jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amirhossein Amiri-Hezaveh | Adrián Buganza Tepole
Source: ArXiv.org (preprint, 2025-07-23)
Field: Materials Science — Materials Chemistry

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids We propose a data-driven constitutive framework for anisotropic damage mechanics based on the second-order damage tensor approach for both compressible and incompressible materials. The formulation is thermodynamically consistent and satisfies the Clausius-Duhem inequality. The strain energy density potentials are expressed as isotropic functions of the right Cauchy-Green deformation tensor, along with structural tensors that encode anisotropy either present in the virgin material or resulting from damage. To guarantee the polyconvexity condition, non-decreasing convex neural networks with inputs that ensure polyconvexity are used to parameterize the strain energy density potentials. The model vanishes in the undeformed state, fulfilling the normality condition. In contrast to classical [1-d] damage models, the expressiveness of the new data-driven model is enhanced by employing a family of nonlinear, convex, decreasing functions to capture the effect of damage. Damage evolution is governed through a damage potential, where the corresponding threshold is defined in terms of the damage conjugate forces. As a special case of the general formulation, a new anisotropic generic format is introduced to predict constitutive responses under damage-induced anisotropy in initially isotropic materials. To reduce the computational burden during training, a decoupled training scheme is introduced, and its accuracy is demonstrated in all numerical examples. These include benchmarks for incompressible isotropic, transversely isotropic, and compressible orthotropic materials. The framework is also validated against experimental data capturing anisotropic Mullins-type damage. Orthotropic material ; Isotropy ; Anisotropy ; Compressibility ; Strain energy density function ; Constitutive equation ; Transverse isotropy ; Tensor (intrinsic definition) High-Velocity Impact and Material Behavior ; Model Reduction and Neural Networks ; Metallurgy and Material Forming
Authors: Chao Suo | Xiaolu Wang | Jiarong Liu | Kun Zhang | Shaohui Guo | Xiao-Chuan Duan | Xian‐Ming Zhang
Source: CHAIN (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Artificial Neural Network-Driven Robust Model for Estimating Hydrogen Bonded Formation Potential Towards Rational Design of MOFs Proton Conductors Significant advancements have been made in metal-organic frameworks (MOFs) for proton conductivity in proton exchange membrane fuel cells; however, a comprehensive and rational design road map for these materials remains under explored, particularly regarding the quantitative contributions of internal structural factors (proton donor-acceptor types and spatial distances) and external environmental factors (temperature and humidity) to hydrogen-bond formation. In this study, the hydrogen-bonded network formation potential (Φ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</inf>) is proposed based on these factors to establish structure-property relationship of MOFs via artificial neural network (ANN) model, by which a linear function relationship has been fitted to facilitate the rational design of MOFs, providing a simple assessment of proton conductivity performance in reverse. As a proof of concept, UiO-66-NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>H with varying group ratios was fabricated, and UiO-66-NH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>H-1:5 exhibited favorable proton conductivity (0.08 S·cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>) due to the favorable matching of Brönsted acid-base pairs, which facilitates the extensive formation of hydrogen-bonded networks. The proposed hydrogen-bonded network formation potential is expected to advance the design and synthesis of MOF-based proton-conductive materials, serving as a valuable index for the proton exchange membrane fuel cell research community. Rational design ; Proton ; Conductivity ; Proton exchange membrane fuel cell ; Rational function ; Artificial neural network ; Function (biology) ; Matching (statistics) Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials ; Advancements in Solid Oxide Fuel Cells
Authors: Timothée Devergne | Vladimir Kostić | Massimiliano Pontil | Michele Parrinello
Source: ArXiv.org (preprint, 2025-08-02)
Field: Materials Science — Materials Chemistry

The seeds of the future are in the present: A blind exploration of metastable states In this work, we present a novel type of molecular dynamics simulation that aims at discovering, in a blind way, new metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural networks to generalize. Contrary to most other enhanced sampling methods, which need previous knowledge of the reactive process, we are able to discover in a blind way new metastable states, overcoming otherwise insuperable kinetic bottlenecks. We illustrate the workings of the method with a number of instructive examples. Metastability ; Sampling (signal processing) ; Space (punctuation) ; Kinetic energy ; Sampling bias ; Type (biology) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Andrey B. Lisitsyn | I M Chernukha | Marina Ņikitina | V.A. Pchelkina
Source: Food Processing Techniques and Technology (article, 2025-12-25)
Field: Chemistry — Analytical Chemistry

Chemometrics in Laboratory Data Analysis Food is a complex system that requires holistic quality assessment. Chemometrics uses mathematical, statistical, and computer science methods to analyze and interpret chemical data, which means good prospects for food quality evaluation. This review covered Russian and international publications indexed in Scopus, PubMed, MEDLINE, Web of Knowledge, Google Scholar, IEEE Xplore, Science Direct, and eLIBRARY.RU (RSCI). The search queries included such keywords as chemometrics; chemometric methods; principal component analysis; PLS (projection to latent structures); artificial neural network (ANN); multivariate classification; multivariate data analysis. The main chemometric tools applied to food systems included hierarchical cluster analysis (HCA), principal component analysis (PCA), latent structures-discriminant analysis (PLS-DA), projections to latent structures (PLS), quadratic projection to latent structures (QPLS), multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and ensemble model prediction (RF, XGBoost). The PCA proved to be the most popular chemometric method applied in the food industry. However, combinations of methods were always more effective than a single one. The KNN methods appeared to be quite unreliable. Combinations of chemometric methods demonstrate the best prospects, e.g., PCA + PLS-DA + ANN or PCA + PLS-DA +KNN. If combined with instrumental tools, they are able to improve analytical accuracy and provide effective management approaches, thus ensuring sustainable food industry. Principal component analysis ; Chemometrics ; Multivariate statistics ; Artificial neural network ; Projection pursuit ; Latent variable ; Projection (relational algebra) ; Partial least squares regression ; Pattern recognition (psychology) ; Component (thermodynamics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Nishchhal Agarwal
Source:  (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

AI-Driven Material Discovery for Sustainable Solar Cells in Rural Electrification Globally, rural areas still face many difficulties in accessing reliable, clean, and sustainable electricity. Solar energy is a green source of electricity which is good and cheap. But the current solar cell technology is not so good in the aspects of the efficiency of energy conversion, price, and the ability to adapt to rural conditions. The work presented here is an investigation of how computer algorithms can improve the efficiency of the materials prepared by AI designed for the production of solar cells that are more compatible with sustainable electrification in the countryside. The machine learning strategies as well as the databases of materials can be used to find new compounds of materials with the regulated photovoltaic properties that are quite reliable and reduce the number of experiments needed from thousands to a few. This document lays out a plan for the creation of data, the configurations of the models, and the prediction systems that have been utilized in the process of searching for new organic and perovskite solar cells’ materials. The paper also discusses the inclusion of sustainable factors in the definition of the AI framework. Among these are the local availability of resources, the toxic parameters, and the local costs of making them. The concluding part deals with the manifestation of Indian rural areas as the first candidate places where the AI implementation can be the most benefitting due to the huge need for such technology in rural areas of the country. AI-led material search technologies significantly accelerate the identification of high performance and environmentally friendly solar power cell compounds that shrink the search for new materials from several years to just a few weeks, thus offering an innovative solution for the wide-spread rural electrification of a country. Photovoltaic system ; Rural electrification ; Electrification ; Identification (biology) ; Electricity ; Work (physics) ; Rural area ; Process (computing) ; Solar energy Machine Learning in Materials Science ; Photovoltaic Systems and Sustainability ; Hybrid Renewable Energy Systems
Authors: Abdelrahman Amin | Ibrahim Awad | Hamdy Ibrahim
Source: Materials Today Communications (article, 2025-12-09)
Field: Materials Science — Biomaterials

Corrosion prediction for MAO-coated magnesium using machine learning approach Recently, magnesium alloys have attracted significant attention due to their ability to degrade within the human body, making them suitable for temporary biomedical implant applications. However, their rapid corrosion rates pose a challenge. To mitigate this issue, various techniques, such as micro-arc oxidation (MAO) coatings, have been employed. MAO effectively controls the degradation rate by forming a complex ceramic coating on the magnesium surface. Studies have shown that the corrosion behavior of ceramic-coated magnesium substrates is influenced by multiple factors, including electrolyte composition, process parameters, and other related variables. In this context, machine learning (ML) is a powerful approach that can be used to predict corrosion behavior based on such factors. This study presents the first application of ML techniques to predict the corrosion characteristics of MAO-coated magnesium implants based on potentiodynamic polarization (PDP) test results. Using data collected from the literature, data cleaning and transformation were performed before training the models. Multiple ML models were evaluated for forecasting corrosion current density and corrosion potential, with the Random Forest model achieving the best overall performance in terms of mean absolute percentage error (MAPE). Accuracy limitations were addressed by applying a logarithmic transformation to the current density values, which significantly improved prediction accuracy due to data range compression. Predicting log-transformed values directly yielded superior results compared to inverse-log transformations, particularly for current density, while predictions of corrosion potential remained stable across methods. Defining a common output range (10 −6 to 10 −7 A/cm²) further enhanced current density predictions but slightly reduced the accuracy for corrosion potential predictions. These findings highlight the importance of selecting prediction strategies based on specific performance priorities. Future work will integrate experimental validation and extend the approach to other coating systems. • Random Forest model best predicts PDP outcomes for MAO-coated Mg. • Log-transforming current density greatly enhances prediction accuracy. • Common range 10 −06 -10 −07 A/cm² improves current density predictions. • Corrosion potential predictions remain stable across transformations. Corrosion ; Magnesium ; Polarization (electrochemistry) ; Coating ; Transformation (genetics) ; Electrolyte ; Logarithm ; Current density Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors
Authors: Hongxin Xiang | Jun Xia | Xin Jin | Wenjie Du | Li Zeng | Xiangxiang Zeng
Source:  (article, 2025-09-01)
Field: Materials Science — Surfaces, Coatings and Films

Electron Density-enhanced Molecular Geometry Learning Electron density (ED), which describes the probability distribution of electrons in space, is crucial for accurately understanding the energy and force distribution in molecular force fields (MFF). Existing machine learning force fields (MLFF) focus on mining appropriate physical quantities from the atom-level conformation to enhance the molecular geometry representation while ignoring the unique information from microscopic electrons. In this work, we propose an efficient Electronic Density representation framework to enhance molecular Geometric learning (called EDG), which leverages images rendered from ED to boost molecular geometric representations in MLFF. Specifically, we construct a novel image-based ED representation, which consists of 2 million 6-view images with RGB-D channels, and design an ED representation learning model, called ImageED, to learn ED-related knowledge from these images. We further propose an efficient ED-aware teacher and introduce a cross-modal distillation strategy to transfer knowledge from the image-based teacher to the geometry-based students. Extensive experiments on QM9 and rMD17 demonstrate that EDG can be directly integrated into existing geometry-based models and significantly improves the capabilities of these models (e.g., SchNet, EGNN, SphereNet, ViSNet) for geometry representation learning in MLFF with a maximum average performance increase of 33.7%. Code and appendix are available at https://github.com/HongxinXiang/EDG Representation (politics) ; Construct (python library) ; Focus (optics) ; Energy (signal processing) ; Field (mathematics) ; Distribution (mathematics) ; Feature learning ; Electron Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Yingjian Xin | Peipei Fang | Hongpeng Wang | Xiong Wan
Source: BioResources (article, 2025-04-28)
Field: Chemistry — Organic Chemistry

Application of MTF-SKNet for wood species classification using mid-infrared spectroscopy With the recovery of the economy and growth of living standards, the demand for wood furniture is increasing, leading to a focus on wood quality and market value. Mid-infrared (MIR) spectroscopy, which characterizes molecular vibrations, is well-suited for wood classification due to its ability to identify molecular structures. This study utilizes a Fourier Transform Infrared (FTIR) spectrometer to classify 31 wood species based on their commercial categories. While the basic composition of wood species is similar, spectral data are overall close, necessitating a robust approach for accurate identification. To address this, a two-dimensional transformation of the spectral data is performed, to convert wavenumber sequence and state transition probabilities (quantized intensity levels) of spectra into a matrix, followed by deep learning classification using the transformed data. This resulted in the development of the MTF-SKNet model, achieving a classification accuracy of 93% for wood species. The model demonstrated strong generalization performance, reaching 96% accuracy in classifying the rosewood category of woods. Spectroscopy ; Infrared spectroscopy ; Materials science ; Infrared ; Remote sensing ; Analytical Chemistry (journal) ; Environmental science ; Optics ; Environmental chemistry ; Geology ; Chemistry ; Physics ; Organic chemistry ; Astronomy Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection
Authors: Diego Ontiveros (21987107) | Sergi Vela (1824829) | Francesc Viñes (1276491) | Carmen Sousa (2413285)
Source: Figshare (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

MXgap: A MXeneLearning Tool for Bandgap Prediction The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar-to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications. Band gap ; MXenes ; Python (programming language) ; Pipeline (software) ; Renewable energy ; Absorption (acoustics) ; Process (computing) ; Carbide MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Xudi Pang | Zhiyu Xu | Zhiwei Li | Jinling Liu | Huimin Tan | Huiming Cao | Yuzhen Sun | Yong Liang
Source: Journal of environmental chemical engineering (article, 2025-10-25)
Field: Chemistry — Electrochemistry

AsRTNet: A multimodal feature fusion model for improved prediction of retention time of arsenic compounds in environmental and metabolism samples Interpretability ; Arsenic ; Feature (linguistics) ; Identification (biology) ; Pattern recognition (psychology) ; Retention time ; Fusion Electrochemical Analysis and Applications ; Arsenic contamination and mitigation ; Remote-Sensing Image Classification
Authors: Diego Ontiveros Cruz | Sergi Vela Llausí | Francesc Viñes Solana | Carmen Sousa Romero
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

MXgap: A MXene Learning Tool for Bandgap Prediction The increasing demand for clean and renewable energy has intensified the exploration of advanced materials for efficient photocatalysis, particularly for water splitting applications. Among these materials, MXenes, a family of two-dimensional (2D) transition metal carbides and nitrides, have shown great promise. This study leverages machine learning (ML) to address the resource-intensive process of predicting the bandgap of MXenes, which is critical for their photocatalytic performance. Using an extensive data set of 4356 MXene structures, we trained multiple ML models and developed a robust classifier-regressor pipeline that achieves a classification accuracy of 92% and a mean absolute error (MAE) of 0.17 eV for bandgap prediction. This framework, implemented in an open-source Python package, MXgap, has been applied to screen 396 La-based MXenes, identifying six promising candidates with suitable band alignments for water splitting whose optical properties were further explored via optical absorption and solar to-hydrogen (STH) efficiency. These findings demonstrate the potential of ML to accelerate MXene discovery and optimization for energy applications. Band gap ; MXenes ; Python (programming language) ; Pipeline (software) ; Renewable energy ; Solar energy ; Absorption (acoustics) ; Process (computing) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Hariz Zoran Farooq | M. Atif | Eilaf Azeem
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Designing a Carbon Quantum Dot-Based Intrinsic Layer for a PIN Photodiode Tuned to Human L, M, and S Cone Responses Photodiode ; Quantum dot ; Cone (formal languages) ; Optoelectronics ; Physics ; Layer (electronics) ; Materials science ; Nanotechnology ; Computer science Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Min Soo Kim | Young Eun Song | Yuri Kim | Soo Youn Lee | Jinhee Heo | Taehoon Kim | Changman Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Electrochemistry

Solar-assisted bioelectrochemical recovery of silver from synthetic wastewater with mechanistic and machine-learning insights Microbial fuel cell ; Wastewater ; Electrochemistry ; Cathode ; Oxide ; Sewage treatment ; Transition metal ; Deposition (geology) ; Anode Electrochemical Analysis and Applications
Authors: Yong Qiu | Lei Wang | Letian Chen | Yun Tian | Zhen Zhou | Jianzhong Wu
Source: ArXiv.org (preprint, 2025-05-09)
Field: Chemistry — Inorganic Chemistry

A Mechanism-Guided Inverse Engineering Framework to Unlock Design Principles of H-Bonded Organic Frameworks for Gas Separation The diverse combinations of novel building blocks offer a vast design space for hydrogen-boned frameworks (HOFs), rendering it a great promise for gas separation and purification. However, the underlying separation mechanism facilitated by their unique hydrogen-bond networks has not yet been fully understood. In this work, a comprehensive understanding of the separation mechanisms was achieved through an iterative data-driven inverse engineering approach established upon a hypothetical HOF database possessing nearly 110,000 structures created by a material genomics method. Leveraging a simple yet universal feature extracted from hydrogen bonding information with unambiguous physical meanings, the entire design space was exploited to rapidly identify the optimization route towards novel HOF structures with superior Xe/Kr separation performance (selectivity &gt;103). This work not only provides the first large-scale HOF database, but also demonstrates the enhanced machine learning interpretability of our model-driven iterative inverse design framework, offering new insights into the rational design of nanoporous materials for gas separation. Interpretability ; Design elements and principles ; Gas separation ; Nanoporous ; Rational design ; Inverse ; Rendering (computer graphics) ; Separation of concerns Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies