jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuai Wang | ChiYung Yam | Lihong Hu | Faan-Fung Hung | Shuguang Chen | Chi‐Ming Che | GuanHua Chen
Source: Materials Futures (article, 2025-02-06)
Field: Chemistry — Organic Chemistry

A general protocol for phosphorescent platinum(II) complexes: generation, high throughput virtual screening and highly accurate predictions Abstract The utilization of phosphorescent metal complexes as emissive dopants for organic light-emitting diodes (OLEDs) has been the subject of intense research. Cyclometalated Pt(II) complexes are particularly popular triplet emitters due to their color-tunable emissions. To make them viable for practical applications as OLED emitters, it is essential to develop Pt(II) complexes with high radiative decay rate constants ( k r ) and photoluminescence quantum yields (PLQY). To this end, an efficient and accurate prediction tool is highly desirable. In this work, we propose a general yet powerful protocol achieving metal complex generation, high throughput virtual screening (HTVS), and fast predictions with high accuracy. More than 3600 potential structures are generated in a synthesis-friendly manner. Moreover, three HTVS-machine learning (ML) models are established using different algorithms with carefully designed features that are suitable for metal complexes. Specifically, 30 potential candidates are filtered out by HTVS-ML models with a three-tier screening rule and put into accurate predictions with experimental calibration Δ-learning method. The highly accurate prediction approach further reduces the stress of experiments and inspires greater confidence in identifying the most promising complexes as excellent emitters. As a result, 12 promising complexes ( k r &gt; 10 5 s −1 and PLQY &gt; 0.6) with the same superior core structures are confirmed from over 3600 Pt-complexes. Experiments revealed that two very close complexes have excellent emission properties and are consistent with the prediction results, providing strong evidence for the efficacy of the proposed protocol. We expect this protocol will become a valuable tool, expediting the rational design and rapid development of novel OLED materials with desired properties. Throughput ; Protocol (science) ; Phosphorescence ; Platinum ; Computer science ; Chemistry ; Physics ; Catalysis ; Operating system ; Optics ; Medicine ; Wireless Organometallic Complex Synthesis and Catalysis ; Lanthanide and Transition Metal Complexes ; Analytical Chemistry and Sensors
Authors: Sibasish Chakraborty | Saptarshi Roy | Md. Ahmaruzzaman
Source: Journal of Water Process Engineering (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Advancing the frontier of spinel ferrites in sustainable remediation: Fundamentals, synthetic strategies, and mechanistic insights Spinel ; Leaching (pedology) ; Catalysis ; Wastewater ; Characterization (materials science) ; Redox Magnetic Properties and Synthesis of Ferrites ; Advanced oxidation water treatment ; Environmental remediation with nanomaterials
Authors: Farah Tamer Nasser | Ahmed Refaat Elsissy | W.M. Farouk
Source: The International Journal of Advanced Manufacturing Technology (article, 2025-10-21)
Field: Materials Science — Biomaterials

Comprehensive characterization, optimization, modelling, and validation for FDM of commercial PLA + for bone scaffolds Abstract Bone scaffolds require tailored mechanical and structural properties to support tissue regeneration. This study optimizes fused deposition modeling (FDM)-printed commercial poly-lactic acid plus (PLA +) scaffolds enhanced with calcium carbonate (CaCO₃) and barium sulfate (BaSO₄) by evaluating printing temperature (190–230 °C), infill density (20–60%), and raster angle (30°–90°). Using response surface methodology (RSM) and analysis of variance (ANOVA), infill density was identified as the most statistically significant parameter ( p &lt; 0.05), followed by raster angle and temperature. Optimal parameters (230 °C, [0,90]° raster angle, 60% infill density) achieved an elastic modulus of 1537 MPa and compressive strength of 128 MPa, surpassing conventional PLA scaffolds. Artificial neural networks (ANN) outperformed RSM in predictive accuracy, with single-output ANN models yielding high correlation coefficients (training, testing, validation). Differential scanning calorimetry (DSC) confirmed elevated crystallinity at 230 °C, while X-ray diffraction (XRD) identified semi-crystalline PLA, presence of BaSO 4 and CaCO 3 , and crystallinity shifts under compression. Fourier transform infrared spectroscopy (FTIR) revealed molecular interactions linked to strength variations, and thermogravimetric analysis (TGA)/derivative thermogravimetry (DTG) demonstrated improved thermal stability with CaCO₃/BaSO₄ additives (Weight loss: 70–74% at ≤ 210 °C vs. 92.21% at 230 °C). Scanning electron microscopy (SEM) and XRD correlated crystallinity with mechanical performance. By integrating ANN with RSM-driven validation, this work advances predictive modeling for FDM-based scaffold design, positioning commercial PLA + as a candidate for patient-specific bone tissue engineering. Crystallinity ; Differential scanning calorimetry ; Thermogravimetric analysis ; Scanning electron microscope ; Thermal stability ; Compressive strength ; Fourier transform infrared spectroscopy ; Thermogravimetry biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Bhanugoban Maheswaran | Komal Chawla | Abhishek Gupta | Ramathasan Thevamaran
Source: Extreme Mechanics Letters (article, 2025-05-17)
Field: Materials Science — Biomaterials

Implicit geometric descriptor-enabled ANN Framework for a unified structure-property relationship in architected nanofibrous materials Property (philosophy) ; Computer science ; Biological system ; Materials science ; Artificial intelligence ; Biology ; Philosophy ; Epistemology Calcium Carbonate Crystallization and Inhibition ; Material Selection and Properties ; Building materials and conservation
Authors: Hooman Shirmahd | Soroush Rafiee Rad | Sepehr Jahedi Manesh | Nazanin Sadat Hashemian | Ata Jamavari
Source: Journal of Alloys and Compounds (article, 2025-08-18)
Field: Chemistry — Organic Chemistry

Optimized synthesis and artificial intelligence-based prediction (ANN-MLP) of structural and magnetic properties in Fe₃O₄@SiO₂@MgAl-LDH nanocomposites: Experimental study and Monte Carlo-assisted modeling Monte Carlo method ; Nanocomposite ; Artificial neural network ; Materials science ; Artificial intelligence ; Computer science ; Nanotechnology ; Statistics ; Mathematics Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Layered Double Hydroxides Synthesis and Applications
Authors: Lukas Schulze Balhorn | Kevin Degens | Artur M. Schweidtmann
Source: Computers & Chemical Engineering (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence Control structure design is an important but tedious step in P&ID development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engineers. Previous research on generative AI in chemical process design mainly represented processes by sequences. However, graphs offer a promising alternative because of their permutation invariance. We propose the Graph-to-SFILES model, a generative AI method to predict control structures from flowsheet topologies. The Graph-to-SFILES model takes the flowsheet topology as a graph input and returns a control-extended flowsheet as a sequence in the SFILES 2.0 notation. We compare four different graph encoder architectures, one of them being a graph neural network (GNN) proposed in this work. The Graph-to-SFILES model achieves a top-5 accuracy of 73.2% when trained on 10,000 flowsheet topologies. In addition, the proposed GNN performs best among the encoder architectures. Compared to a purely sequence-based approach, the Graph-to-SFILES model improves the top-5 accuracy for a relatively small training dataset of 1,000 flowsheets from 0.9% to 28.4%. However, the sequence-based approach performs better on a large-scale dataset of 100,000 flowsheets. These results highlight the potential of graph-based AI models to accelerate P&ID development in small-data regimes but their effectiveness on industry relevant case studies still needs to be investigated. • Introduced Graph-to-SFILES model to predict control structures from flowsheets. • Uses flowsheet topology graph as input and outputs control structure in SFILES 2.0. • Proposed graph neural network outperforms other graph encoders on benchmark dataset. • Demonstrated significant improvement in small-data regimes over transformer model. • Results promising but further tests needed for industry-scale deployment. Network topology ; Generative grammar ; Artificial intelligence ; Graph ; Computer science ; Process (computing) ; Machine learning ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Business Process Modeling and Analysis
Authors: Elham M. A. Dannoun | R. M’nassri | Muaffaq M. Nofal
Source: Journal of Materials Science Materials in Electronics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

ANN-based investigation of the electric field dependence of electrocaloric performances in Mn/Y co-doped Ba0.67Sr0.33TiO3 ceramics Materials science ; Electrocaloric effect ; Ceramic ; Electric field ; Doping ; Condensed matter physics ; Engineering physics ; Composite material ; Optoelectronics ; Physics ; Ferroelectricity Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Amol Marotrao Kale | Jagadis Gautam | Jishu Rawal | Pooja Varma | Seung Jun Lee | Seul‐Yi Lee | Soo‐Jin Park
Source: Materials Science and Engineering R Reports (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Carbon nanostructures for photocatalytic degradation of organic pollutants: Synthesis strategies and functionalization techniques Surface modification ; Photocatalysis ; Heteroatom ; Nanostructure ; Bridging (networking) ; Quantum dot ; Heterojunction ; Carbon nanotube Carbon and Quantum Dots Applications ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Eaindra Lwin | Mathis Julian Gölz | Nils O. B. Lüttschwager | Martin A. Suhm | Silvan Käser | Valerii Andreichev | Magalie Brandes | Markus Meuwly
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Rotational, vibrational, conformational and diastereomeric dimer cooling of aminoalcohols in soft supersonic expansions and the monohydrate of dimethylaminoethanol Supersonic jet expansions allow to cool molecules and to form molecular complexes over a wide range of expansion conditions, ranging from nearly effusive expansions of the pure vapour to colder expansions in carrier gases. The resulting molecular species can be probed by infrared absorption and Raman scattering. They are not in thermal equilibrium, but one can assign effective average Boltzmann temperatures for rotational, selected vibrational and in low-barrier cases even conformational degrees of freedom. If the conformational energy difference is not known, one can at least follow the evolution of competing structures with expansion conditions and from this derive relative energy sequences. For aminoethanol and its <i>N</i>-methylated variants, we explore rotational band contour analysis in OH stretching fundamentals, intensity analysis of sum and difference transitions with scaffold modes, relative intensities of isomers and the evolution of transient relative chirality to estimate the associated Boltzmann temperatures or energy sequences. The focus is on trends rather than on highly accurate numbers, which anyway depend on details like nozzle geometry or precise nozzle distance. These trends can be used for a better understanding of the vibrational spectra of other hydrogen-bonded systems. We show that the B3LYP functional is not able to describe the diastereomeric energy sequence for the dimethylaminoethanol dimer and that thermal shifts of infrared bands due to the weakening of hydrogen bonding depend strongly on the hydrogen bond strain. We also discuss high-barrier cases of conformational isomerism, which resist supersonic cooling and allow for low-temperature spectroscopy of metastable isomers. We assign the OH stretching spectra of the monohydrate of dimethylaminoethanol with an unusually strong water downshift. Finally, one of the successful machine learning-based models of the first HyDRA blind challenge is applied and improved for predicting the position of its water OH stretch wavenumber. The original model, based on computed harmonic wavenumbers for moderately strong H-bonds leads to a difference of 461 cm<sup>-1</sup> whereas improvements based on VPT2 calculations for the base model reduce this to 49 cm<sup>-1</sup>. Supersonic speed ; Dimer ; Jet (fluid) ; Diastereomer ; Molecule ; Chemistry ; Range (aeronautics) ; Computational chemistry ; Molecular physics ; Thermodynamics ; Stereochemistry ; Materials science ; Physics ; Organic chemistry Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies
Authors: Jiayu Wang | Yao Lu | Xiaoya Wang | Siyan Liang | Jie Xiong | Liang Zhen | Li Liu
Source: Materials & Design (article, 2025-11-25)
Field: Materials Science — Metals and Alloys

