jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tatchaphon Leelaprachakul | Yoshitaka Umeno
Source: Computational Materials Science (article, 2025-06-02)
Field: Materials Science — Polymers and Plastics

From coarse-grained molecular dynamics to machine learning: A comparative study of modeling techniques for predicting polycarbonate maximum stress from structural features This study explores the use of machine learning to predict the maximum stress of polycarbonate materials based on key structural parameters: polydispersity, radius of gyration, and molecular entanglements. These parameters are derived from coarse-grained molecular dynamics simulations of transverse constrained tension. We compare various machine learning algorithms, including multivariate linear regression, log–log regression, gradient boosted regression trees, extreme gradient boosted regression trees, and artificial neural networks. Model performance is evaluated using the coefficient of determination R 2 , root-mean-squared error, and computational efficiency. While traditional linear and log–log models achieve R 2 values of 0.77 and 0.83, respectively, boosted regression trees and artificial neural networks attain higher R 2 values of approximately 0.90. However, artificial neural networks require over four orders of magnitude more computation time compared to boosted regression tree models, which has R 2 of 0.89. To interpret feature importance, we employ Shapley additive explanations analysis on the extreme gradient boosted model. The radius of gyration is identified as the most influential parameter, highlighting its role in maintaining structural integrity during tensile deformation. A larger radius of gyration enhances load distribution along molecular chains, ensuring uniform stress transfer and improved mechanical performance. • Machine learning predicted maximum stress from coarse-grained simulation data. • Machine learning offered a faster alternative to molecular dynamics simulation. • Five different algorithms are assessed in terms of accuracy and efficiency. • Shapley explanations revealed the effects of structure on material properties. • The approach helps accelerate design by predicting material properties. Molecular dynamics ; Polycarbonate ; Stress (linguistics) ; Dynamics (music) ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Composite material ; Chemistry ; Computational chemistry ; Physics ; Biology Polymer crystallization and properties ; Injection Molding Process and Properties ; Machine Learning in Materials Science
Authors: Lijie Ding | Chi-Huan Tung | Bobby G. Sumpter | Wei‐Ren Chen | Changwoo Do
Source: Journal of Applied Crystallography (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Machine learning inversion from scattering for mechanically driven polymers A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters. Inversion (geology) ; Polymer ; Scattering ; Materials science ; Polymer science ; Optics ; Composite material ; Geology ; Physics ; Seismology Machine Learning in Materials Science
Authors: Jiao-Jiao Cheng | Caohui Duan | Yunzhen Du | Jizheng Duan | Meiling Qi | Yanwei Chen | Lei Yang | Wen‐Shan Duan | Sheng Zhang | Ping Lin
Source: Journal of Materials Science (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Neural network potential model molecular dynamics study on the effect of temperature and pressure on the lattice thermal conductivity of Mg3Sb2 Materials science ; Solid mechanics ; Thermal conductivity ; Molecular dynamics ; Condensed matter physics ; Lattice (music) ; Thermodynamics ; Chemical physics ; Composite material ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: H. Wang | Wenbin Liu | Fang Fang | Qiuyi Fu | Guobing Zhou | Zhen Yang
Source: Langmuir (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Atomic-Level Exploration of Local Structural Heterogeneity in Liquid Ga–In Alloys Using a Machine Learning Potential Liquid gallium-indium (Ga-In) alloys have emerged as an intriguing class of materials that combine the advantages of both metals and liquids. Their unique properties enable a range of potential applications in chemistry, physics, materials, and engineering. Thus, elucidating the local structural properties of liquid Ga-In alloys at the nanoscale is essential for understanding the processes occurring within them and for advancing new applications. Here, we performed an atomic-level investigation of local coordination behavior and structural ordering around Ga and In atoms in five liquid Ga-In alloys (i.e., Ga<sub>79.3</sub>In<sub>20.7</sub>, Ga<sub>83.2</sub>In<sub>16.8</sub>, Ga<sub>85.8</sub>In<sub>14.2</sub>, Ga<sub>91.8</sub>In<sub>8.2</sub>, and Ga<sub>96.9</sub>In<sub>3.1</sub>) using machine learning force field molecular dynamics simulations. Our findings reveal that the Ga<sub>79.3</sub>In<sub>20.7</sub>, Ga<sub>83.2</sub>In<sub>16.8</sub>, and Ga<sub>85.8</sub>In<sub>14.2</sub> alloys exhibit a continuous and predominant Ga-In phase, which is characterized by a spatial distribution of clustered In atoms. In stark contrast, the Ga<sub>91.8</sub>In<sub>8.2</sub> and Ga<sub>96.9</sub>In<sub>3.1</sub> alloys demonstrate the coexistence of pure Ga and Ga-In alloy phases, with In atoms forming dimers in the former and being atomically dispersed in the latter. More importantly, it is shown that there is a prominent difference in the local structural ordering surrounding Ga and In atoms in the first three Ga-In alloys, following a specific order of In-In > Ga-In > Ga-Ga, which is ascribed to the differences in interaction strengths. Overall, this study presents the first theoretical evidence of local structural heterogeneity in liquid Ga-In alloys and offers new insights into the nanoscale structural details in liquid Ga-based alloys. Nanoscopic scale ; Molecular dynamics ; Alloy ; Field (mathematics) ; Local structure ; Range (aeronautics) ; Order (exchange) Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys ; Material Dynamics and Properties
Authors: Dong-Hee Shin | Young-Han Son | Tae-Eui Kam
Source:  (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Diverse Sharpness-Aware Minimization for Machine Learning Force Fields in Semiconductor Simulations The growing demand for advanced semiconductor chips has driven the need for rapid and accurate material property prediction. While density functional theory (DFT) provides high accuracy, its computational cost limits practical applications. Machine learning force fields (MLFFs) offer faster alternatives but struggle with out-of-distribution (OOD) generalization when encountering unseen atomic structures. In this work, we investigate the use of sharpness-aware minimization (SAM) and its variants to improve MLFF generalization performance. Experiments on the SAMD23 benchmark demonstrate that SAM and its variants significantly enhance generalization on OOD datasets, with physics-informed SAM achieving the best performance. Generalization ; Minification ; Benchmark (surveying) ; Property (philosophy) ; Semiconductor industry ; Semiconductor device fabrication ; Semiconductor Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Electron Microscopy Techniques and Applications
Authors: Jules Bertrandie | Mehmet A. Noyan | Luis Huerta Hernandez | Anirudh Sharma | Derya Baran
Source: Advanced Energy Materials (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

From Experimental Values to Predictive Models: Machine Learning‐Driven Energy Level Determination in Organic Semiconductors Abstract The precise determination of ionization energy (IE) and electron affinity (EA) is crucial for the development and optimization of organic semiconductors (OSCs). These parameters directly impact the performance of organic electronic devices. Experimental techniques to measure IE and EA, such as UV photoelectron spectroscopy (UPS) and low‐energy inverse photoelectron spectroscopy (LE‐IPES), are accurate but resource‐intensive and limited by their availability. Computational approaches, while beneficial, often rely on gas‐phase calculations that fail to capture solid‐state phenomena, leading to discrepancies in practical applications. In this work, machine learning methods are used to develop a chained model for estimating solid‐state IE and EA values. By implementing a transfer learning strategy, the challenge of limited experimental data is effectively addressed, utilizing a large database of intermediate properties to enhance model training. The efficacy of this model is demonstrated through its performance achieving mean absolute errors of 0.13 and 0.14 eV for IE and EA, respectively. The model has also been tested on an external validation dataset comprising newly measured molecules. These findings highlight the potential of machine learning in OSC research, significantly enhancing property accessibility and accelerating molecular design and discovery. Materials science ; X-ray photoelectron spectroscopy ; Organic semiconductor ; Semiconductor ; Machine learning ; Inverse ; Electron affinity (data page) ; Resource (disambiguation) ; Work (physics) ; Computer science ; Ionization energy ; Ionization ; Artificial intelligence ; Molecule ; Optoelectronics ; Physics ; Nuclear magnetic resonance ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Molecular Junctions and Nanostructures
Authors: Jiang Deng | Shiqi Guo | Yuejin Li | Penglu Wang | Su Chen | Jun‐Min Liu | Xingchi Li | Guang Yan | Sha Wang | Zaisheng Jin | Ming Xie | Dengsong Zhang
Source: Chemical Engineering Science (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Insight into the alkali metal poisoning sensitivity of V2O5-WO3/TiO2 catalysts for NOx abatement via machine learning and in situ Raman spectroscopy Catalysis ; Lewis acids and bases ; Redox ; Density functional theory ; Adsorption ; NOx ; Alkali metal ; Metal Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Transition Metal Oxide Nanomaterials
Authors: Mohammed A. Atiea | Reham Reda | Sabbah Ataya | Mervat Ibrahim
