jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rui Liu | Lin Shu | Jing Wan | Le Li | Guoqiang Zhang | Huasong Qin | Yilun Liu
Source: Thin-Walled Structures (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials Mechanical design ; Computer science ; Machine learning ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Anjana S. Desai | Aparna Ashok | Shivali Amit Wagle | Neeru Bhagat | Zhadyra Ashirova | Zhanna Abdrassulova | Nurshat Abdolla | Zhazira Mukazhanova | Alibek Ydyrys | Ainur Seilkhan
Source: Advanced Composites and Hybrid Materials (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Studying the wound healing property of biologically and chemically synthesized gold nanoparticles through scratch assay and neural network modeling Scratch ; Colloidal gold ; Wound healing ; Nanoparticle ; Artificial neural network ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence ; Medicine ; Composite material Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection
Authors: Yifeng Zhang | Jie Tian | Guanyu Li | Dongyang Ji | Chen Sun | Zeng Fan | Lujun Pan
Source: ACS Applied Materials & Interfaces (article, 2025-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design Principles for Gradient Porous Carbon on Aqueous Zinc-Ion Hybrid Capacitors: A Combined Molecular Dynamic and Machine Learning Study Gradient porous carbon has become a potential electrode material for energy storage devices, including the aqueous zinc-ion hybrid capacitor (ZIHC). Compared with the sufficient studies on the fabrication of ZIHCs with high electrochemical performance, there is still lack of in-depth understanding of the underlying mechanisms of gradient porous structure for energy storage, especially the synergistic effect of ultramicropores (<1 nm) and micropores (1-2 nm). Here, we report a design principle for the gradient porous carbon structure used for ZIHC based on the data-mining machine learning (ML) method. It is clarified that the combination of 0.6-0.9 nm ultramicropore and 1.6 nm micropore achieves the highest specific capacity. Molecular dynamic simulation was further employed to investigate the electric double-layer structures in several kinds of electrified gradient porous carbon electrode/electrolyte interface. It is found that the Zn<sup>2+</sup> ions in the 1.6 nm micropore balance the most charges of the electrode surface as the counterion with the modification of the solvation structure. Furthermore, the ML-based force field is trained and employed in the simulation of the ion charging dynamic in the gradient porous carbon electrode. Based on the free energy profile result, the remarkable benefit of the step-by-step desolvation process is found in the 0.86 and 1.6 nm gradient porous structure, which could be the origin of the enhanced ion charging dynamic and better capacity retention performance. Materials science ; Capacitor ; Aqueous solution ; Porosity ; Carbon fibers ; Zinc ; Ion ; Chemical engineering ; Nanotechnology ; Inorganic chemistry ; Composite material ; Metallurgy ; Organic chemistry ; Electrical engineering ; Composite number ; Voltage ; Engineering Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Conducting polymers and applications
Authors: Christian Ebere Enyoh | Qingyue Wang
Source: Separation and Purification Technology (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

Box–Behnken design and machine learning optimization of PET fluorescent carbon quantum dots for removing fluoxetine and ciprofloxacin with molecular dynamics and docking studies as potential antidepressant and antibiotic Box–Behnken design ; Ciprofloxacin ; Docking (animal) ; Fluorescence ; Fluoxetine ; Nanotechnology ; Quantum dot ; Chemistry ; Combinatorial chemistry ; Materials science ; Antibiotics ; Biochemistry ; Physics ; Medicine ; Response surface methodology ; Chromatography ; Quantum mechanics Carbon and Quantum Dots Applications ; Tryptophan and brain disorders ; Cholinesterase and Neurodegenerative Diseases
Authors: Ricardo A. García Cárcamo | Jiexin Shi | Ali Estejab | Tianjun Xie | Sanchari Bhattacharjee | Sayani Biswas | Cameron J. Bodenschatz | Xiuting Chen | Manish Maurya | Xiaohong Zhang | Rachel B. Getman
Source: ACS Catalysis (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

A Perspective on Multiscale Modeling of Explicit Solvation-Enabled Simulations of Catalysis at Liquid–Solid Interfaces Catalysis at liquid–solid interfaces is profoundly influenced by the interfacial solvent structure, which affects catalytic activity, selectivity, and reaction pathways. This perspective discusses state-of-the-art multiscale modeling methods that integrate quantum mechanics and molecular mechanics approaches to apply explicit solvent molecules to capture these interfacial phenomena. Specifically, the construction of multiscale models, the importance of capturing the interfacial solvent structure, and the computational strategies used to achieve this are explored, and the challenges in balancing chemical accuracy with computational expense are highlighted. Additionally, this perspective addresses the limitations of current methods. Opportunities for integrating machine learning are proposed. By advancing the efficiency and user friendliness of multiscale modeling, it is argued that deeper insights into heterogeneous catalysis in the liquid phase can be provided, which will ultimately contribute to the development of more efficient catalytic processes. Solvation ; Catalysis ; Perspective (graphical) ; Materials science ; Multiscale modeling ; Nanotechnology ; Chemistry ; Chemical physics ; Computational chemistry ; Computer science ; Organic chemistry ; Molecule Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: Yuanting Wu | Xuhua Liu | Jinrong Wang | Bocheng Zhang | Xinmeng Zhang | Ou Hai
Source: Journal of Energy Storage (article, 2025-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Layered double hydroxide/polypyrrole with neural-like transmission network for high-performance supercapacitor electrode Supercapacitor ; Polypyrrole ; Electrode ; Materials science ; Hydroxide ; Nanotechnology ; Chemical engineering ; Electrochemistry ; Chemistry ; Engineering Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Beatriz Mouriño | Sauradeep Majumdar | Xin Jin | Fergus Mcilwaine | Joren Van Herck | Andres Ortega‐Guerrero | Susana García | Berend Smit
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Exploring the chemical design space of metal–organic frameworks for photocatalysis In this work, we introduce a combined DFT and machine learning approach to obtain insights into the chemical design of metal-organic framework (MOF) photocatalysts for hydrogen (HER) and oxygen (OER) evolution reactions. To train our machine learning models, we evaluated a dataset of 314 MOFs using a dedicated DFT workflow that computes a set of five descriptors for both closed and open shell MOFs. Our dataset is composed of a diverse selection of the QMOF database and experimentally reported MOF photocatalysts. In addition, to ensure a balanced dataset, we designed a set of MOFs (CDP-MOF) inspired by insights obtained regarding different types of photocatalytic materials. Our machine-learning approach allowed us to screen the entire QMOF and CDP-MOF databases for promising candidates. Our analysis of the chemical design space shows that we have many materials with a suitable spatial overlap of electron and hole, band gap, band-edge alignment to HER, and charge-carrier effective masses. However, we have identified in the QMOF database only a very small percentage of materials that also have the right band-edge alignment to OER. With the CDP-MOF database, we successfully targeted building blocks that potentially have the correct OER band alignment, and indeed obtained a larger percentage of materials that obey these criteria. Among those, a few motifs stood out, such as Au-pyrazolate, Ti clusters and rod-shaped metal nodes, and a particular MOF designed with the Mn<sub>4</sub>Ca cluster, which mimics the OER center in the photosystem II of photosynthesis. Photocatalysis ; Chemical space ; Metal-organic framework ; Space (punctuation) ; Metal ; Hydrogen ; Work (physics) ; Computer science ; Materials science ; Chemistry ; Engineering ; Mechanical engineering ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Kevin P. Greenman | Ava P. Amini | Kevin Yang
Source: PLoS Computational Biology (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Benchmarking uncertainty quantification for protein engineering Machine learning sequence-function models for proteins could enable significant advances in protein engineering, especially when paired with state-of-the-art methods to select new sequences for property optimization and/or model improvement. Such methods (Bayesian optimization and active learning) require calibrated estimations of model uncertainty. While studies have benchmarked a variety of deep learning uncertainty quantification (UQ) methods on standard and molecular machine-learning datasets, it is not clear if these results extend to protein datasets. In this work, we implemented a panel of deep learning UQ methods on regression tasks from the Fitness Landscape Inference for Proteins (FLIP) benchmark. We compared results across different degrees of distributional shift using metrics that assess each UQ method's accuracy, calibration, coverage, width, and rank correlation. Additionally, we compared these metrics using one-hot encoding and pretrained language model representations, and we tested the UQ methods in retrospective active learning and Bayesian optimization settings. Our results indicate that there is no single best UQ method across all datasets, splits, and metrics, and that uncertainty-based sampling is often unable to outperform greedy sampling in Bayesian optimization. These benchmarks enable us to provide recommendations for more effective design of biological sequences using machine learning. Machine learning ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Uncertainty quantification ; Bayesian optimization ; Benchmarking ; Bayesian inference ; Inference ; Deep learning ; Sampling (signal processing) ; Bayesian probability ; Data mining Machine Learning in Materials Science ; Machine Learning and Algorithms ; Protein Structure and Dynamics
Authors: Ankit Roy | Giridhar Nandipati | Andrew M. Casella | David J. Senor | Ram Devanathan | Ayoub Soulami
Source: npj Materials Degradation (review, 2025-01-02)
Field: Materials Science — Materials Chemistry

A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components Abstract This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects on damage evolution in TPBAR components, recommending suitable potentials and discussing advancements for materials in extreme radiation environments. Molecular dynamics ; Cascade ; Interatomic potential ; Displacement (psychology) ; Radiation damage ; Quantum ; Computer science ; Physics ; Radiation ; Statistical physics ; Materials science ; Chemistry ; Computational chemistry ; Nuclear physics ; Quantum mechanics Nuclear Materials and Properties ; Fusion materials and technologies ; Ion-surface interactions and analysis
Authors: Hirendra Das | Mridusmita Sharma
Source: ACS Applied Electronic Materials (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques The rapid growth of nanotechnology has enabled the design and synthesis of materials on the nanoscale, transforming various scientific fields. However, the complex behavior of nanostructures, coupled with challenges in material design and property prediction, limits its full potential. Traditional trial-and-error methods are often resource-intensive and inefficient, necessitating computational approaches to accelerate material discovery and optimization. Recently, soft computing techniques (SCTs) such as machine learning (ML), genetic algorithms (GAs), fuzzy logic (FL), and artificial neural networks (ANNs) have emerged as powerful tools to address these challenges. These data-driven frameworks enable accurate property prediction, synthesis optimization, and rapid identification of novel nanomaterials, reducing reliance on costly experiments. Hybrid approaches that integrate ML, GAs, and FL further enhance multiobjective optimization and predictive modeling, allowing researchers to explore vast design spaces efficiently. These integrated techniques have been successfully applied to nanophotonic devices, energy storage materials, and catalytic systems, demonstrating their ability to tackle long-standing challenges in nanoscience and nanotechnology. This review, focusing primarily on studies published in the last five years, explores the transformative role of SCTs in computational nanotechnology, highlighting key applications, challenges, and future directions. Additionally, issues such as data availability, model interpretability, and scalability are discussed along with potential solutions. By consolidating recent advancements, this work provides a comprehensive roadmap for leveraging SCTs in materials science, paving the way for the design and development of next-generation high-performance materials and functional nanostructures. Property (philosophy) ; Nanotechnology ; Computer science ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Memory and Neural Computing
Authors: Ziyang Wang | Jeewan C. Ranasinghe | Wenjing Wu | Dennis Chan | Ashley Gomm | Rudolph E. Tanzi | Can Zhang | Nanyin Zhang | Genevera I. Allen | Shengxi Huang
Source: ACS Nano (article, 2025-04-15)
Field: Chemistry — Analytical Chemistry

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpretation remains a challenge. Machine learning methods have gained prominence in spectral analyses, efficiently unveiling analyte compositions. However, these methods still face challenges in interpretability, particularly in generating clear feature importance maps that highlight the spectral features specific to each class of data. These limitations arise from feature noise, model complexity, and the lack of optimization for spectroscopy. In this work, we introduce a machine learning algorithm─logistic regression with peak-sensitive elastic-net regularization (PSE-LR)─tailored for spectral analysis. PSE-LR enables classification and interpretability by producing a peak-sensitive feature importance map, achieving an F1-score of 0.93 and a feature sensitivity of 1.0. Its performance is compared with other methods, including k-nearest neighbors (KNN), elastic-net logistic regression (E-LR), support vector machine (SVM), principal component analysis followed by linear discriminant analysis (PCA-LDA), XGBoost, and neural network (NN). Applying PSE-LR to Raman and photoluminescence (PL) spectra, we detected the receptor-binding domain (RBD) of SARS-CoV-2 spike protein in ultralow concentrations, identified neuroprotective solution (NPS) in brain samples, recognized WS<sub>2</sub> monolayer and WSe<sub>2</sub>/WS<sub>2</sub> heterobilayer, analyzed Alzheimer's disease (AD) brains, and suggested potential disease biomarkers. Our findings demonstrate PSE-LR's utility in detecting subtle spectral features and generating interpretable feature importance maps. It is beneficial for the spectral characterization of materials, molecules, and biosamples and applicable to other spectroscopic methods. This work also facilitates the development of nanodevices such as nanosensors and miniaturized spectrometers based on nanomaterials. Spectroscopy ; Interpretation (philosophy) ; Materials science ; Logistic regression ; Machine learning ; Analytical Chemistry (journal) ; Artificial intelligence ; Biological system ; Nanotechnology ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Physics ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ming‐Zhi Zhang | Yue Zhao | Chun‐Li Hu | Jiang‐Gao Mao
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

BaSbBS<sub>4</sub>: a record-high-performance birefringent crystal identified by a target-driven closed-loop strategy Exploring infrared (IR) birefringent materials with both large birefringence (Δ<i>n</i>) and wide band gaps (<i>E</i> <sub>g</sub>) is urgently demanded for high-power optoelectronic applications and has long been a tough challenge due to the intrinsic contradictory relationship between the two metrics. Herein, we developed a target-driven closed-loop framework in coupling with functional motif and crystal structure screening, deep learning assisted high-throughput optical property computation, targeted experiment and mechanism investigation, enabling efficient discovery of potential birefringent materials. Utilizing it, a batch of superior IR birefringent crystals containing planar [BS<sub>3</sub>]<sup>3-</sup> and/or stereochemically active lone pair (SCALP) groups ([SbS<sub>3</sub>]<sup>3-</sup>, [SnS<sub>3</sub>]<sup>4-</sup>, <i>etc.</i>) were identified: six with huge birefringence (Δ<i>n</i> > 1.0) and three with both large birefringence (Δ<i>n</i> > 0.5) and wide band gaps (<i>E</i> <sub>g</sub> > 3.5 eV). Remarkably, benefiting from a maximal synergy of [BS<sub>3</sub>]<sup>3-</sup> and [SbS<sub>3</sub>]<sup>3-</sup> motifs achieved by an optimal assembly of 1D [SbBS<sub>4</sub>]<sub>∞</sub> chains, BaSbBS<sub>4</sub> was highlighted and then validated experimentally as the most promising IR birefringent crystal, unlocking a record high birefringence in the wide-band-gap range (Δ<i>n</i> = 0.95 & 2.70 eV). This work not only discovers new high-performance birefringent crystals but also offers a universal avenue for precise and efficient evaluation of optical functional materials. Birefringence ; Loop (graph theory) ; Closed loop ; Crystal (programming language) ; Materials science ; Crystallography ; Computer science ; Physics ; Chemistry ; Optics ; Mathematics ; Control engineering ; Engineering ; Combinatorics ; Programming language Crystal Structures and Properties ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Han Fu | Amin Mojiri | Junli Wang | Zhe Zhao
Source: Energies (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Hydrogen storage ; Energy storage ; Architecture ; Carbon fibers ; Process engineering ; Hydrogen ; Computer science ; Chemical engineering ; Environmental science ; Waste management ; Materials science ; Nanotechnology ; Engineering ; Chemistry ; Organic chemistry ; Power (physics) ; Composite material ; Physics Hydrogen Storage and Materials ; Fuel Cells and Related Materials ; MXene and MAX Phase Materials
Authors: Luis Itza Vazquez-Salazar | Silvan Käser | Markus Meuwly
Source: npj Computational Materials (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Outlier-detection for reactive machine learned potential energy surfaces Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods-Ensembles, deep evidential regression (DER), and Gaussian Mixture Models (GMM)-were applied to the H-transfer reaction between <i>syn</i>-Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the best results for detecting outliers, followed by GMM. For example, from a pool of 1000 structures with the largest uncertainty, the detection quality for outliers is ~90% and ~50%, respectively, if 25 or 1000 structures with large errors are sought. On the contrary, the limitations of the statistical assumptions of DER greatly impact its prediction capabilities. Finally, a structure-based indicator was found to be correlated with large average error, which may help to rapidly classify new structures into those that provide an advantage for refining the neural network. Outlier ; Anomaly detection ; Artificial intelligence ; Artificial neural network ; Computer science ; Gaussian ; Pattern recognition (psychology) ; Energy (signal processing) ; Mixture model ; Biological system ; Mathematics ; Statistics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions