jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D. Johnston | Dylan Tarter | Kitiara Griffin | Tanja Karp | Gordon F. Christopher | Dimitri Pappas
Source:  (article, 2025-06-12)
Field: Chemistry — Electrochemistry

Classification of Analyte in Aqueous Solution via High-Frequency Transmission Spectroscopy High-frequency transmission spectroscopy (HFTS) is the fusion of traditional radiofrequency and microwave characterization techniques with deep learning models to perform classification of materials across a wide range of application domains. It is especially applicable to the classification of chemical compounds found in aqueous solution. A potential application domain of HFTS is the rapid detection of contamination in the water supply. Each year, over 800,000 people are killed as a result of contaminants in the drinking water supply. This work presents a proof-of-concept application of HFTS to the classification of contaminants in a water sample. The “contaminants” used in this work are a selection of ten chloride salt solutions prepared at a concentration of 0.15μM. S<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> scattering parameter measurements were taken via a commercial vector network analyzer on a frequency range of 50 kHz to 500MHz. From these scattering parameter measurements, the magnitude response curves are calculated and used in the analysis. Visual exploratory data analysis is performed on the magnitude response curves to visually confirm that there are distinct patterns in the response of the various solutes which could be detected automatically through the application of a deep learning model. A multiclass convolutional neural network, composed of a single convolution layer and a fully connected linear layer, was developed and trained using cloud-hosted GPUs which achieved a testing accuracy of 92% during multiclass classification. Analyte ; Aqueous solution ; Spectroscopy ; Transmission (telecommunications) ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Optoelectronics ; Chemistry ; Chromatography ; Physics ; Telecommunications Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Hai-he Chen | Wenxing Chen | Wen-zhao Ren | H. Song | Yong Lu
Source: Journal of Advanced Thermal Science Research (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Temperature-Dependent Structural, Thermodynamic, and Phonon Properties of Lithium Fluoride from a Neuro-Evolution Machine-Learning Potential Accurate prediction of structural and thermodynamic properties at finite temperature is a prerequisite for deploying ionic-crystal LiF in solid-state electrolytes and molten-salt coolants. To simultaneously boost predictive accuracy and accessible simulation scale, we have developed a neuro-evolution potential (NEP) machine-learning model for LiF. The potential exhibits outstanding fidelity and stability when describing high-temperature structures and thermodynamics. Force-field molecular dynamics driven by this NEP yields a thermal-expansion coefficient and lattice thermal conductivity in excellent agreement with experiments. LiF displays remarkable dynamical stability at elevated temperature, a conclusion corroborated by its elastic constants and phonon dispersion relations. A pronounced frequency resonance between Li and F motions appears at high temperature, underscoring strong phonon anharmonicity. This work delivers a rigorously validated and transferable potential for LiF, providing both a research framework and a data foundation for future investigations of its behaviour at electrolyte interfaces, in high-temperature solid solutions and under other extreme thermodynamic conditions. Phonon ; Electrolyte ; Work (physics) ; Thermal stability ; Dispersion (optics) ; Stability (learning theory) ; Lattice (music) ; Thermal conductivity ; Lithium (medication) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Aviral Joshi | Awanit Saha | Anubhav Pratap Singh | Karnika Dwivedi | Jyoti Chaudhary | Deepak Chandra Joshi
Source:  (article, 2025-11-28)
Field: Chemistry — Analytical Chemistry

Explainable Deep Learning Approach for Food Adulteration Detection using LIME Food adulteration is a critical global public health concern leading to life-threatening illnesses like cancer, paralysis, and gastrointestinal disorders. Conventional detection methods rely on expensive, time-consuming chemical analysis techniques, making large-scale monitoring challenging. To address this, we propose a deep learning-based framework for identifying adulterated groundnut oil samples using Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Autoencoders. Furthermore, to improve the transparency and trust of the models, Local Interpretable Model-Agnostic Explanations (LIME) are incorporated. Our CNN-based model achieved the highest accuracy, precision, and recall among tested architectures. LIME visualizations highlighted the important chemical properties impacting decisions, such as iodine and saponification values. We present not only an efficient and scalable approach for food adulteration detection but also emphasize model interpretability, essential for regulatory acceptance. Future work includes deploying transformer-based models and real-time mobile solutions to enhance food safety globally. Deep learning ; Convolutional neural network ; Transparency (behavior) ; Artificial neural network ; Leverage (statistics) ; Lime ; Food products Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: Renling Hu | Jialu Wu | Qun Su | Shimeng Li | Yang Li | Tianyue Wang | Yu Kang | Tong Zhu | Chang-yu Hsieh | Tingjun Hou
Source: National Science Review (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

