jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haipan Xiang | Yangchun Chen | Yufei Deng | Mingxuan Jiang | Dong Wang | Yi Zhao | Kun He | Huiqiu Deng
Source: Nuclear Materials and Energy (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Uniaxial strain-induced cascade defect evolution in molybdenum: An atomistic study with a new machine-learning potential • A high-precision machine-learning potential for molybdenum has been developed and validated for irradiation damage studies. • Uniaxial tensile strain gradually increases the number of thermal-spike defects, whereas the stable-defect population is governed by both strain and temperature: it rises with tensile strain only in the low-temperature region. • Distinct from <100> or <110> strain, increasing <111> uniaxial tensile strain (up to 2%) in molybdenum at 673 K uniquely induces preferential alignment of interstitial pairs along the strain axis. This alignment subsequently enhances defect cluster growth and nucleates 1/2 <111> dislocation loops. The evolution of irradiation defects in nuclear structural materials is profoundly influenced by mechanical stress. However, a predictive understanding of this coupling at the atomic scale remains challenging. In this work, we develop a machine-learning moment tensor potential (MTP) suitable for cascade simulations and widely validate its accuracy. Utilizing this potential, we systematically perform molecular dynamics simulations of collision cascades with a focus on uniaxial [111] strain across a wide range of temperatures and PKA energies. These are supplemented by simulations under [100] and [110] strains at 673 K and 10 keV to assess crystallographic orientation effects. Our results reveal that the thermal spike defect yield increases monotonically with tensile strain, regardless of temperature; the stable defect number only increases with strain changing from compressive to tensile at low temperatures, while the strain effect is not significant at high temperatures. Moreover, the orientational response of self-interstitial atoms to strain is found to be highly dependent on the crystallographic direction of the applied strain. Tensile strain along [111] dirction increases the <111> lattice spacing and reduces the formation energy of interstitials, causing self-interstitial atoms to preferentially align along the 〈1 1 1〉 direction and significantly increasing the yield of large-sized clusters and dislocation loops. This work provides an atomic-scale basis for understanding the irradiation defect evolution of Mo under actual service stress. Ultimate tensile strength ; Strain (injury) ; Molecular dynamics ; Dislocation ; Yield (engineering) ; Plasticity ; Molybdenum ; Cascade ; Coupling (piping) ; Population Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Mohamed Elnemr | Mohammed Ayaz Uddin | Jwala Pradeep | Lianxi Zheng | Butt Haider
Source: Materials & Design (article, 2026-03-03)
Field: Materials Science — Surfaces, Coatings and Films

Machine-Learning-Guided design and inverse engineering of 3D-printed optical filters • 3D printing via vat photopolymerization used to create low-cost tunable optical filters. • Machine learning models predict and design filter spectra with high accuracy. • AI-guided formulations enabled 3D-printed replicas of commercial optical filters. • Material tests show printed filters are elastic, stable, and recover after strain. Optical filters are essential components in imaging, sensing, and communication systems, where they selectively control light transmission across specific wavelength ranges. Additive manufacturing offers a flexible approach to producing optical devices with customizable shapes, dimensions, and spectral responses. In this work, color-tunable filters were produced using digital light processing 3D printers and alcohol-ink-doped photoresins (developed in-house) that span the entire visible spectrum. Their optical properties were strongly dependent on ink concentration, allowing subtle control of transmission intensity and bandwidth. To predict and inversely design the optical properties of the printed filters, artificial intelligence models were trained to connect ink concentrations and compositions with measured optical transmission spectra. The Multilayer Perceptron achieved the best performance in predicting ink compositions from known optical spectra (R 2 up to 0.99), whereas the Random Forest model most accurately reconstructed the optical spectra from known ink compositions (R 2 = 0.96). Additionally, rheological testing showed that the printed materials were mainly elastic and could fully recover after large deformations, while mechanical measurements confirmed adequate strength and tear resistance for practical handling. The study demonstrates that combining machine learning with material characterization can enable data-driven design of 3D-printed photonic devices that are both mechanically stable and spectrally tunable. Optical filter ; Inkwell ; Transmission (telecommunications) ; Transmittance ; Photonics ; Filter (signal processing) ; Optical fiber ; Characterization (materials science) ; Inverse Optical Coatings and Gratings ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Zahra Khatibi | Mariano, Lorenzo A. | Lion Frangoulis | Alessandro Lunghi
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Efficient training of generative models from multireference simulations and its application to the design of Dy complexes with large magnetic anisotropy Generative machine learning models can potentially provide direct access to novel and relevant portions of the full chemical space, overcoming the cost of systematic sampling. However, the training of these models generally requires a large amount of data, often precluding the use of expensive high-level ab initio simulations for this task. The generation of coordination compounds of Dy with large magnetic anisotropy represents a topical example, where multireference simulations of large molecules are necessary to perform reliable predictions. Here, we show that a semi-supervised chemically-inspired training-by-proxy of generative variational autoencoders can reduce the cost associated with building a training set from multireference simulations by two orders of magnitude. We illustrate the power of this approach by generating 100s of new organic ligands for Dy(III) pentagonal bipyramidal complexes exhibiting record values of magnetic anisotropy, while starting from datasets as small as 1k multireference calculations. This work thus paves the way to the computational generation of molecules as complex coordination compounds with target electronic and magnetic properties. Ab initio ; Generative grammar ; Set (abstract data type) ; Anisotropy ; Ab initio quantum chemistry methods ; Work (physics) Machine Learning in Materials Science ; Magnetism in coordination complexes ; Computational Drug Discovery Methods
Authors: Jing Shen | Ziyan Ye | Minghui Du | Shiyu He | Dong Ru Zhang | Venkat Kapil | Jia-Xi Zeng | Wei Fang
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Rigorous Quantum Thermodynamics from Extended Path Integral Coarse-Graining Recent advances in machine-learning potentials have made molecular dynamics (MD) simulations increasingly predictive by enabling efficient descriptions of electronic quantum effects. However, nuclear quantum effects (NQEs) remain largely neglected due to their significant computational cost, limiting MD's ability to capture isotope effects and quantum tunnelling and leading to systematic thermodynamic errors. We extend path-integral coarse-graining (PIGS), a machine-learning approach for qualitatively incorporating NQEs, into a rigorous and accurate method for quantum thermodynamics. Specifically, this extended framework (EPIGS) enables the construction of temperature-transferable effective potentials that rigorously incorporate NQEs into classical MD simulations. Central to EPIGS is an instanton-based free-energy perturbation scheme (RPI-FEP) that provides direct and efficient estimation of the path-integral centroid potential. Benchmarks against full path-integral simulations on representative hydrogen-bonded systems, including liquid water, show that EPIGS reproduces free energies and enthalpies within 0.2 meV/atom at near-classical cost. EPIGS transforms the qualitative PIGS approach into an accurate and scalable route to quantum thermodynamic simulations in complex systems. Path integral formulation ; Quantum ; Quantum thermodynamics ; Quantum tunnelling ; Quantum dynamics ; Limiting ; Molecular dynamics ; Perturbation theory (quantum mechanics) ; Quantum simulator Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Jinying Yu | Manni Ke | Binbin Xu | Ziyu Gao | Qiuju Fan | Yaling Wu | Qingsong Shao | Ailian Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Rapid authentication and quantification of adulterated Atractylodes rhizoma based on portable near-infrared spectroscopy combined with chemometrics Chemometrics ; Partial least squares regression ; Atractylodes ; Pattern recognition (psychology) ; Artificial neural network ; Medicinal herbs ; Support vector machine Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: John Crosby | Haoran Cui | Mehrab Lotfpour | Y. Wang | Lei Cao
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Enhanced Graphene-Water Thermal Transport via Edge Functionalization without Compromising In-Plane Thermal Conductivity Interfacial thermal transport between graphene and water plays a critical role in a wide range of thermal and energy applications. Although chemical functionalization can significantly enhance graphene-water interfacial thermal conductance, it often degrades graphene's intrinsic in-plane phonon transport. In this work, we perform a systematic deep neural network molecular dynamics study comparing edge-functionalized graphene nanoribbons with surface-functionalized graphene in aqueous environments. We demonstrate that functionalizing only 10% of the ribbon edges with hydroxyl groups increases the graphene-water interfacial thermal conductance by more than eightfold, primarily due to strengthened interfacial interactions and improved wettability at the edges. In contrast to basal-plane oxidation, edge functionalization largely preserves in-plane thermal conductivity. Importantly, hydroxyl edge groups exert competing effects on phonon transport: they introduce additional boundary scattering that suppresses heat conduction, while simultaneously passivating dangling bonds at bare edges, thereby reducing phonon localization and edge-induced scattering. This competition leads to a non-monotonic dependence of in-plane thermal conductivity on edge functionalization ratio. These results establish edge functionalization as an effective strategy for enhancing graphene-water interfacial thermal transport without sacrificing intrinsic phonon transport properties. Surface modification ; Thermal conductivity ; Graphene ; Phonon ; Thermal ; Wetting ; Phonon scattering ; Interfacial thermal resistance Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Graphene research and applications
Authors: Watcharaporn Cholamjiak | Siwat Lawanwadeekul | Kunrada Kankam | Prasit Cholamjiak | Nipa Jun-on
Source: Carpathian Journal of Mathematics (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Classifying compressive strength of clay bricks using aninertial projected forward-backward algorithm The enduring popularity of clay bricks as a construction material can be attributed to their af fordability. Nevertheless, the elevated temperatures necessary to attain the desired physical and mechanical characteristics result in substantial energy consumption and the emission of thermal pollutants. Accurately classifying clay bricks is essential in evaluating their suitability for construction purposes, with compressive strength being a critical parameter. The present study introduces new projected forward-backward algorithms for solving constrained convex minimization problems by using line search techniques to classify the compres sive strength of clay bricks. The algorithm performs better than current methodologies, displaying exceptional precision, recall, F1 score, and accuracy. As a numerical result, the finding highlight the significance of water absorption for classifying compressive strength. The bulk density is directly influenced by the size of additives, whereas the firing temperature, firing shrinkage, and apparent porosity exhibit interrelationships throughout the firing process. A comprehensive understanding of these parameters is pivotal in enhancing the clay brick manufacturing process and facilitates informed decision-making about material selection and structural design. Moreover, the algorithm can enhance machine learning methodologies in materials science and engineering applications. Compressive strength ; Brick ; Process (computing) ; Porosity ; Thermal ; Minification ; Selection (genetic algorithm) Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Calcium Carbonate Crystallization and Inhibition
Authors: Jing Shen | Ziyan Ye | Minghui Du | Shiyu He | Dong H. Zhang | Venkat Kapil | Jia-Xi Zeng | Wei Fang
Source: arXiv (Cornell University) (preprint, 2026-02-21)
Field: Materials Science — Materials Chemistry

Rigorous Quantum Thermodynamics from Extended Path Integral Coarse-Graining Recent advances in machine-learning potentials have made molecular dynamics (MD) simulations increasingly predictive by enabling efficient descriptions of electronic quantum effects. However, nuclear quantum effects (NQEs) remain largely neglected due to their significant computational cost, limiting MD's ability to capture isotope effects and quantum tunnelling and leading to systematic thermodynamic errors. We extend path-integral coarse-graining (PIGS), a machine-learning approach for qualitatively incorporating NQEs, into a rigorous and accurate method for quantum thermodynamics. Specifically, this extended framework (EPIGS) enables the construction of temperature-transferable effective potentials that rigorously incorporate NQEs into classical MD simulations. Central to EPIGS is an instanton-based free-energy perturbation scheme (RPI-FEP) that provides direct and efficient estimation of the path-integral centroid potential. Benchmarks against full path-integral simulations on representative hydrogen-bonded systems, including liquid water, show that EPIGS reproduces free energies and enthalpies within 0.2 meV/atom at near-classical cost. EPIGS transforms the qualitative PIGS approach into an accurate and scalable route to quantum thermodynamic simulations in complex systems. Path integral formulation ; Quantum ; Quantum thermodynamics ; Quantum tunnelling ; Quantum dynamics ; Limiting ; Molecular dynamics ; Perturbation theory (quantum mechanics) ; Quantum simulator Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Xueqi Chen | Junsong Peng | Manyao Wang | Shuran Liao | Yang Chen | Zongyu Huang | Xiang Qi
Source: Journal of Electroanalytical Chemistry (article, 2026-01-09)
Field: Materials Science — Polymers and Plastics

Artificial synapses with electrochromic optically responsive for signal recognition Electrochromism ; Neuromorphic engineering ; Synapse ; Transmittance ; Modulation (music) ; Synaptic weight ; Artificial neural network ; Tungsten trioxide Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Conducting polymers and applications
Authors: Nuo Xu | Biqi Liu | Renyi Geng | Y. e Liang Zhang | Xinyan Yang | Wenquan Zhang | Bo Qu | Ling Guo | Yue Jiang
Source: Trends in Food Science & Technology (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

AI in cheese manufacturing: A comprehensive review of technologies, applications, and future prospects Production (economics) ; Quality (philosophy) ; Quality assurance ; Process (computing) ; Key (lock) ; Food processing ; Control (management) ; Process control ; Industrial production Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Denis O. Potapov | Sergei Rogovoi | Kuzma Khrabrov | Konstantin Ushenin | А. Н. Коровин | Anton Ber | Artur Kadurin | Artem Tsypin
Source: Communications Chemistry (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

A conformational benchmark for optical property prediction with solvent-aware graph neural networks Accurately predicting optical spectra of molecules is essential for creating better OLED emitters, solar-cell dyes, and fluorescent probes. Traditional methods, such as time-dependent density-functional theory, are computationally expensive and often inaccurate. Current Graph Neural Network (GNN) approaches for optical properties prediction are faster and offer better performance. Still, they operate on 2D graphs and ignore the 3D geometrical features that control excited-state behavior. We present nablaColors-3D, a rigorously curated dataset for the prediction of optical properties consisting of 26369 chromophore-solvent pairs with three conformations optimized at different levels of quantum theory. Based on this dataset, we establish a scaffold-split benchmark for 3D GNNs and systematically quantify how the fidelity of geometry optimization affects accuracy. Furthermore, we propose a solvent-aware modification for pretrained SE(3)-invariant architectures. Our best model, built on UniMol+, achieves MAE of 15.97 nm on a held-out test set, improving the previous state of the art by more than 30%. Benchmark (surveying) ; Artificial neural network ; Property (philosophy) ; Graph ; Fidelity ; Representation (politics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Ahmed Mohammed Ali Abdulkhaleq | Zhonghua Jiang | Hai Feng Chen | Ridong Huang | Lingling Ma | He Yang | Wuyu Mao
Source: Journal of Materials Chemistry B (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Carbon dot nanoparticles as multifunctional fluorescent probes: from tunable synthesis to biomedical applications Carbon dots (CDs) have emerged as highly promising fluorescent nanomaterials due to their exceptional optical properties, biocompatibility, and facile synthesis. These attributes position them as versatile platforms for bioimaging, biosensing, and diagnostic applications in modern biomedicine. Significant progress has been achieved in translating CDs from proof-of-concept studies toward practical biomedical tools, particularly in real-time bioimaging and sensing. This review summarizes recent advancements in the synthesis and fluorescence mechanisms of CDs, with a focus on their applications as targeted fluorescent probes for critical analytes (<i>e.g.</i>, metal ions, reactive oxygen species, enzymes, biomolecules) and cellular organelles. We provide a comparative analysis of CDs classification and correlate synthesis parameters with optical performance. Additionally, we critically evaluate the role of CDs in key disease areas, including oncology, cardiovascular disorders, neurodegenerative conditions, pulmonary and inflammatory diseases, highlighting their capacity for early diagnosis, therapeutic monitoring, and intraoperative guidance. Furthermore, we discuss the transition toward sustainable, green synthesis and the emerging role of computational tools, such as machine learning and artificial intelligence, in optimizing CDs properties and enhancing image interpretation. Despite their transformative potential, challenges persist in achieving synthesis reproducibility, enhancing selectivity <i>via</i> "turn-on" mechanisms, and validating long-term biocompatibility <i>in vivo</i>. Addressing these limitations through interdisciplinary collaboration and advanced engineering strategies is essential for fully realizing the clinical potential of CD-based fluorescent probes in modern medicine. Biocompatibility ; Fluorescence ; Nanomaterials ; Nanoparticle ; Carbon nanotube ; Optical imaging Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Melamine detection and toxicity
Authors: Philipp Schienbein
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Mimyria: Machine learned vibrational spectroscopy for aqueous systems made simple Vibrational spectroscopy provides a powerful connection between molecular dynamics (MD) simulations and experiment, but its routine use in condensed-phase systems remains limited. We introduce mimyria, a modular and automated framework that orchestrates electronic-structure reference calculations, trains atom-resolved machine-learning response models, and generates IR and Raman spectra from MD trajectories within a unified workflow. We introduce the polarizability gradient tensor (PGT) as a novel atom-resolved machine-learning target property for Raman spectroscopy, complementing the established atomic polar tensor (APT) for IR spectroscopy. As a necessary prerequisite, we demonstrate how both PGTs and APTs can accurately be computed from electronic-structure theory, validate them across formally equivalent derivative formulations, and thereby benchmark their numerical consistency. We then employ machine learning as an efficient surrogate to represent the validated APT and PGT response functions on aqueous benchmark systems. We validate the trained models directly at the level of the spectrum against explicit ab initio reference calculations and find that IR and Raman spectra converge with surprisingly small training sets. Moreover, spectral agreement improves more rapidly than the root-mean-square error (RMSE). While RMSE is straightforward to compute, statistically converged reference spectra are generally impractical to obtain, motivating the need to relate model-level errors to observable-level accuracy. By connecting these complementary error measures, we provide practical guidelines and early-stopping criteria for achieving sufficient spectral fidelity. By integrating response-tensor learning, automated training, and spectral-domain validation into a unified workflow, mimyria enables data-efficient and quantitatively reliable vibrational spectroscopy. Benchmark (surveying) ; Raman spectroscopy ; Tensor (intrinsic definition) ; Polarizability ; Mean squared error ; Spectral line ; Flexibility (engineering) ; Ab initio Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: R. Prasanna Venkatesh | D. Siva Sundhara Raja | KarthikPalanivel | K.K. Mohammed Rafat
Source: Powder Technology (article, 2026-02-06)
Field: Chemistry — Spectroscopy

Enhanced oxy-fuel combustion monitoring in FCC regenerators through deep learning and sensor drift compensation Combustion ; Compensation (psychology) ; Artificial neural network ; Noise (video) ; Filter (signal processing) ; Preprocessor ; Data pre-processing ; Soft sensor Spectroscopy and Laser Applications ; Advanced Combustion Engine Technologies ; Combustion and flame dynamics