jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Indrojit Paul | Asadul Islam Shimul | Bipul Chandra Biswas | Karim Kriaa | Mohamed Benghanem | Md. Azizur Rahman | Mekuria Tsegaye Alemu | Noureddine Elboughdiri
Source: Scientific Reports (article, 2026-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring optoelectronic and photovoltaic properties of Be3MF3 (M = P, As, Sb) perovskites via machine learning and numerical simulation This study presents a comprehensive Density Functional Theory (DFT) analysis of the structural, mechanical, electrical, and optical characteristics of cubic Be<sub>3</sub>MF<sub>3</sub> (M = P, As, Sb) perovskites, highlighting its potential for optoelectronic applications. The computed elastic constants adhere to the Born stability criterion, affirming mechanical resilience and ductility. Analyses of band structure and density of states reveal that all Be<sub>3</sub>MF<sub>3</sub> compounds exhibit indirect band gaps between 2.0 and 3.3 eV, categorizing them as wide-bandgap semiconductors suitable for high-power and UV optoelectronic applications. Optical investigations demonstrate significant absorption, especially in Be<sub>3</sub>SbF<sub>3</sub>, highlighting its promise as a lead-free absorbing material for solar cell applications. Device-level performance was modeled utilizing SCAPS-1D, incorporating Be<sub>3</sub>SbF<sub>3</sub> as the absorber across diverse configurations with four electron transport layers (In<sub>2</sub>S<sub>3</sub>, TiO<sub>2</sub>, Mg:ZnO, IGZO) and six-hole transport layers (Cu<sub>2</sub>O, MoO<sub>3</sub>, P3HT, NiO, PTAA, V<sub>2</sub>O<sub>5</sub>). The best configuration, Al/FTO/In<sub>2</sub>S<sub>3</sub>/Be<sub>3</sub>SbF<sub>3</sub>/Cu<sub>2</sub>O/Ni, attained a maximum power conversion efficiency (PCE) of 18.28%, with open-circuit voltage (V<sub>OC</sub>) of 1.79 V, short-circuit current (J<sub>SC</sub>) of 11.04 mA/cm<sup>2</sup>, and a fill factor (FF) of 89.6%. Subsequent examinations of defect density, interface states, thickness variation, and temperature impacts clarify charge transport and recombination kinetics. A Random Forest machine learning (ML) model was created to forecast device performance, with high accuracy (R<sup>2</sup> = 0.987, MSE = 0.00305, MAPE = 0.075) and pinpointing bandgap energy and interface defect density as critical limiting factors. The integrated DFT-SCAPS 1D-ML framework identifies Be<sub>3</sub>SbF<sub>3</sub> perovskites as viable, lead-free materials for advanced photovoltaic and optoelectronic technologies. Photovoltaic system ; Band gap ; Semiconductor ; Stability (learning theory) ; Current density ; Power (physics) ; Voltage ; Limiting ; Energy conversion efficiency ; Interface (matter) Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity ; 2D Materials and Applications
Authors: Ashutosh Tiwari | Widodo | Dyah Ika Krisnawati | Chih-Yu Chen | Tsung Rong Kuo
Source: Molecular Cancer (article, 2026-02-13)
Field: Materials Science — Biomaterials

AI-driven nanomedicine for cancer theranostics Nanomedicine's merging with artificial intelligence (AI) is fundamentally changing cancer theranostics through precise creation of multifunctional nanoparticles which can simultaneously diagnose and treat diseases. Traditional cancer treatments today face issues with imprecise delivery and general body toxicity as well as late-stage disease recognition which theranostic nanoplatforms address through precision drug transport and live imaging functions. In this analysis, we examine the existing state and forthcoming developments of AI applications in cancer theranostics through nanoparticles. Our study first examines the three primary classes of theranostic nanomaterials that show clinical significance: liposomes, gold nanoparticles, iron oxide nanoparticles, and quantum dots. AI is being applied to nanoparticle development through machine learning, deep learning, reinforcement learning, and generative models that support physicochemical predictions, synthesis optimization, biodistribution modeling, and inverse design. We analyze clinical applications of AI solutions which support patient identification, response predictions, and implementation of virtual patient models for individualized cancer treatment. The paper evaluates major difficulties which are nanotoxicity, AI explainability, data limitations, and regulatory concerns while addressing ethical dilemmas. Rather than a broad overview of nanomedicine, we center on AI methods that directly improve theranostic decisions and support this with worked exemplars reporting datasets, baselines, metrics, and clinical tie-ins. This Task–Data–Method–Metric lens replaces generic background and grounds claims in reproducible evidence. Nanomedicine ; Cancer ; Cancer therapy ; Cancer treatment ; Cancer imaging ; Precision medicine Nanoparticle-Based Drug Delivery ; Artificial Intelligence in Healthcare and Education ; Computational Drug Discovery Methods
Authors: Sompriya Chatterjee | Dhiman Ray
Source: The Journal of Physical Chemistry Letters (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning The conformational flexibility of RNA molecules enables them to play vital physiological roles, including carrying genetic information, catalyzing reactions, and forming organelles. However, this structural diversity complicates the quantitative sampling of their conformational landscape, even for simple single-stranded RNA tetramers. We show that combining explainable artificial intelligence (XAI) with enhanced sampling algorithms can effectively explore the complex free energy landscapes of RNA tetramers. Our simulations capture key conformational states, such as stacked, intercalated, nucleobase-flipped, and random coil structures, while reproducing unbiased populations with much less computational effort than conventional molecular dynamics. This data-driven approach distinguishes several metastable states that are often indistinguishable in standard analysis. Additionally, our interpretable machine learning framework identifies key torsion angles driving slow transitions and those responsible for unphysical intercalated structures, paving the way for improvements in nucleic acid force fields. Tetramer ; RNA ; Conformational ensembles ; Energy landscape ; Key (lock) ; Flexibility (engineering) ; Nucleic acid structure ; Molecular dynamics ; Nucleic acid Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Jian Cheng Wong | Chin Chun Ooi | Abhishek Gupta | Pao‐Hsiung Chiu | Joshua Shao Zheng Low | My Ha Dao | Y. J. Ong
Source: IEEE Transactions on Evolutionary Computation (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. However, today’s PINNs are often trained for a single physics task and require computationally expensive re-training for each new task, even for tasks from similar physics domains. To address this limitation, this paper proposes a pioneering approach to advance the generalizability of PINNs through the framework of Baldwinian evolution. Drawing inspiration from the neurodevelopment of precocial species that have evolved to learn, predict and react quickly to their environment, we envision PINNs that are pre-wired with connection strengths inducing strong biases towards efficient learning of physics. A novel two-stage stochastic programming formulation coupling evolutionary selection pressure (based on proficiency over a distribution of physics tasks) with lifetime learning (to specialize on a sampled subset of those tasks) is proposed to instantiate the Baldwin effect. The evolved Baldwinian-PINNs demonstrate fast and physics-compliant prediction capabilities across a range of empirically challenging problem instances with more than an order of magnitude improvement in prediction accuracy at a fraction of the computation cost compared to state-of-the-art gradient-based meta-learning methods. For example, when solving the diffusion-reaction equation, a 70x improvement in accuracy was obtained while taking 700x less computational time. This paper thus marks a leap forward in the evolutionary meta-learning of PINNs as generalizable physics solvers. Sample codes are available at. Generalizability theory ; Evolutionary algorithm ; Evolutionary computation ; Artificial neural network ; Evolutionary programming ; Generality ; Human-based evolutionary computation ; Genetic programming ; Computation ; Evolutionary robotics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: Chandan Kumar Singh | Ratnali Bania | Satyendra Deka | Monika Dwivedi | Anroop B. Nair | Trishna Bal | Prasenjit Maity | Pran Kishore Deb
Source: AAPS PharmSciTech (article, 2026-02-04)
Field: Materials Science — Biomaterials

Recent Trends in the Development and Clinical Translation of Polymer-based Targeted Therapeutic Nanoparticle Nanocarriers ; Drug delivery ; Drug ; Applications of artificial intelligence ; Nanoparticle ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization ; Nanoparticles: synthesis and applications
Authors: Chuang-Wei Liu | Jinzhe Li | Sen Li | Zhongqiu Guo | Yao Chen | Tian Li | Renchi Qin | Jiaxu Sun | Yongxi Lu | Fanbin Meng
Source: Advanced Science (article, 2026-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network‐Based Permittivity Engineering of Magnetic Absorbers for Customizable Microwave Absorption The development of high-performance microwave absorbers featuring ultrathin profiles and customizable bandwidth remains a formidable obstacle for cutting-edge electromagnetic stealth and long-term service applications. Traditional design approaches for absorbers often rely on inefficient trial-and-error methods, a challenge exacerbated in magnetic absorbers by the intricate coupling between permittivity and permeability. This work introduces a neural network-based permittivity engineering strategy, underpinned by a novel "permeability locking-permittivity optimization" paradigm that effectively decouples the interdependent electromagnetic parameters. A high-throughput permittivity feature space was constructed via tensor-based electromagnetic theory calculations, and a dual-task screening strategy was implemented to identify optimal and effective absorption conditions. This data-driven framework facilitated the inverse design of a magnetic composite, culminating in the guided synthesis of flaky carbonyl iron/barium titanate composites. The material experimentally demonstrates an exceptional effective absorption bandwidth of 5.1 GHz at an ultralow thickness of 1.0 mm, with an optimal reflection loss of -45.12 dB at a 1.9 mm. Furthermore, the formation of a protective Si─O─Si surface layer significantly enhances corrosion resistance, confirming practical durability. This study establishes an AI-guided paradigm that successfully bridges electromagnetic theory with materials design, offering a robust and generalizable platform for the accelerated development of advanced microwave absorption materials. Permittivity ; Microwave ; Reflection loss ; Electromagnetic radiation ; Absorption (acoustics) ; Bandwidth (computing) ; Electromagnetics ; Ceramic Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Robert Geitner | Christian Dreßler
Source: ACS Omega (article, 2026-01-16)
Field: Chemistry — Spectroscopy

Predicting <sup>31</sup> P NMR Shifts in Large-Scale, Heterogeneous Databases by Gas Phase DFT: Impact of Conformer and Solvent Effects In this study, we extend the Ilm-NMR-P31 data set by systematically enriching experimental <sup>31</sup>P nuclear magnetic resonance (NMR) data with quantum-chemically derived information. Using density functional theory (DFT) at the BLYP and B3LYP levels, we calculated <sup>31</sup>P chemical shifts and optimized molecular geometries for 10,007 phosphorus-containing compounds. This hybrid data set provides a consistent framework that combines experimental measurements with theoretical descriptors, thereby facilitating the development of improved machine learning (ML) models and advanced structure elucidation tools. To evaluate the reliability of the computational data, we compared the DFT-derived shifts against experimental values. Single-point calculations in vacuum resulted in a root mean squared error (RMSE) of 30.82 ppm, which improved to 29.37 ppm when up to 20 conformers per molecule were included. Implicit solvent models produced minor additional improvements. These findings highlight the importance of conformational sampling. Evaluation of different functionals on the Ilm-NMR-P31 database, including those closely reproducing coupled-cluster results, demonstrated that B3LYP not only achieves sufficient accuracy for reproducing NMR shifts across large experimental databases but also emerges as a broadly applicable choice for routine applications. Overall, this work provides a large-scale, quantum chemically enriched <sup>31</sup>P NMR data set that enhances the value of experimental data and supports future ML-based prediction models. Conformational isomerism ; Chemical shift ; Density functional theory ; Molecule ; Set (abstract data type) ; Work (physics) ; Dimension (graph theory) ; Experimental data ; Gas phase ; Root mean square Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Machine Learning in Materials Science
Authors: Jiayi Wang | Chunwei Dong | Xiaochuan Gou | Shaopeng Fu | Peng Yuan | Xin Song | Mohammad Bodiuzzaman | Mutalifu Abulikemu | Wanyu Lin | Ren-wu Huang | Omar F. Mohammed | Di Wang | Osman M. Bakr
Source: ACS Central Science (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Deep Learning Framework for Atomic-Level Design and Presynthesis Prediction of Coinage-Metal Nanoclusters The atomically precise nature of coinage-metal nanoclusters (CMNs) enables systematic exploration of structure-property relationships and motivates application oriented inverse design. However, the synthesis of CMNs typically relies on trial-and-error methods, with atomic-level structures only revealed through crystallography (postsynthesis), posing a major challenge to the deterministic synthesis of predesigned cluster structures, which is known as <b>inverse synthesis</b>. Here, we introduce CoLiM, a deep neural network framework that predicts the chemical compatibility between the unexplored inorganic core and ligands <i>before synthesis</i>. CoLiM employs a dual-encoder architecture and is trained on a newly constructed dataset comprising 1,989 reported CMN structures, supplemented by an additional gas-phase cluster dataset. The optimal CoLiM model achieves an area under the curve (AUC) exceeding 0.83 on a held-out test set, outperforming all of the baseline methods. To demonstrate its practical utility, CoLiM is applied to address the long-standing challenge of achieving atomically precise structural tailoring. Starting from [Cu<sub>20</sub>Cl-(PET)<sub>12</sub>(PPh<sub>3</sub>)<sub>4</sub>-(MeCOO)<sub>6</sub>]<sup>+</sup>, we successfully performed single-atom editing on its inorganic core to synthesize [Cu<sub>19</sub>Cl-(PET)<sub>12</sub>(PPh<sub>3</sub>)<sub>3</sub>-(HCOO)<sub>6</sub>] guided by the prediction of CoLiM, validating the model's generalizability under real experimental conditions. Our framework facilitates the inverse synthesis and precise atomic-level modification of nanoclusters, underscoring its substantial potential to accelerate rational nanocluster discovery. Nanoclusters ; Generalizability theory ; Deep learning ; Deep neural networks ; Cluster (spacecraft) ; Artificial neural network ; Inverse Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Alya A. Arabi | Anju Choorakottayil Pushkaran
Source: BMC Artificial Intelligence (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence models trained on data from molecular dynamics trajectories Artificial intelligence (AI) and machine learning (ML) models trained on molecular dynamics (MD) trajectories are increasingly used for predicting various properties reported from MD simulations. The reliability of such models depends critically on the quality and reproducibility of the underlying simulations. A frequent assumption in MD research is that longer single simulations necessarily yield more accurate results. In practice, unless mechanistic insights are the primary goal, multiple shorter, independent simulations are likely to provide a more representative sampling of highly complex potential energy surfaces, often characterized by numerous conformational states and local minima. These considerations are especially important for AI models, where biased or poorly sampled training data can propagate errors and reduce predictive power. This commentary discusses best practices in MD simulation design for training AI models that depend on MD data. Reliability (semiconductor) ; Sampling (signal processing) ; Quality (philosophy) ; Molecular dynamics ; Training set ; Energy (signal processing) ; Dynamics (music) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maxwell Pinczes | Dattatray S. Dhawale | Omer Elmutasim | Sarbjit Giddey | Sankar _P. Bhattacharya
Source: Progress in Materials Science (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Hydrogenous solid oxide fuel cells: role and evolution of anode materials The accelerating depletion of fossil fuel reserves and the growing impact of greenhouse gas emissions underscore the urgent need for sustainable energy carriers. Hydrogen and hydrogen-rich fuels such as ammonia, methane, and methanol, stand out due to their exceptional energy density and low environmental impact when produced from renewable sources. Solid oxide fuel cells (SOFCs) can leverage the high energy density of these hydrogenous fuels and offer high efficiencies by directly utilising these hydrogen-rich fuels over the anode. However, Hydrogenous SOFCs face challenges with these fuels, such as slow kinetics, anode deactivation through nitridation, sulphur and carburation, or stack component compatibility and manufacturing cost. This review presents a historical overview of cermets, perovskites, and high-entropy alloys (HEAs) as anode materials for SOFCs. It aims to provide a comprehensive understanding of why these materials are effective as anodes and to elucidate how improvements to anode design have improved and evolved over time. This review aims to collate the most recent studies into the mechanisms for hydrogenous fuel usage at a fundamental level. Additionally, the latest methodologies combining artificial intelligence and machine learning algorithms to enhance research efficacy within the SOFC field will be discussed. Recent literature highlights the ever-improving viability of directly utilising ammonia, methane, and methanol compared to hydrogen, due to the improvements in the anode. Among all studied anode materials, traditional nickel-based cermet anodes continue to display the highest power densities, albeit at the expense of carbon and sulphur tolerance compared to HEAs. Ultimately, challenges in anode stability and system integration persist, highlighting the need for continued investment and innovation. Anode ; Oxide ; Fuel cells ; Solid solution ; Solid oxide fuel cell Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Chemical and Physical Properties of Materials
Authors: Zhenli Sun | Shan Sun | Sheng Wang | Hanyang Wang | Yiyan Zhang | Jianwei Huang | Fuyou Fan | Yuan Yang | Xiangke Wang
Source: Science China Chemistry (article, 2026-03-23)
Field: Chemistry — Inorganic Chemistry

Prediction of uranium adsorption performance by machine learning for sustainable seawater extraction Uranium ; Adsorption ; Uranyl ; Extraction (chemistry) ; Support vector machine ; Boosting (machine learning) Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Dina A. Naser | Abd El-razek Mahmoud | Mohamed H. Essai Ali | G.A. Gamal
Source: Ceramics International (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

The prediction of energy storage parameters for (Ba0.85Ca0.15) (Ti0.9Zr0.1) O3 ceramic using artificial neural networks Artificial neural network ; Energy storage ; Energy (signal processing) ; Dielectric ; Electric field ; MATLAB ; Computer data storage ; Mean squared error Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Ferroelectric and Negative Capacitance Devices
Authors: Xiao Wang | Zikang Yan | Hao Si | dengdi sun | Wanli Lyu | Jin Yong Tang | Zhendong Yang | Q.  Q. Yang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network Thermal conduction ; Artificial neural network ; Heat flux ; Finite element method ; Divertor ; Process (computing) ; Sampling (signal processing) ; Thermal Fusion materials and technologies ; Magnetic confinement fusion research ; Model Reduction and Neural Networks
Authors: Yueqi Cao | Chongsen Duan | Jikang You | Fei Liu | Keli Yang | Xin Liu | Huacheng Xu
Source: Water Research (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Cascade machine learning reveals the regulatory mechanism of dissolved organic matter on nanoparticles aggregation behavior Cascade ; Steric effects ; Dissolved organic carbon ; Nanoparticle ; Particle aggregation ; Mechanism (biology) Nanoparticles: synthesis and applications ; Coagulation and Flocculation Studies ; Microplastics and Plastic Pollution