeter equation. Leveraging data from the Computational 2D Materials Database (C2DB), our ML models establish connections between cheaply available material descriptors and complex excitonic properties, significantly accelerating the screening process for materials with pronounced excitonic effects. Additionally, Bayesian optimization with Gaussian process regression was employed to efficiently filter materials with largest exciton binding energies, further enhancing the discovery process. Although developed for 2D systems, this approach is versatile and can be extended to three-dimensional materials, broadening its applicability in materials discovery. Materials science ; Nanotechnology Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Aarthi Sai Meghana Munnangi
Source: Jordan Journal of Chemistry (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Fusion of Experimental and Machine Learning: Predicting Refractive Index and Molecular Interactions in (Bmim.Triflate) with 1-Butanol and 1-Pentanol Mixtures This study delves into the refractive index of binary mixtures of (Bmim.Triflate) with 1-butanol and 1-pentanol, which is key to unlocking deeper insights into the molecular interactions that drive innovations in chemical synthesis, catalysis, and energy storage. Using high-precision experimental measurements, we ensured accurate refractive index data across varying mole fractions and temperatures. Traditional mixing rules, such as Arago-Biot and Lorentz‒Lorenz, were tested for refractive index prediction but showed limitations in accuracy. To overcome these challenges, we harnessed the power of machine learning (ML), employing a polynomial regression model to accurately capture the nonlinear relationships among the mole fraction, temperature, and refractive index. The ML models significantly outperform conventional methods, delivering highly precise predictions with low mean absolute errors (MAEs) and high R² values for both 1-butanol and 1-pentanol. This study highlights the transformative potential of machine learning in predicting complex physical properties, offering a flexible and more accurate approach to optimizing processes in cutting-edge applications. Refractive index ; Binary number ; Fusion ; Mixing (physics) ; Energy (signal processing) ; Regression ; Linear regression ; Index (typography) ; Experimental data Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Molecular spectroscopy and chirality
Authors: Xiao Wang | Xi Meng | Haiguang Huang | Yuh-Pring Lin | Zhiwei Yuan | Xiangming Wan | Xingrui Zheng | Peng Hu | Fenghua Zheng | Lu Li
Source: Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control (article, 2025-07-21)
Field: Materials Science — Metals and Alloys

Interpretable machine learning models for predicting corrosion behaviour in Titanium alloys Titanium alloys are prized for their exceptional corrosion resistance, yet controlling their corrosion behaviour remains challenging due to the complex interactions between alloying elements and corrosive media. This study developed a machine learning model to predict the corrosion current density and potential using data from 115 titanium alloys. The results indicate that the random forest (RF) model, using alloy composition and nine environmental factors as variables, is the most effective for predicting corrosion. The RF model outperforms others in predicting corrosion current density and potential on the test set, achieving the highest R² values (0.670 and 0.489) and the lowest mean absolute error values (0.367 and 0.107). Sixteen physical and chemical features and nine environmental features were used to create new features. Feature reduction results show that the first six principal components were extracted, with their cumulative variance contribution reaching 90.466%, surpassing the 90% threshold. SHAP analysis reveals that Icorr is a more sensitive and direct indicator of corrosion performance, with pH having the greatest impact (coefficient of 0.569). Overall, this study provides valuable insights into optimising titanium alloy compositions for specific corrosive environments through advanced predictive modelling and data-driven analysis. Corrosion ; Artificial intelligence ; Titanium ; Titanium alloy ; Materials science ; Metallurgy ; Machine learning ; Computer science ; Alloy Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Javeriya Sayed
Source: Global Trends in Science and Technology (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Frontiers in Organic Chemistry: From Reaction Mechanisms to Molecular Design The field of organic chemistry is developing as one of the key areas of contemporary science serving as a connection between the mechanistic knowledge and the molecular design and usability. The current review outlines the most important developments between reaction mechanisms and catalytic approaches, green and sustainable techniques, and new technologies like machine learning and automated synthesis. The accurate control of reactivity is possible with the help of mechanistic insights, whereas complex molecules can be construscted with high efficiency with the help of catalysis and functionalization. Greener strategies reduce environmental impact and computer aided technologies are faster in predictive design. Collectively, these trends broaden the horizons of organic chemistry, with the growth and development of innovation in the pharmaceuticals, materials, and energy field. The future directions are on the emphasis on interdisciplinary collaboration, predictive modeling, and the use of environmentally friendly chemical practices. Reactivity (psychology) ; Field (mathematics) ; Environmentally friendly ; Organic molecules ; Mechanism (biology) ; Sustainable development ; Key (lock) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Asymmetric Hydrogenation and Catalysis
Authors: Mingjun Han (6013943) | Yukai Zhang (10929419) | Taotao Yu (10204) | Guodong Du (1320129) | ChiYung Yam (1330032) | Ho-Kin Tang (12069938)
Source: Figshare (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Enhancing Accuracyand Feature Insights in HydrationFree Energy Predictions for Small Molecules with Machine Learning Accurately predicting solvation free energy and understanding its physical determinants are essential for studying solute behavior in solution. This work employs advanced machine learning techniques to enhance predictive accuracy and extract insights into the solvation free energy of small molecules. Traditional machine learning approaches, compared to deep learning, are lightweight and require fewer computational resources. Our analysis identifies molecular geometry and topology as critical factors in predicting alchemical free energy, aligning with the theory that surface tension is a key determinant, while highlighting the role of charge distribution in improving force field designs for molecular dynamics. We propose an improved machine learning scheme that integrates K-nearest neighbors for feature processing, ensemble modeling, and dimensionality reduction. This scheme achieves a mean unsigned error of 0.53 kcal/mol on the FreeSolv data set using only two-dimensional features without pretraining on large databases, offering substantial accuracy improvements. This lightweight approach provides a viable alternative to computationally intensive deep learning models and holds promise for broad applications in chemical predictions. Curse of dimensionality ; Feature (linguistics) ; Energy (signal processing) ; Field (mathematics) ; Experimental data ; Force field (fiction) ; Scheme (mathematics) ; Solvation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Xinyu Chen | Qian Chen | Liang Ma | Qionghua Zhou | Jinlan Wang
Source: Nano Research (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Decoding plasticity in van der Waals crystals via causality-driven interpretable machine learning Plastic deformation in van der Waals (vdW) crystals is essential for flexible and wearable electronics. However, its evaluation typically requires destructive testing or costly first-principles calculations. To address this, we present a causality-driven, interpretable machine learning framework that extracts accurate plasticity descriptors from easily accessible material features, avoiding empirical assumptions and high computational costs. By leveraging causal discovery, we clarify the roles of interlayer and intralayer interactions in plasticity and identify key features that govern plastic behavior. A causality-constrained symbolic regression method then generates a compact, physically meaningful descriptor with 96% accuracy in distinguishing plastic from brittle vdW materials. This descriptor enables rapid screen of large material databases and identify 29 previously unrecognized semiconductors with superior plasticity. This framework provides a generalizable approach to convert empirical data into actionable materials knowledge, accelerating functional materials design. van der Waals force ; Plasticity ; Brittleness ; Empirical research ; Support vector machine ; Key (lock) ; Experimental data ; Computation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks