jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Malik Almunif | Jade Le | Anthony Grbic
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Deep Neural Network Design of Reactively Loaded Metasurfaces A tandem deep neural network approach is presented for the inverse design of reactively loaded metasurfaces with prescribed far-field radiation characteristics. The proposed approach integrates a deep neural network (DNN) with a physics-based microwave network forward solver. The DNN maps target far-field patterns to distributions of reactive loads across the metasurface unit cells. The predicted distribution of reactive loads is evaluated by the forward solver to compute the resulting radiation pattern and guide the learning process through a cosine-similarity loss function. The forward solver enables a fast evaluation of the metasurface's electromagnetic response, significantly reducing the computational cost required for training. The proposed approach is applied to a metasurface with aperture-coupled unit cells loaded with reactances. Several design examples are presented to demonstrate the accurate synthesis of shaped and steered radiation patterns. Full-wave electromagnetic simulations are performed to validate the accuracy of the designed beamforming metasurfaces. Solver ; Artificial neural network ; Process (computing) ; Beamforming ; Microwave ; Inverse problem ; Radiation ; Inverse Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Armen G. Beck | Gwenyth Jones | Andrew Singh | Jonathan Fine | Rojan Shrestha | Rodell C. Barrientos | Edward C. Sherer | K N Williams | Erik L. Regalado | Pankaj Aggarwal
Source: Analytical Chemistry (article, 2026-02-02)
Field: Chemistry — Spectroscopy

Uncertainty-Aware Learning of Multiple Conditions as a Framework for Streamlined Retention Time Prediction to Accelerate Method Development Liquid chromatography is a cornerstone analytical technique for separating, quantifying, and purifying components from complex mixtures. To accelerate method screening, we herein introduce an uncertainty-aware graph-based neural network that predicts retention times across multiple column chemistries and buffer pH conditions. Specifically, the multiple condition retention time model, MC-Retention, incorporates explicit column and buffer descriptors and is trained on a newly assembled data set produced by screening 480 analytes under eight chromatography conditions. By being able to accurately model multiple chromatographic methods simultaneously, MC-Retention can substantially reduce the time and resources required for LC method screening and development. With an aggregate <i>R</i><sup>2</sup> of 0.86 and a mean absolute error (MAE) of 15.5 s during cross-validation, MC-Retention's respectable performance is greatly enhanced for analytes made present during training for one of the four column chemistries, when those analytes are otherwise not associated with the columns being validated. This selective training of analytes for a single column chemistry reduces error by 50%, with an <i>R</i><sup>2</sup> of 0.95 and MAE of 8.2 s in aggregate. Additionally, we demonstrate that MC-Retention effectively identifies optimal conditions for separating amide bond-forming reaction components, while also supplying calibrated uncertainty estimates for its retention time predictions. Analyte ; Retention time ; Column (typography) ; Artificial neural network ; Column chromatography ; Set (abstract data type) ; Principal component analysis Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Pranesh Chowdhury | Nandagopal Bar
Source: Polyhedron (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Harnessing the photochromic behavior of metal-complexes from non-photochromic ligands: A brief review of current insights and future perspectives Photochromism ; Smart material ; Coordination complex ; Phase (matter) ; Design elements and principles Photochromic and Fluorescence Chemistry ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Tim Hörmann | Domenic Mayer | Max Lewandowski | Andrea Hunklinger | Thomas Wein | Daniel Merk
Source: Nature Communications (article, 2026-04-11)
Field: Materials Science — Materials Chemistry

Structural optimization of drug molecules with incrementally trained language models Automating structural optimization of drug molecules for on-target potency by machine learning is an open challenge in chemistry. Here, we capitalize on the ability of chemical language models (CLMs) to learn from sequential data and design new molecules with desired properties. We establish a training strategy mimicking the learning trajectory of a drug discovery program. Incremental CLM fine-tuning with increasingly potent template molecules from a given structure-activity relationship (SAR) series successfully biases the model to design highly active analogues. Prospective application of this technique to ligand development enables the data-driven design of molecules exceeding known representatives of given bioactive chemotypes in potency without external scoring. Our results reveal an ability of CLMs to capture SAR patterns and long-range dependencies, and to exploit SAR knowledge in designing analogues with improved on-target activity de novo corroborating their applicability to structural optimization of drug molecules. Drug discovery ; Exploit ; Chemical space ; Training set ; Language model ; Series (stratigraphy) ; Drug target ; Drug development Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Ziyong Chen | Jonathan Lam | Vivian Wing-Wah Yam
Source: Journal of Chemical Theory and Computation (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semiempirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies. Superposition principle ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Construct (python library) ; Molecular orbital ; Organic molecules ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Synthesis and Properties of Aromatic Compounds
Authors: Moritz Schäfer | Matthias Kellner | Johannes Kästner | Michele Ceriotti
Source: ArXiv.org (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

How to Train a Shallow Ensemble Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different ensemble members share a large part of the model weights. In this work, we systematically investigate training strategies for shallow ensembles to balance calibration performance with computational cost. We first demonstrate that explicit optimization of a negative log-likelihood (NLL) loss improves calibration with respect to approaches based on ensembles of randomly initialized models, or on a last-layer Laplace approximation. However, models trained solely on energy objectives yield miscalibrated force estimates. We show that explicitly modeling force uncertainties via an NLL objective is essential for reliable calibration, though it typically incurs a significant computational overhead. To address this, we validate an efficient protocol: full-model fine-tuning of a shallow ensemble originally trained with a probabilistic energy loss, or one sampled from the Laplace posterior. This approach results in negligible reduction in calibration quality compared to training from scratch, while reducing training time by up to 96%. We evaluate this protocol across a diverse range of materials, including amorphous carbon, ionic liquids (BMIM), liquid water (H$_2$O), barium titanate (BaTiO$_3$), and a model tetrapeptide (Ac-Ala3-NHMe), establishing practical guidelines for reliable uncertainty quantification in atomistic machine learning. Calibration ; Range (aeronautics) ; Energy (signal processing) ; Training (meteorology) ; Probabilistic logic ; Protocol (science) ; Laplace transform ; Uncertainty quantification ; Ensemble forecasting Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Murat Aydemir
Source: Journal of Luminescence (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Integrating photophysics and machine learning to decode Triplet–Triplet annihilation in anthracene systems Photon upconversion ; Annihilation ; Boosting (machine learning) ; Anthracene ; Quantum yield ; Quantum ; Interpretability Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: Chao Yu | Xiaojie Chen | Feng Zhang | Hui Rong | Xiaofeng Zhou
Source: Biofouling (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

The bio-corrosion mechanisms, mathematical modelling and machine learning applications in concrete structures: a systematic review Concrete is a widely utilised material for infrastructure such as sea-crossing bridges, harbour terminals and sewage pipelines. However, concrete structures are susceptible to various forms of deterioration, including biological and chemical corrosion. Among these, bio-corrosion poses a significant threat to structural durability by promoting surface scaling and inducing complex physicochemical and biophysical damage. This study provides a comprehensive review of the current state of bio-corrosion research, with a focus on clarifying its mechanisms and established quantification techniques. Additionally, it examines advances in mathematical modelling and machine learning for analysing bio-corrosion in concrete structures. This review also addresses ongoing debates and insights within the field of bio-corrosion and bio-protection. Field (mathematics) ; Durability ; Focus (optics) ; Mathematical model ; Scaling Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Odor and Emission Control Technologies
Authors: Junhua Zhang | Yuan Rui | Zhang Xujie | Tang Yu | Yang Xiaojia
Source: Journal of Materials Engineering and Performance (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Atmospheric Corrosion Research of Carbon Steel for Automobile Based on Corrosion Big Data Technology Corrosion ; Big data ; Automotive industry ; Carbon steel ; Work (physics) ; Corrosion monitoring Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Xu Long | Bassem Mohamed | Tang Gu | Benelfellah Abdelkibir | Qiang Guo
Source: Mechanics of Materials (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Effects of size and texture on anisotropic elasto-plasticity in Cu–Nb nanolaminates by integrating semi-empirical and data-driven approaches Anisotropy ; Texture (cosmology) ; Orientation (vector space) ; Composite number ; Deformation (meteorology) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Shape Memory Alloy Transformations
Authors: Pierre Ladevèze | L. Chamoin
Source: European Journal of Mechanics - A/Solids (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

From data to reality: Criteria for the validation of data-driven models for history-dependent materials This paper addresses the issue of validating identified data-driven material models for history-dependent materials, which are nowadays typically represented using neural networks. For this purpose, we introduce an a posteriori general acceptability criterion based on the Constitutive Relation Error (CRE) . A computed solution using the identified material model is deemed acceptable if it closely approximates the experimental data when using the CRE metric. • We define a posteriori criteria for the validation of identified history-dependent material models. • A CRE metric is used to assess proximity to the experimental constitutive manifold. • Additional technicalities are given on the computation of data-driven material models. • Insights are also given on the determination of the hidden variable dimension. A priori and a posteriori ; Relation (database) ; Metric (unit) ; Model validation ; Computation ; Experimental data ; Variable (mathematics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Digital Humanities and Scholarship
Authors: Huanrong Liu | Shan Zhang | Qingan Li | Bin Xu | Jian Li | Pengfei Guan
Source: AI for Science (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop As advanced amorphous materials with superior mechanical, physical, and chemical properties, metallic glasses (MGs) hold significant promise for a wide range of applications. However, their rational design and precise property control have long been impeded by notable challenges. These obstacles largely arise from the inherent compositional and structural complexity of MGs, which not only slows empirical trial-and-error experimentation but also limits the scalability of computationally intensive first-principles simulations. In recent years, machine learning has emerged as a transformative tool, offering unprecedented capabilities to decode these intricate relationships and overcome conventional research limitations. Here we provide a systematic overview of machine-learning-guided investigations of MGs and their associated data pipelines, centering on two key paradigms: the feature-driven ‘Keplerian’ data pipeline and a next-generation theory-experiment-aligned pipeline designed to close the gap between simulation and experiment. By breaking down both paradigms into modular workflow components, we underscore the essential roles of data standardization, interpretable feature engineering, and physics-informed validation in constructing a reliable research framework. We anticipate that a sufficiently robust, efficient, and generalizable data pipeline will not only unlock novel scientific insights and accelerate material discovery but also propel the field from a ‘trial-and-error’ approach toward an era of intelligent and principled design. Closing (real estate) ; Loop (graph theory) ; Key (lock) ; Intelligent control ; Control theory (sociology) ; Control system Machine Learning in Materials Science ; Material Selection and Properties ; Metallic Glasses and Amorphous Alloys
Authors: Amirali Shateri | Zhiyin Yang | Lei Xing | Yuying Yan | Zhen Wu | Jianfei Xie
Source: Energy and AI (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

An extrapolative machine learning framework for forecasting nitrogen oxide mitigation in alcohol enhanced ammonia-methane blends As carbon-free energy carrier, combustion of ammonia suffers from low burning rates, poor flame stability, and excessive nitrogen oxide (NOx) emissions. Although blending with hydrocarbon fuels such as methane alleviates some drawbacks, NOx formation remains a critical barrier. To address these challenges, we propose a hybrid framework combining reactive force field molecular dynamics (ReaxFF-MD) simulations with machine learning (ML). MD simulations at 2000-3000 K were performed for ammonia-methane blended combustion with 0-10% addition of ethanol or methanol. Adding alcohols suppressed the NOx formation by altering charge redistributions and redirecting nitrogen intermediates into stabilising pathways. Particularly at 3000 K, 10% ethanol and methanol reduced NOx by ∼39.5% and ∼30.1%, respectively. Both chemical and physical descriptors derived from MD were used to train ML models and successfully predicted NOx trends at intermediate compositions (2%, 7%, 12%) with <5% error for ethanol-rich mixtures, though predictions beyond 12% require further validation. This framework reduces reliance on costly simulations while providing mechanistic insights and predictive capability of designing alternative fuels. NOx ; Nitrogen oxide ; Combustion ; Nitrogen oxides ; Methanol ; Methane ; Alcohol ; Nitrogen Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Oxidation Reactions
Authors: Stuti . | Shivani Yaduvanshi | Dushyant Sharma | Vansh Kashyap | Veerendra Kumar
Source: Preprints.org (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications Molecular dynamics (MD) simulation is a fundamental technique for resolving biomolecular structures and functions at atomic resolution. Accelerated by GPU computing and machine learning-integrated force fields (FF), modern MD simulation facilitates the study of large-scale systems and rare biological events, such as protein folding, allosteric transitions, etc. While advanced sampling methods and AI integration have significantly enhanced efficiency in drug discovery and protein engineering, the field still faces challenges regarding FF accuracy, timescale constraints, and quantum effects. Continued development of hybrid quantum and molecular mechanics methods and standardized workflows is essential to further improve the predictive power and reproducibility of MD in biotechnological research. In this review, we attempted to provide the latest developments in the MD simulations. Molecular dynamics ; Workflow ; Field (mathematics) ; Force field (fiction) ; Sampling (signal processing) ; Quantum Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Zhonghua Guo | Yanhua Jiang | Yinyu Cheng | Gang Liu | Jun Xie | Quanhong Ou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

FTIR spectroscopy coupled with interpretable machine learning for authentication of processed Polygonatum sibiricum Interpretability ; Principal component analysis ; Chemometrics ; Pattern recognition (psychology) ; Partial least squares regression ; Dimensionality reduction ; Fourier transform ; Product (mathematics) ; Reliability (semiconductor) Spectroscopy and Chemometric Analyses ; Polysaccharides and Plant Cell Walls ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xinhua Xu | Rui Shi | Guang Yang | Yi-Qi Wei | Chao Li | Yuhong Wei | Shu-Xin Zeng | Xianguo Tuo
Source: Journal of the Korean Physical Society (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Numerical quantitative analysis of toxic elements Cd and Hg in cemented nuclear waste solidified bodies using PGNAA technology and deep learning Convolution (computer science) ; Deep learning ; Convolutional neural network ; Feature (linguistics) ; Feature extraction ; Monte Carlo method ; Matrix (chemical analysis) ; Fourier transform ; Artificial neural network Radiation Shielding Materials Analysis ; Nuclear Physics and Applications ; Concrete and Cement Materials Research
Authors: Mohd Aqib | Kopparthi Ravikiran | Leijun Li | Vinay Prasad
Source: Materials & Design (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

A multi-fidelity active learning framework for accelerated alloy design Machine learning (ML) driven methodologies are more efficient than traditional trial-and-error-based materials design; however, extensive training datasets are required for their development. To overcome this challenge, we developed a multifidelity active learning (MFAL) framework that significantly improves optimization efficiency compared to typical evolutionary methods. This framework strategically balances the computational effort between high- and low-fidelity evaluations, thereby reducing experimental burden while effectively guiding the search towards optimal compositions. MFAL was applied to high-entropy alloy (HEA) design, enabling the discovery of compositions that approach the theoretical minimum coefficient of thermal expansion (CTE). The optimized composition was efficiently identified, validating the developed framework as robust and scalable. The quantitative study shows that the MFAL framework achieved near-optimal convergence in around 75 iterations, using only 55–65% costly high-fidelity evaluations, in contrast to single-fidelity methods that needed 100% high-fidelity assessments. Compared to trial-and-error approaches, MFAL delivers five-fold improvement in optimization speed while requiring 40% fewer high-fidelity evaluations than conventional methods. It demonstrates optimization of experimental/computational resources by strategically focusing expensive evaluations on the most promising compositional areas. MFAL has the potential for rapid development of next-generation alloys with customized properties and insights into composition-8093property relationships. • A multi-fidelity active learning (MFAL) framework is developed for HEA alloy design. • MFAL identifies the alloy with minimum coeAicient of thermal expansion. • MFAL provides significant resource savings compared to global optimizer. Convergence (economics) ; Active learning (machine learning) ; Alloy ; Resource (disambiguation) ; Thermal ; Learning curve Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Jiang Du | Arwa Abdulkreem AL-Huqail | Yan Cao | Hui Yao | Yiding Sun | Mazen Garaleh | Ehab El Sayed Massoud | Elimam Ali | Hamid Assilzadeh | José Escorcia-Gutierrez
Source: Environmental Research (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Retraction notice to “Green synthesis of zinc oxide nanoparticles from Sida acuta leaf extract for antibacterial and antioxidant applications, and catalytic degradation of dye through the use of convolutional neural network” [Environ. Res. 258 (2024) 119204] Antioxidant ; Zinc ; Degradation (telecommunications) ; Nanoparticle ; Human immunodeficiency virus (HIV) ; Notice Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications ; Curcumin's Biomedical Applications