Target-driven design of high strength yet corrosion resistant medium Mn steel via interpretable machine-learning • An interpretable machine learning framework encompassing data collection, data augmentation, model selection, and experimental validation was proposed to develop novel strong, ductile and corrosion resistant steel. • Shapley additive explanation (SHAP) based analysis identified that austenitization temperature serves as the dominant factor in determining the comprehensive properties of medium Mn steels. • The performance optimized steel possesses a heterogeneous microstructure with both lamellar and block morphology, and block austenite with rare Mn content is more resistant to galvanic corrosion. • The synchronously improved mechanical and corrosion properties of medium Mn steel were attributed to the continuous TRIP effect, moderate corrosion sites, and satisfactory corrosion driving force induced by heterogeneous microstructure. Corrosion-resistant steels with high strength and large ductility are desirable for industrial applications. In this study, an interpretable machine learning (ML) framework including data collection, data augmentation, model selection, and experimental validation was developed for predicting and optimizing the mechanical yet corrosion properties of medium Mn steels. Furthermore, a model interpretation based on Shapley additive explanation (SHAP) approach was utilized to analyze the feature importance. A significant finding in SHAP analysis is that the austenitization temperature serves as the key factor in tailoring mechanical yet corrosion properties. Microstructural analysis further reveals that the performance-optimized medium Mn steel shows a heterogeneous microstructure with both lamellar and block morphology. The block austenite with lower Mn content decreases its potential difference and corrosion sites compared with the lamellar microstructure for micro-galvanic corrosion. The heterogeneous austenite yields a sustainable transformation-induced plasticity (TRIP) effect during deformation and improved strength-ductility synergy. Thus, the mechanical and corrosion properties of medium Mn steels are synchronously enhanced. The interpretable ML strategy for identifying key variables affecting mechanical yet corrosion performance, along with heterogeneous microstructure design to improve the comprehensive properties in medium Mn steel, can also be applied to other materials systems. Microstructure ; Austenite ; Corrosion ; Lamellar structure ; Ductility (Earth science) ; Deformation (meteorology) Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Yang Wang | Chenyang Zhang | Shun‐Xin Li | Guanjun Xiao | Bo Zou
Source: Advanced Materials (article, 2025-12-18)
Field: Materials Science — Polymers and Plastics

Pressure‐Adaptive Artificial Synapses with High Linearity for Intelligent Computing in Extreme Environments The ongoing exploration of the physical world has intensified the demand for intelligent computing in extreme environments. However, intelligent devices operating under extreme high-pressure conditions are limited by the pressure tolerance of the materials used for intelligent computing. A pressure-adaptive artificial synapse (PAAS) using VO<sub>2</sub> (M<sub>1</sub>) nanoparticles is developed, leveraging the increased lattice rigidity during the M<sub>1</sub>-to-M<sub>1</sub>' phase transition (1 atm to 15.1 GPa), which causes the photoinduced insulator-to-metal transition to be Mott dominated. The PAAS demonstrated a stable operating current, a superior biomimetic plasticity (maximum paired-pulse facilitation index from 109.6% to 155.4%), and an improved postsynaptic current linearity (Pearson's r from 0.64 to 0.97) from 1 atm to 15.1 GPa. Furthermore, an artificial neural network mapped by PAAS under high pressure achieved a validation accuracy of 95%-97% in handwritten digit recognition. The PAAS is also applied to a convolutional autoencoder for denoising reconstruction of color images. Linearity ; Artificial neural network ; Convolutional neural network ; Artificial muscle ; Neuromorphic engineering ; Autoencoder ; Extreme learning machine ; GRASP Transition Metal Oxide Nanomaterials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Wanli Yang | Suxiang Ma | Sergio Gámez‐Valenzuela | Sang Young Jeong | Jin‐Woo Lee | Hao Cai | Rongjin Zhu | Bin Liu | Han Young Woo | Bumjoon J. Kim | Shu‐Jen Wang | Paddy K. L. Chan | Xugang Guo | Kui Feng
Source: Advanced Materials (article, 2025-10-04)
Field: Materials Science — Polymers and Plastics

Backbone Tailoring Enables High‐Performance and Stable n‐Type Organic Mixed Ionic‐Electronic Conductors for Synaptic Simulation and Biosensor High-performance and stable n-type organic mixed ionic-electronic conductors (OMIECs) are crucial for advancing organic electrochemical transistors (OECTs)-based low-power complementary circuits and biosensors, yet their development remains a great challenge. Herein, the study presents a series of donor-acceptor polymers incorporating bithiophene (BTI) and fused BTI derivatives with varying conjugation backbone lengths as acceptors. The mid-size fused BTI dimer enables polymer PBTI2g-DTCN with simultaneously improved ion-uptake capability, film structural order, and ion/electron transport capability. Consequently, an impressive electron mobility of 0.84 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> and a record figure-of-merit (µC*) of 287.8 F cm<sup>-1</sup> V<sup>-1</sup> s<sup>-1</sup> are achieved for PBTI2g-DTCN-based n-type conventional OECT in accumulation mode, while the vertical OECTs (vOECTs) attain a state-of-the-art area-normalized transconductance (g<sub>m,A</sub>) of 71.8 µS µm<sup>-2</sup> with remarkable operational stability. Through finely manipulating the channel components, the vOECTs demonstrate dual-mode operation, switching between non-volatile and volatile states. In non-volatile mode, vOECT-based artificial synapses with excellent ambient stability enable dynamic learning and are employed in convolutional neural networks for image recognition. In volatile mode, they excel in biosensing, monitoring electrocardiography and electromyography signals. These remarkable results demonstrate that backbone tailoring is a powerful strategy for developing high-performance n-type OMIECs for synaptic and sensor applications. Electrical conductor ; Conductive polymer ; Biosensor ; Polymer ; Electronic circuit ; Transistor ; Transconductance ; Electrode ; Electrochemistry ; Synapse Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Yubing Ouyang | Rongkai Ma | Zhifang Zhao | Yufeng Song | Ting Zhang | Tao Shi
Source: ACS Omega (article, 2025-03-18)
Field: Materials Science — Ceramics and Composites

The Investigation of Interlayer Water on the Drying Deformation of C–S–H: An ANN-Assisted MD Simulation Approach Drying shrinkage of the hardened cement mixture plays an important role in the durability of cementitious structures. However, the drying behavior of the cement paste remains unclear at the nanoscale. In this study, the effect of interlayer water on the drying deformation of C-S-H was investigated at the globule level (10<sup>-10</sup>-10<sup>-8</sup>m) via an artificial neural network-assisted molecular dynamics simulation approach. The present work revealed two drying deformation mechanisms of C-S-H from the perspective of the nanoscale and determined the transition threshold as RH ≈ 55%. Moreover, NaCl in the interlayer water was found to have a deleterious effect on the drying shrinkage at the nanoscale. Adding 25 wt % NaCl can increase the drying shrinkage to 54.3%. Overall, a prediction model with high accuracy was established to analyze the drying deformation of C-S-H under different moisture conditions, which explores the drying behavior at the nanoscale and paves the way for the multiscale application of drying shrinkage mitigations. Deformation (meteorology) ; Materials science ; Composite material ; Geotechnical engineering ; Geology Advanced ceramic materials synthesis ; Glass properties and applications ; Metallurgical Processes and Thermodynamics
Authors: Xuanjie Xia | Chuanting Qin | Enze Zhang | Bin Wang | Ting Wang | Yuan Lu
Source: Nano Materials Science (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

MXene/peptide biomimetic olfactory sensor array with machine learning for gas sensing MXene-based gas sensors have demonstrated great potential for applications in gas detection, food preservation, and medical diagnostics. However, they still suffer from limitations in selectivity and sensitivity. In this study, to enhance the sensing performance of MXene-based gas sensors, a biomimetic olfactory sensing system was successfully developed by functionalizing MXene materials with specific peptides derived from odorant-binding proteins and integrating sensor arrays with machine learning algorithms for improved gas recognition. Experimental results showed that the introduction of olfactory-derived peptides significantly enhanced sensor performance, increasing the overall response by 2 ​times to 4 times compared to pristine MXene. Moreover, the system exhibited broad applicability with a detection range spanning from as low as 50 ​ppb to as high as 500 ​ppm. Based on this sensing system, we successfully achieved freshness detection of pork and distinguished between lung cancer patients and healthy individuals using breath analysis, achieving a classification accuracy of up to 94%. This study provides a promising foundation for future applications in food safety monitoring and noninvasive medical diagnostics. Sensor array ; Electronic nose ; Optical sensing ; Pattern recognition (psychology) ; Deep learning MXene and MAX Phase Materials ; Polydiacetylene-based materials and applications ; Gas Sensing Nanomaterials and Sensors