LamNet: an alchemical-path-aware graph neural network to accelerate binding free energy calculations for drug discovery and beyond Accurate prediction of protein-ligand binding free energies is critical yet computationally demanding in drug discovery. Alchemical free energy methods (AFEMs) offer high accuracy but suffer from significant computational costs and complex modeling setup, such as tuning the λ-schedule of alchemical transformation. While conventional deep learning (DL) models may instantly predict binding affinity, they often require a large training set and exhibit limited generalizability across chemical space. To address these challenges, we introduce LamNet, an alchemical-path-aware graph neural network. LamNet integrates endpoint molecular states and the bridging alchemical path (parametrized by λ) into a physics-informed representation learning framework, explicitly modeling free energy changes along a chosen thermodynamic transformation pathway. Trained on molecular-dynamics-simulated data along alchemical pathways and incorporating data reliability metrics, LamNet accurately predicts relative binding free energies and absolute binding free energies, and optimizes λ-schedules to improve traditional AFEM convergence. Evaluations on diverse datasets (463 ligands, 16 proteins) demonstrate that LamNet achieves superior or comparable performance to state-of-the-art methods, including traditional AFEM, but with up to 1000-fold acceleration. These findings establish LamNet as a generalizable, physics-grounded, and cost-effective tool that not only accelerates computations but also provides a novel framework for integrating rigorous computational physics into modern DL-driven drug discovery workflows. Artificial neural network ; Generalizability theory ; Bridging (networking) ; Graph ; Drug discovery ; Representation (politics) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rong Kang | Subhavna Juneja | Alan X. Wang
Source:  (article, 2025-01-01)
Field: Materials Science — Biomaterials

Morphology-Correlated Quantitative SERS Sensing on Diatom Photonic Crystals We developed a quantitative SERS sensing approach by exploiting the morphology feature of diatom photonic crystal frustules. Combining with an efficient inkjet dispensing technique, we applied machine learning analysis to achieve trace-level chemical detection. Photonic crystal ; Diatom ; Feature (linguistics) ; Photonics ; Crystal (programming language) ; Optical sensing Diatoms and Algae Research ; Gold and Silver Nanoparticles Synthesis and Applications ; Photonic Crystals and Applications
Authors: Shuai Huang | Wenzhi Huang | Yanpeng Fang | Yingli Zhu | Jiaguo Huang | Fei Chen | Jie Dong | Wenbin Zeng
Source: Research (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Interpretable Artificial Intelligence Decodes the Chemical Structural Essence of Twisted Intramolecular Charge Transfer and Planar Intramolecular Charge Transfer Fluorophores D-π-A-type fluorescent materials are crucial tools in life sciences and medicine, with their development hinging on a precise understanding of fluorophore mechanisms, particularly twisted intramolecular charge transfer (TICT) and planar intramolecular charge transfer (PICT) processes. These fluorophores exhibit unique charge transfer properties, making them highly valuable in organic optoelectronics, fluorescent probes, and sensors. However, despite their growing applications, the structural essence of TICT and PICT fluorophores remains poorly understood. This often results in molecules with similar structures displaying charge transfer modes that contradict design expectations, substantially hindering the application of TICT and PICT fluorescent probes. In this study, we meticulously designed various computational strategies based on interpretable machine learning to thoroughly deconstruct the chemical structural essence of TICT and PICT fluorophores. Utilizing the first real-world TICT and PICT dataset, we constructed predictive models that balance both interpretability and accuracy (area under the receiver operating characteristic curve = 0.846) using a range of algorithms, including deep learning. We established artificial intelligence (AI)-guided rules comprising 5 structural factors-electron-donating group strength, electron-withdrawing group strength, alkyl cyclization, steric hindrance, and solvent-solute interactions-that influence TICT and PICT. These rules provide obvious guidance for probe design based on molecular rigidity and charge transfer driving forces. Compared to community-suggested rules, the AI-guided rules achieved an over 20% improvement in accuracy in a controlled evaluation. By applying these rules, we successfully synthesized and validated several representative fluorophores that are challenging to distinguish using chemical intuition alone. Both quantitative calculations and experimental results confirmed the accuracy of the model and the practicality of the AI-guided rules. This novel approach is expected to establish a novel paradigm for exploring ideal TICT and PICT molecules, offering a robust framework for future research and application in fluorescent materials. Interpretability ; Intramolecular force ; Fluorophore ; Charge (physics) ; Fluorescence ; Steric effects Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Shabikta Momin Idris Momin
Source: Characterization and Application of Nanomaterials (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

A review: Synthesis Methods, Characterization Techniques and Applications of Metal Oxide-based Nanohybrids Metal oxide-based nanohybrids have become multipurpose materials that connect basic nanoscience with useful technology uses. They are appealing for a variety of sectors, from biology to energy and environmental remediation, due to their tunable physicochemical features and synergistic interactions. The main synthesis approaches—physical, chemical, and green/biological—are presented in a cohesive manner in this review, emphasizing their benefits, drawbacks, scalability, and appropriateness for various application requirements. Characterization methods including spectroscopy, diffraction, and microscopy are presented as crucial connections that link final functional performance with structure, composition, and morphology in addition to being analytical instruments. Additionally, the review incorporates new advancements such as data-driven intelligent material design, sustainable synthesis utilizing microbes and plant extracts, and machine learning-assisted process optimization. All things considered, this work provides a coherent overview linking synthesis techniques, property assessment, and application potential, providing insights that can direct the future development of effective, environmentally friendly metal oxide nanohybrids designed for practical technological deployment. Characterization (materials science) ; Environmentally friendly ; Process (computing) ; Property (philosophy) ; Variety (cybernetics) ; Sustainable energy Magnesium Oxide Properties and Applications ; Nanoparticles: synthesis and applications ; Catalysis for Biomass Conversion
Authors: Gabriele Scrivanti | Luca Calatroni | Stéfania Cacovich
Source:  (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Noise2noise Image Reconstruction of Lifetime Maps in Halide Perovskite Thin Films We present an unsupervised deep-learning approach for lifetime map reconstruction from noisy time-resolved fluorescence imaging (TR-FLIM) datasets. In the context of semiconductor and photovoltaic device characterisation, this method is critical for accurately predicting solar cell performance and detecting early signs of degradation. More precisely, we consider an unsupervised Noise2Noise (N2N) training framework combined with physics-driven modelling for the quantitative reconstruction of lifetime maps. The proposed approach incorporates a loglinear fit in the <tex>$\mathbf{N} \mathbf{2} \mathbf{N}$</tex> loss function and parameterises the unknown maps as outputs of a shallow neural network with a multibranch architecture. By learning from multiple noisy acquisitions of the same scene, our method effectively allows an accurate estimation with shorter acquisition protocols, which translates into a lower risk of damage for the sample under consideration. Tests on simulated data and comparisons with available model-based approaches show that the proposed approach improves robustness w.r.t. noise levels with limited tuning of the regularisation/algorithmic parameters. Robustness (evolution) ; Artificial neural network ; Context (archaeology) ; Iterative reconstruction ; Noise (video) ; Thin film ; Pattern recognition (psychology) ; Sample (material) ; Function (biology) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Surface Roughness and Optical Measurements
Authors: Byungju Lee | Hyun‐Jae Lee | C. I. Eugene Kim
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Developing Solid-State Electrolyte Using Machine-Learned Potential The introduction of Density Functional Theory (DFT) has improved the study of material properties, enabling significant breakthroughs in various scientific fields. However, when it comes to the complex analysis of solid electrolytes, limitations of DFT become evident from its extremely high computational costs. This study introduces Neural Network Potentials (NNP) as a complementary approach that extends the capabilities of DFT in this area. Focusing on the influence of anions in lithium mobility within Li-argyrodite (Li6PS5Cl) and amorphous Li-P-S, our research reveals that sulfur ions notably impede lithium transport This finding critically refines our understanding of solid electrolytes, bridging theoretical insights with practical material science applications. The incorporation of NNP not only improves predictions of material behavior but also deepens our comprehension of the fundamental principles governing these behaviors. The results represent a pivotal advancement in solid electrolyte research, merging large-scale computational techniques with in-depth material analysis, and contributing a notable perspective to the field. Bridging (networking) ; Density functional theory ; Electrolyte ; Amorphous solid ; Fast ion conductor ; Ion Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: D. R. Fortney | Brett M. Savoie
Source: Journal of Chemical Theory and Computation (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Data-Driven Coarse-Graining with Simple Functional Forms Deep neural networks have become popular model architectures for fitting coarse-grained molecular dynamics potentials (CGMD) owing to their ability to describe complex features and ease of training against large data sets. However, such architectures are much more complicated than traditional functional forms. This raises the question of whether a similar data-driven approach that used a simpler functional form would have any advantages. Here, we develop a genetic algorithm that optimizes Lennard-Jones potentials for coarse-grained models of various crystal and liquid crystal forming materials based on both structural and thermodynamic information. A detailed description of the genetic algorithm, its loss function, and hyperparameters is presented. The models developed by the algorithm reproduce a much broader range of physical properties than achieved by simpler parametrization schemes used in more general methods. The models also show surprising transferability in reproducing properties that were not directly trained against. Simulations of larger systems with these models demonstrate stabilization of the reference crystal structure on long time scales, preserve melting-point trends, and reproduce liquid crystalline phase transitions, despite this information being absent from training. Despite the rush to adopt neural network potentials, these case-studies show that simpler functional forms retain untapped potential for CGMD when coupled with data-driven training algorithms. Granularity ; Simple (philosophy) ; Computer science ; Data science ; Data mining ; Programming language Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Anatoly Grudinin | Rajeev Ratan
Source: Mineralogy and Petrology (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

On the possibility of non-invasive identification of natural diamonds and their origin using mid-IR spectroscopy and deep machine learning Diamond ; Diamond cubic ; Identification (biology) ; Artificial neural network ; Lattice (music) ; Synthetic diamond ; Material properties of diamond ; Crystal (programming language) ; Pattern recognition (psychology) Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Geological and Geochemical Analysis
Authors: Sejun Kim
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Deep Learning Model for Efficient Prediction of Dielectric Properties High dielectric materials are essential in a wide array of applications, particularly in electronics, energy storage, and electrochemistry. Their significance arises from their ability to influence the performance and efficiency of devices and systems. While Density Functional Perturbation Theory (DFPT) is a popular computational method for predicting dielectric constants, it is computationally expensive and not scalable for large systems. Recently, artificial intelligence (AI) has advanced rapidly, but the intrinsic challenges of predicting dielectric constants, such as the broad range of data, have hindered the development of AI models for this task. To address these challenges, we propose a deep learning model that efficiently predicts dielectric properties by leveraging the interactions between fundamental chemical fingerprints and a convolutional neural network (CNN). Our model demonstrates superior accuracy in predicting both isotropic and anisotropic dielectric properties compared to state-of-the-art graph-based models. It outperforms existing approaches in terms of various error metrics, including mean absolute error and mean squared error. We believe that this model offers a powerful and efficient alternative to traditional methods, effectively capturing the complex relationships between material composition and dielectric response. Dielectric ; Deep learning ; Artificial neural network ; Scalability ; Isotropy ; Mean squared error ; Range (aeronautics) ; Convolutional neural network Machine Learning in Materials Science ; Dielectric materials and actuators ; Advanced Graph Neural Networks
Authors: T. SEO | Donghun Kim | Shinwon Ham | You Kyoung Chung | Inho Jeong | Joonsuk Huh | Hyunwoo Kim | Do Hyun Ryu
Source: Angewandte Chemie (article, 2025-11-02)
Field: Chemistry — Inorganic Chemistry

Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning Abstract The longstanding quest for substrate generality stems from the unpredictability of single‐model optimization. Leveraging high‐throughput experimentation (HTE), we present a practical multi‐substrate screening strategy for the general asymmetric mono‐reduction of 1,2‐dicarbonyls. Quantitative 1 H NMR spectroscopy combined with simultaneous chiral analysis by 19 F NMR for pooled crude mixtures accelerated the workflow eightfold. Robust screening of 31 chiral oxazaborolidinium ion (COBI) variants across eight substrates tackled even ethyl/methyl differentiation. HTE data were utilized in a machine learning (ML) model with CGR (Condensed Graphs of Reaction)‐based descriptors, identifying catalysts for target substrates without quantum chemical calculations. The ARMS (Automated Reaction Mapping for various Substituents) system was introduced to streamline SMILES (Simplified Molecular Input Line Entry System) preprocessing for multi‐substrate datasets. The resulting chiral α ‐silyloxy ketones, obtained in excellent yields (up to &gt;99%) and selectivities (up to &gt;99% ee, &gt;20:1 r.r.), could be readily transformed into high‐value compounds, such as ( S )‐bupropion. Workflow ; Regioselectivity ; Preprocessor ; Substrate (aquarium) ; Generality ; Troubleshooting ; Nuclear magnetic resonance spectroscopy ; Catalysis Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Asymmetric Synthesis and Catalysis