Source: Processes (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach High-performance copper alloys are crucial for integrated circuit lead frames due to their high density, multifunctionality, and low cost. High-performance copper alloys typically address the competing issues of high strength and high electrical conductivity through alloying and processing control methods. However, the traditional methods for developing these alloys are time-consuming, expensive, and complex processes. This study utilizes Explainable AI by employing machine learning (ML) and deep learning (DL) techniques to predict the hardness (HRC) and electrical conductivity (mS/m) based on the alloy composition, including Cr, Zr, Ce, and La, and the processing parameters, namely the aging time, of Cu-Cr-Zr alloys. A comprehensive dataset of 47 experimental Cu-Cr-Zr alloy samples, derived from prior experimental studies, was analyzed using feature engineering, correlation analysis, and explainability methods such as SHapley Additive exPlanations (SHAP). Various ML models, including ensemble methods like XGBoost, CatBoost, and AdaBoost, were evaluated for their predictive performance. The feature importance analysis revealed that the aging time and Zr content significantly influence the hardness, followed by Ce content, while Cr and La contents reveal a weak contribution to hardness values. Electrical conductivity is predominantly controlled by aging time, with a weak negative influence of the alloying elements. These findings align well with metallurgical principles, where microstructural refinement and precipitation behavior dictate the hardness and conductivity of Cu-Cr-Zr alloys. Hyperparameter tuning and model stacking further enhanced the predictive accuracy, with the final stacked models achieving R2 scores of 0.8762 for hardness within a training time of 1.739 s and 0.8132 for electrical conductivity within a training time of 1.091 s. These findings demonstrate the effectiveness of ML-driven approaches in material property predictions, providing valuable insights for material design and property processing parameter optimization. Hyperparameter ; Stacking ; Feature (linguistics) ; Machine learning ; Artificial intelligence ; Computer science ; Feature engineering ; Materials science ; Chemistry ; Deep learning Machine Learning in Materials Science
Authors: Zhaohui Wang | Xin Li | Song Wang | Chuqiao Wang | Zihuan Wang | Xiaoming Peng
Source: Chemical Research in Chinese Universities (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Catalyst Design for Advanced Oxidation Processes: Efficient and Streamlined Development Catalysis ; Computer science ; Systems engineering ; Process engineering ; Chemical engineering ; Manufacturing engineering ; Engineering ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Mohammadamin Tavakoli | Yin Ting T. Chiu | Annmarie G. Carlton | David L. Van Vranken | Pierre Baldi
Source: Journal of Chemical Information and Modeling (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive framework based on the interaction of molecular orbitals that operates on mechanistic-level radical reactions. Given our chemistry-aware model, all predictions are provided with different levels of interpretability. Our models are trained and evaluated using the RMechDB database of radical reaction steps. Our model predicts the correct orbital interaction and products for 96% of the test reactions in RMechDB. By chaining these predictions, we perform a pathway search capable of identifying all intermediates and byproducts of a radical reaction. We test the pathway search on two classes of problems in atmospheric and polymerization chemistry. RMechRP is publicly available online at https://deeprxn.ics.uci.edu/rmechrp/. Artificial intelligence ; Deep learning ; Computer science ; Chemistry ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Qingchun Yang | Lei Zhao | Qiwen Guo | Zhao Wang | Dawei Zhang
Source: Industrial & Engineering Chemistry Research (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

A Hybrid Encoding-Enhanced Machine Learning and Multioptimization Framework for the Optimal Catalyst Design of the Methane-to-Ethylene Process The oxidative coupling of methane to ethylene (OCMtE) technology has garnered significant attention due to its potential economic value and environmental benefits. However, the industrialization process is significantly impeded by the challenges associated with low selectivity and limited catalyst yield. Therefore, a Hybrid Encoding-enhanced Catalyst information extraction and multiobjective Optimization-based Machine Learning (HECO-ML) framework is proposed to expedite the screening and optimization of catalysts for this process with improved efficiency, accuracy, and adaptability. The advantages of combining word vector embedding encoding and one-hot encoding techniques in hybrid encoding have been quantitatively demonstrated to comprehensively capture potential features and semantic information across various categories of OCMtE catalysts, thereby providing higher-quality variable information for training machine learning models. After constructing an optimized data set comprising 850 data points (divided into 17 input features and 3 output features), four machine learning models were developed and compared for the OCMtE processes. The results suggest that the categorical boosting model demonstrates outstanding performance in accurately predicting the CH4 conversion and C2H4 selectivity, with an R2 value exceeding 0.90. The importance and interaction mechanism of each input feature in the OCMtE process for the outputs are determined through interpretable analyses, which include feature importance analysis and partial dependency analysis. It indicates that reaction conditions and catalyst properties contribute the largest to the outputs of the OCMtE process. Finally, the preferred model combined with the adaptive genetic optimization algorithm successfully predicted six novel OCMtE catalysts, which exhibited higher C2H4 yields. The predicted outcomes of the proposed HECO-ML framework exhibit strong consistency with experimental data across a range of catalysts. Ethylene ; Methane ; Process (computing) ; Catalysis ; Process engineering ; Encoding (memory) ; Chemical engineering ; Computer science ; Chemistry ; Organic chemistry ; Engineering ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Nathan A. Mahynski | David A. Sheen | Rick L. Paul | Huaiyu H. Chen‐Mayer | Vincent K. Shen
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2025-05-27)
Field: Chemistry — Analytical Chemistry

Encoding PGAA spectra as images for material classification with convolutional neural networks Abstract We trained deep convolutional neural networks (CNN) to classify a material based on its prompt gamma ray activation analysis (PGAA) spectrum. We focused on two dimensional (2D) models to leverage abundant open-source models pre-trained on other computer vision tasks for transfer learning. This allows models to be built with a relatively small number of trainable parameters. Moreover, CNNs can be explained naturally using class activation maps and can be equipped with out-of-distribution tests to identify materials which were not present in its training set. Together, these features suggest such models may be excellent candidates for automated material identification in real-world scenarios. Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Encoding (memory) ; Spectral line ; Computer science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Image Processing and 3D Reconstruction
Authors: John S. Weeks | Aaron P. Stebner
Source: Integrating materials and manufacturing innovation (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Efficient Multiscale Simulations of Incremental Sheet Forming Using Machine Learning Surrogate Models for Crystal Plasticity Abstract Multiscale crystal plasticity modeling of metal forming offers potential for effective design processes that consider microstructural evolution during significant plastic deformation. However, conventional multiscale methods for forming are expensive due to complex loading conditions and high-cost microscale models, making them challenging to apply in practice. These methods can be significantly accelerated through use of low-cost machine learning surrogate models for the microscale response. While these techniques have been demonstrated for simple load cases, they have not yet been demonstrated for manufacturing applications with full-field texture results. In this work, we develop efficient multiscale simulation workflows for manufacturing through single-point incremental forming using recurrent neural networks for constitutive response and texture evolution of a crystal plasticity model. This approach achieves up to a 63.6x increase in speed compared to conventional techniques and is demonstrated for an aluminum alloy with two unique forming paths. These workflows yield consistent trends between forming force and thickness variation, texture results in agreement with ground truth models, and permit extraction of the full-field texture evolution over the entire formed part. This enables efficient multiscale trade studies and optimization of local microstructures in industrial forming applications. Microscale chemistry ; Texture (cosmology) ; Plasticity ; Forming processes ; Workflow ; Microstructure ; Crystal plasticity ; Deep drawing Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Guozhi Zeng | Yexin Yang | Xiaoqiang Zhai
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Study on the catalytic adsorption performance of SO₂ based on O₂ adsorption energy optimization-optimization design of single-atom materials with integrated DFT and machine learning Adsorption ; Catalysis ; Density functional theory ; Molecule ; Process (computing) ; Response surface methodology Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Brandon Wilfong | Alexander New | Gregory Bassen | Wyatt Bustine | Michael J. Pekala | Nam Q. Le | Karun K. Rao | Elizabeth A. Pogue | Eddie Gienger | Michał J. Winiarski | Tyrel M. McQueen | Christopher D. Stiles
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ternary materials discovery using human-in-the-loop generative machine learning Machine learning (ML) approaches to materials discovery are limited by data curation, availability, and bias. These issues can be addressed through the generation of new data points representing novel material compositions and/or structures. We demonstrate the implementation of this process to produce and subsequently determine the stability of novel materials using a generative ML model. Furthermore, we successfully synthesize two predicted materials, LiZn<sub>2</sub>Pt and NiPt<sub>2</sub>Ga, and use these predictions to extrapolate to other unreported ternary compounds in the Heusler family. Our work demonstrates and expands the use of generative ML models to successfully discover and synthesize novel materials. This has broad implications for material exploration by design, as previous ML approaches to materials discovery were biased by the limits of known phase spaces and experimentalist bias, and has the potential to enable inverse-design of materials with targeted properties. Generative grammar ; Ternary operation ; Workflow ; Generative model ; Stability (learning theory) ; Computer science ; Loop (graph theory) ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties