jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Liyuan Wang | Xiangyun Ge | Richard Johnston | Li Shi
Source:  (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Understanding of Statistical Descriptors and Accelerated Calculation Methods through CNN In the field of materials science, statistical descriptors are essential for characterizing and understanding the microstructural properties of materials. However, traditional methods for calculating these descriptors usually rely on numerical simulations or analytical calculations, which are complex and time-consuming, limiting their practicality in large-scale applications. In this study, a new method based on convolutional neural networks (CNNs) is proposed to accelerate the calculation of statistical descriptors. We designed a CNN model that takes a two-dimensional binarized material microstructure image as input and predicts the key points of its two-point correlation function and pore size distribution. Experimental results on Fontainebleau sandstone(FS) materials show that the prediction accuracy of the CNN model is almost the same as that calculated by traditional methods, and the calculation speed is increased by 20 times. Therefore, this study provides an effective way to quickly and accurately calculate the statistical descriptors of material microstructure, which has important theoretical significance and practical application value. Convolutional neural network ; Field (mathematics) ; Function (biology) ; Statistical model ; Key (lock) ; Limiting ; Statistical analysis ; Pattern recognition (psychology) Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced Neural Network Applications
Authors: Kai Ren (1457413) | Zilong Zhuang (12505635) | Ke Wang (82395) | Yu Wei (365236) | Jianping Li (300447) | Huabing Shu (1377507)
Source: Figshare (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Machine LearningSpeeds up Predicting the MechanicalProperties of SiC Nanophononic Heterostructures This work investigates the mechanical properties of a silicon carbide (SiC) nanophononic heterostructure (NPH) tuned by temperatures and pore geometries, using molecular dynamics (MD) simulations in conjunction with the machine learning method. The SiC NPH, constructed with pure SiC and phononic crystal pores, exhibits temperature-dependent fracture behavior, showing a decrease in mechanical strength decreasing as the temperature increases from 50 to 500 K. The fracture strength and strain are markedly influenced by pore size, with a particular emphasis on pore length, whereas pore width exerts a negligible influence. In addition, armchair interfaces can cause a higher mechanical strength compared with zigzag interfaces. The presence of larger phononic pores amplifies stress concentrations and thermal effects, consequently resulting in more significant reductions in mechanical performance. More importantly, a random forest model has been developed to accurately predict fracture characteristics of the SiC NPH, achieving a high degree of precision (<i>R</i><sup>2</sup> = 0.99) and offering a 600-fold increase in computational speed compared to traditional MD methods. These findings provide valuable insights for designing robust SiC-based nanodevices with tunable mechanical properties. Zigzag ; Silicon carbide ; Heterojunction ; Fracture (geology) ; Mechanical strength ; Stress (linguistics) ; Work (physics) ; Crystal (programming language) Thermal properties of materials ; Advanced ceramic materials synthesis ; Boron and Carbon Nanomaterials Research
Authors: Yang Deng | Simiao Ren | Jordan M. Malof | Willie J. Padilla
Source:  (article, 2025-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Inverse Design of Metamaterials and Metasurfaces Metamaterials and metasurfaces - here termed artificial electromagnetic materials (AEMs) - offer unprecedented control over the scattering of electromagnetic waves. Often the design of AEMs is cast as a challenging inverse problem, where an electromagnetic response is desired and the AEM which gives rise to it is sought. Inverse design problems are difficult because they are often ill-posed. Over the last several years deep learning has successfully been applied to solving AEM inverse problems and significant advance has been made. We overview various deep learning methods used to study inverse problems - termed deep inverse models (DIMs) - and compare their performance on benchmark datasets. It is found that the neural adjoint (NA) method is the most accurate DIM on problems investigated, whereas the popular Tandem and conditional variational autoencoder (cVAE) DIMs are often the fastest. We also summarize the different DIM approaches and explain the reasons for their varied success, and give an outlook of this exciting research area. Inverse ; Inverse problem ; Metamaterial ; Benchmark (surveying) ; Inverse scattering problem ; Autoencoder ; Electromagnetics Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Microwave Imaging and Scattering Analysis
Authors: Sean Colby | Jessica Bade | Amy Jystad | Peter S. Rice | Yehia Ibrahim | Simone Raugei | Christopher Harrilal
Source: Analytical Chemistry (article, 2025-09-11)
Field: Chemistry — Analytical Chemistry

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra We present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule's functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on <i>in silico</i> techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity. Pattern recognition (psychology) ; Similarity (geometry) ; Matching (statistics) ; Spectral line ; Identification (biology) ; Range (aeronautics) ; Infrared ; Infrared spectroscopy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Liang Wang | Rong Yu | Tingyang Xu | Z. Zhong | Zhiyuan Liu | Pengju Wang | Deli Zhao | Qiang Liu | Shu Wu | Yang Zhang
Source: ArXiv.org (preprint, 2025-07-09)
Field: Materials Science — Materials Chemistry

DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models Molecular structure elucidation from spectra is a fundamental challenge in molecular science. Conventional approaches rely heavily on expert interpretation and lack scalability, while retrieval-based machine learning approaches remain constrained by limited reference libraries. Generative models offer a promising alternative, yet most adopt autoregressive architectures that overlook 3D geometry and struggle to integrate diverse spectral modalities. In this work, we present DiffSpectra, a generative framework that formulates molecular structure elucidation as a conditional generation process, directly inferring 2D and 3D molecular structures from multi-modal spectra using diffusion models. Its denoising network is parameterized by the Diffusion Molecule Transformer, an SE(3)-equivariant architecture for geometric modeling, conditioned by SpecFormer, a Transformer-based spectral encoder capturing multi-modal spectral dependencies. Extensive experiments demonstrate that DiffSpectra accurately elucidates molecular structures, achieving 40.76% top-1 and 99.49% top-10 accuracy. Its performance benefits substantially from 3D geometric modeling, SpecFormer pre-training, and multi-modal conditioning. To our knowledge, DiffSpectra is the first framework that unifies multi-modal spectral reasoning and joint 2D/3D generative modeling for de novo molecular structure elucidation. Generative grammar ; Parameterized complexity ; Generative model ; Interpretation (philosophy) ; Spectral line ; Diffusion ; Molecular model ; Molecule Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sofia Albesa (22771735) | Ezequiel Giménez (22771738) | Jose M. Piccinini (18309643) | María G. Vizoso-Pinto (22771741) | Waldemar A. Marmisollé (1460245) | Esteban Piccinini (4806726) | Omar Azzaroni (1460248)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning-AugmentedGraphene Transistor Biosensing:Quantitative Platform Validation and Immunotesting of Hepatitis E Graphene-based chips face persistent sensor-to-sensor variability due to manufacturing defects and polymer contamination, limiting their analytical reliability for healthcare applications. Here, we demonstrate that the integration of a machine learning (ML) model with graphene field-effect transistors (GFETs) enables quantitative and calibration-free analytical sensing. Using Random Forest Regression and field-effect-related figures of merit, the model enabled robust, quantitative predictions across analytes of varying chemical naturefrom small ions to viral antigens. pH sensing was used as a reference system to validate the augmented platform. Compared with the reference analytical model, ML enabled a marked improvement of accuracy, from 93 to 97%, and a reduction of the coefficient of variability, from 14 to 3%. Then, the ML-integrated GFETs were applied to chloride detection, the gold standard for cystic fibrosis diagnosis. Finally, using GFETs functionalized with llama nanobodies, we targeted the ORF2 antigen of the Hepatitis E virus. ML integration significantly enhanced immunoassay sensitivity-specificity from 89–69% to 100–100% and allowed the quantitative prediction of antigen concentration. Furthermore, the ML-augmented test demonstrated a strong performance for HEV antigen detection in capillary blood samples without the need for any sample pretreatment. Analyte ; Reliability (semiconductor) ; Transistor ; Graphene ; Limiting ; Detection limit ; Quantitative analysis (chemistry) ; Immunoassay ; Polymer Graphene research and applications ; Nanowire Synthesis and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Sean M. Colby (5827871) | Jessica L. Bade (11966056) | Amy M. Jystad (4477723) | Peter S. Rice (11805875) | Yehia M. Ibrahim (1320999) | Simone Raugei (249177) | Christopher P. Harrilal (3911395)
Source: Figshare (article, 2025-09-11)
Field: Chemistry — Analytical Chemistry

Paired NeuralNetwork for Matching Experimental andPredicted Infrared Spectra We present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on <i>in silico</i> techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity. Pattern recognition (psychology) ; Matching (statistics) ; Similarity (geometry) ; Identification (biology) ; Spectral line ; Range (aeronautics) ; Artificial neural network ; Infrared Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rebello, Carine Menezes | Di Caprio, Ulderico | Steen-Hansen, Jenny | Rodrigues, Bruno | Costa, Erbet Almeida | Santos, Anderson Rapello dos | Esposito, Flora | Leblebici, Mumin Enis | Nogueira, Idelfonso B. R.
Source: ArXiv.org (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

ExPUFFIN: Thermodynamic Consistent Viscosity Prediction in an Extended Path-Unifying Feed-Forward Interfaced Network Accurate prediction of liquid viscosity is essential for process design and simulation, yet remains challenging for novel molecules. Conventional group-contribution models struggle with isomer discrimination, large molecules, and parameter availability, while purely data-driven graph neural networks (GNNs) demand large datasets and offer limited interpretability. Even when feasible to be applied, purely data-driven models lack thermodynamic consistency in their predictions and are not a reliable solution. This work introduces ExPUFFIN, an extended version of the Path-unifying Feed-Forward Interfaced Network, consisting of a hybrid GNN-based framework that directly predicts temperature-dependent viscosities of pure hydrocarbons from molecular graphs, while enforcing mechanistic inductive biases in the output layer to ensure thermodynamic consistency. Molecular information is given as graph structures, encoded as a graph convolutional network, and mapped to an inductive bias neuron based on two thermophysical correlations: a three-parameter Andrade-type equation and a four-parameter empirical viscosity-temperature relation. The accuracy of these models is compared with a solely data-driven prediction. The Andrade-based ExPUFFIN variant reduces RMSE compared to the purely data-driven baseline of 37 percent and yields smooth, physically consistent interpolation and extrapolation of viscosity-temperature curves, properties that are not observed in purely data-driven models. The empirical ExPUFFIN model provides comparable accuracy while retaining robust trends. Overall, embedding physics-based structure in GNN outputs improves accuracy, robustness, and transferability, enabling reliable viscosity predictions for complex hydrocarbon molecules. The approach is readily extendable to other properties and significantly broader chemical domains. Extrapolation ; Viscosity ; Consistency (knowledge bases) ; Graph ; Embedding ; Work (physics) ; Artificial neural network ; Process (computing) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: M.M. Korop | A.V. Prybyla
Source: Journal of Thermoelectricity (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Application of LLM to Search and Systematize the Properties of Thermoelectric Materials in Scientific Literature Thermoelectric materials find applications in a variety of fields due to their ability to directly convert heat into electricity. Selecting the optimal thermoelectric material is a challenging task, limited by empirical, time, and economic factors. Recent advances in artificial intelligence (AI), in particular large language models (LLMs), demonstrate significant potential for automatically extracting and organizing information from the scientific literature on the properties of thermoelectric materials. This review analyzes the evolution of machine learning-based methods, from early unsupervised NLP models such as Word2Vec to modern approaches using GPT models. The research results show that LLMs allow for the efficient identification of new promising thermoelectric materials, automation of experimental data collection processes, and the formation of structured databases, which significantly accelerates the search for materials with high efficiency rates. The paper also outlines directions for further research, such as extending the methods to tabular and graphical data, as well as optimizing computational resources. Scientific literature ; Thermoelectric effect ; Materials science ; Nanotechnology ; Engineering ; Engineering physics ; Physics ; Biology ; Botany Machine Learning in Materials Science
Authors: Mustafo Khidirov | Bobosodik Latipov | Nabijon Temirov | Mukhayyo Umaralieva | Khilolakhon Tojiboeva
Source:  (article, 2025-12-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of optical metamaterials using generative neural networks Optical metamaterials are artificial materials with structural features much smaller than the wavelength of light. These man-made materials exhibit exotic properties that have not been found in nature, such as negative refraction, perfect imaging, and ultra-high optical chirality. Inverse design of optical metamaterials based on generative neural networks accelerates discovery of optical metamaterials with specified functionalities. The system considers arbitrary input specifications and produces patterns of optical metamaterial that satisfy the input specifications. A full-wave simulator validates the output patterns and the trained model can be reused to achieve new design objectives. Experimental results show good agreement with specifications, demonstrating the feasibility of exploiting machine intelligence to assist the design of metamaterials. Metamaterial ; Inverse ; Artificial neural network ; Generative grammar ; Generative Design ; Inverse problem ; Generative model Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Orbital Angular Momentum in Optics
Authors: Rebello, Carine Menezes | Di Caprio, Ulderico | Steen-Hansen, Jenny | Rodrigues, Bruno | Costa, Erbet Almeida | Santos, Anderson Rapello dos | Esposito, Flora | Leblebici, Mumin Enis | Nogueira, Idelfonso B. R.
Source: arXiv (Cornell University) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

ExPUFFIN: Thermodynamic Consistent Viscosity Prediction in an Extended Path-Unifying Feed-Forward Interfaced Network Accurate prediction of liquid viscosity is essential for process design and simulation, yet remains challenging for novel molecules. Conventional group-contribution models struggle with isomer discrimination, large molecules, and parameter availability, while purely data-driven graph neural networks (GNNs) demand large datasets and offer limited interpretability. Even when feasible to be applied, purely data-driven models lack thermodynamic consistency in their predictions and are not a reliable solution. This work introduces ExPUFFIN, an extended version of the Path-unifying Feed-Forward Interfaced Network, consisting of a hybrid GNN-based framework that directly predicts temperature-dependent viscosities of pure hydrocarbons from molecular graphs, while enforcing mechanistic inductive biases in the output layer to ensure thermodynamic consistency. Molecular information is given as graph structures, encoded as a graph convolutional network, and mapped to an inductive bias neuron based on two thermophysical correlations: a three-parameter Andrade-type equation and a four-parameter empirical viscosity-temperature relation. The accuracy of these models is compared with a solely data-driven prediction. The Andrade-based ExPUFFIN variant reduces RMSE compared to the purely data-driven baseline of 37 percent and yields smooth, physically consistent interpolation and extrapolation of viscosity-temperature curves, properties that are not observed in purely data-driven models. The empirical ExPUFFIN model provides comparable accuracy while retaining robust trends. Overall, embedding physics-based structure in GNN outputs improves accuracy, robustness, and transferability, enabling reliable viscosity predictions for complex hydrocarbon molecules. The approach is readily extendable to other properties and significantly broader chemical domains. Extrapolation ; Viscosity ; Consistency (knowledge bases) ; Graph ; Embedding ; Work (physics) ; Artificial neural network ; Process (computing) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xing Yu (693880) | Xinyu Chen (47670) | Xuchen Yu (10302218) | Qionghua Zhou (2208739) | Liang Ma (37793) | Jinlan Wang (1235505)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Extending theSchottky–Mott Rule to AtomicThickness: Descriptors for Two-Dimensional Semiconductor–MetalContacts Ultralow-resistance Ohmic contacts at two-dimensional (2D) semiconductor–metal interfaces are pivotal for advancing next-generation electronics, yet the classical Schottky–Mott rule fails in 2D systems due to quantum confinement and pronounced Fermi-level pinning. Here, we use domain knowledge with machine learning to derive compact, physically interpretable Schottky-barrier descriptors that quantitatively characterize interfacial carrier behavior and accurately predict barrier heights from intrinsic parameters (work function and electronegativity). Applying these descriptors to transition metal dichalcogenides and various metal substrates, we identify 12 promising Ohmic contact electrodes from 1392 heterojunctions. We further introduce a physically grounded Schottky-barrier factor (δ) and reformulate the Schottky–Mott rule by explicitly coupling the interfacial work function difference (Δ<i>W</i><sub><i>F</i></sub>) with the electronegativity mismatches (Δχ). The co-modulation of Δ<i>W</i><sub><i>F</i></sub> and Δχ governs the Schottky-barrier formation at semiconductor–metal interfaces. This work extends Schottky–Mott physics to the atomic limit and offers practical design rules for engineering low-resistance 2D contacts. Ohmic contact ; Limit (mathematics) ; Electronegativity ; Coupling (piping) ; Function (biology) ; Work (physics) ; Domain (mathematical analysis) 2D Materials and Applications ; Graphene research and applications ; Electronic and Structural Properties of Oxides