jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jithin D. George | Vinod K. Sangwan | Dilara Meli | Willa Brenneis | Heather Kurtz | Siyang Li | Thanh Bình Nguyễn | Jeffrey Lopez | Jeffrey Richards | Lincoln J. Lauhon | Mark C. Hersam | Maria K. Y. Chan | Valerie Taylor
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

What Complex Analysis Can Tell Us about Electrochemical Impedance Spectroscopy Electrochemical Impedance Spectroscopy (EIS) is a standard non-invasive technique widely used to understand electronic and ionic transport mechanisms in diverse material systems. There have been several approaches, particularly using machine learning, to classify and categorize the complex-valued data that is produced through EIS. Existing approaches typically have been optimized to specific materials systems, thus lacking general applicability of the classification framework. In this work, we describe a novel mathematical framework that allows us to discover key features within the EIS data. This framework builds on the fundamental principles of complex analysis and recent advances in numerical rational function approximation, to extract key mathematical properties of material systems directly from their EIS data, without relying on the knowledge of equivalent circuit models. We look at ways to ascertain the presence of imperfect capacitors i.e constant phase elements and Warburg elements. We explore questions about the identifiability and uniqueness of equivalent circuit models that can produce the EIS impedance data. We highlight results using both synthetic data and experimental data. The experimental data we analyze is obtained from diverse material systems including Lithium ion batteries and electrodes coated with a conjugated polymer. We also compare our results with those obtained by standard machine learning approaches and highlight the complementary insights that our mathematical framework can provide. Equivalent circuit ; Electrical impedance ; Dielectric spectroscopy ; Capacitor ; Identifiability ; Function (biology) ; Experimental data ; Key (lock) ; Categorization Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advancements in Battery Materials
Authors: Z. Y. Xu | Feizhou He | Tao Hu | Sansan Shuai | Xuan Ge | Jiang Wang | Zhongming Ren
Source: The Journal of Chemical Physics (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Anomalous expansion of interatomic distance in liquid Al–Zn alloy during cooling The structural and physical/chemical properties of metallic materials are closely linked to the composition and configuration of their molten state. In this study, the evolution of the local structure in Al-Zn alloys with varying compositions during the quenching process was investigated using on-the-fly machine learning force field (MLFF) simulations based on ab initio molecular dynamics. The results indicate that the first coordination shell of the Al-Zn alloy melt undergoes an anomalous expansion within a specific temperature range, which deviates from the previously reported linear negative expansion, such as metallic Al, Zn, and Sn. The temperature interval of the anomalous expansion decreases with increasing concentration of Zn. Local structural changes, including the abnormal increase in coordination number and the emergence of a shoulder in the second peak of the bond angle distribution function, further confirmed the presence of the anomalous expansion. In addition, the slope of the energy-temperature relationship and the activation energy of diffusion change upon temperature decrease, which suggests that this phenomenon is associated with atomic diffusion driven by thermal excitation. The anomalous expansion is also confirmed in a larger system (over 104 atoms) by MLFF simulations with first-principles accuracy. We found that an increase in cluster radius induces a decrease in pressure due to the interfacial energy of the cluster at ∼1200-1100 K for Al-38Zn melts, which in turn leads to a looser arrangement of the atoms. This study provides valuable insights into the mechanisms governing atomic structure evolution and liquid-liquid transitions. Cluster expansion ; Thermal expansion ; Quenching (fluorescence) ; Materials science ; Molecular dynamics ; Diffusion ; Cluster (spacecraft) ; Alloy ; Thermodynamics ; Ab initio ; Chemical physics ; Atmospheric temperature range ; Radial distribution function ; Pair distribution function ; Condensed matter physics ; Chemistry ; Metallurgy ; Computational chemistry Material Dynamics and Properties ; Thermodynamic and Structural Properties of Metals and Alloys ; nanoparticles nucleation surface interactions
Authors: Auguste Tetenoire | Andrea Vezzosi | Virginia Carnevali | Rothlisberger, Ursula
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Long-Time Scale Molecular Dynamics of Large-Scale Simulations Enhanced by Machine Learning of Hydroxylated Tin Oxide with Additives for Solar Cell Devices International audience Scale (ratio) ; Molecular dynamics ; Oxide ; Dynamics (music) ; Tin oxide ; Nanoscopic scale Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Chemical and Physical Properties of Materials
Authors: Andrew
R. Ickes (1766569) | Jordan P. Liles (11166297) | Niginia Borlinghaus (9225010) | Jeremy Henle (7905863) | Rafal Swiatowiec (9738759) | Niharika Prakash Kaushik (21820164) | Wilfried M. Braje (3075879) | Kaid C. Harper (1990219) | Shashank Shekhar (598479) | Matthew S. Sigman (1306671)
Source: Figshare (article, 2025-07-30)
Field: Chemistry — Inorganic Chemistry

Leveraging Data Scienceto Elucidate Ligand Featuresfor Pd-Catalyzed Enantioretentive <i>N</i>‑Arylationsof Cyclic α‑Substituted Amines in Aqueous Media The combination of high-throughput experimentation (HTE) and data science offers a promising solution for the optimization of challenging chemical reactions, although current data science approaches to deal with highly skewed data sets resulting from typical HTE campaigns are limited. One such attractive yet challenging reaction is the preparation of chiral tertiary anilines via palladium-catalyzed <i>N</i>-arylation of enantioenriched, α-substituted secondary amines. While enantioenriched tertiary anilines are highly valuable motifs in medicinal chemistry, their synthesis remains challenging to accomplish in high yields and enantiospecificity. Herein, we disclose a method for the enantioretentive <i>N</i>-arylation of cyclic secondary amines. After an extensive HTE campaign, a novel phosphorinane ligand for palladium, ‘NiniPhos,’ was found to demonstrate high yields (up to 96%) and enantiospecificity (96 to >99% es) across a range of amines with diverse aryl halides under aqueous conditions. Despite screening more than 120 ligands, only 31 demonstrated catalytic activity (>10% yield), resulting in a highly skewed data set. By applying data science-driven hotspot analysis and machine learning (linear support vector machines), the structural features of ligands leading to the activity cliff were identified, pinpointing di<i>-tert</i>-butylphosphines and phosphorinanes as privileged ligand classes in this reaction. This workflow offers a generalizable strategy for extracting mechanistic insights from skewed data sets, which can be leveraged for reaction design and optimization. Workflow ; Aryl ; Aqueous medium ; Ligand (biochemistry) ; Halide ; Profiling (computer programming) ; Drug discovery Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalytic Cross-Coupling Reactions
Authors: V. Castillo-Gallardo | J. E. Frias-Castro | L.F. Ávalos-Ruíz | Jesús Álvarez | J. F. Gómez‐Aguilar | Vivechana Agarwal
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI supported identification and adulteration monitoring platform for alcoholic beverage using porous silicon photonic structures Artificial neural network ; Identification (biology) ; Photonics ; Pattern recognition (psychology) ; Porous silicon ; Alcohol Silicon Nanostructures and Photoluminescence ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chao Shen | Kang Yang | Wenkang Zhan | Bo-Wei Xu | Zhaonan Li | Shujie Pan | Siming Chen | Zhanguo Wang | Chao Zhao
Source: Progress in Quantum Electronics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Advances in intelligent epitaxy of semiconductor materials Epitaxy ; Transformative learning ; Semiconductor ; Characterization (materials science) ; Quality (philosophy) ; Control (management) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Xinliang Yu | Jiyong Deng
Source: Journal of Polymer Science (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Predicting the Flory–Huggins Parameter for Polymer–Solvents From Quantum Chemical Descriptors ABSTRACT This study develops a predictive model for the Flory–Huggins interaction parameter ( χ ) using quantum chemical descriptors and accounting for temperature effects. A dataset of 2474 χ values across 19 polymers and 88 solvents was utilized. An optimized convolutional neural network (CNN) model, incorporating 30 selected features, demonstrated superior performance, achieving a mean absolute error (MAE) of 0.140, a root mean squared error (RMSE) of 0.177, and a coefficient of determination ( R 2 ) of 0.973 on the test set. These results represent a significant improvement over existing quantitative structure–property relationship (QSPR) models (particularly for those with test set sizes exceeding 150 samples, which typically report RMSE &gt; 0.25 and R 2 &lt; 0.94). Mechanism analysis revealed that χ is primarily governed by charge‐related properties (e.g., the most positive hydrogen charge in the polymer, H_p, and the most negative atomic charge in the solvent, N_s), polarity properties (polymer and solvent dipole moments, μ_p and μ_s), and solvent thermal energy (E_s). These factors collectively regulate the balance between specific molecular interactions and disorder effects. This study delivers not only a state‐of‐the‐art predictive tool but also physical insight, establishing a new paradigm for the rational design of advanced polymer materials. Mean squared error ; Quantum chemical ; Polarity (international relations) ; Dipole ; Polymer ; Mean absolute error ; Correlation coefficient ; Quantum ; Test set ; Artificial neural network Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Polymer Synthesis and Characterization
Authors: Huiyu Liu | Muyao Yu | Xiangyu Zhang | Y. Sun | Y. Ling | Zezhao Xu | Zidi Wang | Na Yu | Yue‐Biao Zhang | Ping Huai | Qisheng Wang | Xuming He | Linjiang Chen | Weihong Zhu | Shan Jiang
Source: ChemRxiv (preprint, 2025-02-20)
Field: Materials Science — Materials Chemistry

Data-driven Discovery of Polar Organic Cocrystals: Integration of Machine Learning and Automated Screening Polar organic cocrystals hold great promise for various advanced technological applications. However, their relatively low prevalence highlights the challenges of achieving the desired polar packing arrangements, making their discovery complex and demanding. Here, we present a data-driven approach that integrates machine learning (ML) with high-throughput (HT) automation to accelerate the discovery of polar organic cocrystals. Using ML methodologies, we identified key parameters governing polar cocrystal formation, enabling the targeted selection of molecular candidates. A total of 20 cocrystal combinations with chloranilic acid (CA) were explored, with 20 solvent systems screened for each combination, enabling a highly efficient selection process across a vast chemical space. HT automation further streamlined the synthesis and characterization processes through rapid screening and accurate structural validation, while comprehensively exploring the chemical landscape. Experimental validation yielded 16 new hydrogen-bonded cocrystals, 8 in polar space groups, achieving a polar cocrystal discovery rate (50%) over three times higher than the Cambridge Structural Database (CSD) average (~14%). his integrated approach represents a new approach in polar organic cocrystal research. The findings highlight the potential of this approach for advancing functional molecular materials, paving the way for next-generation applications using polar organic cocrystals. Polar ; Artificial intelligence ; Computer science ; Machine learning ; Data science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: M. Hjiri | Nazir Mustapha
Source: Journal of Sol-Gel Science and Technology (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Sol–gel derived ZnO nanoparticles doped with transition and alkaline-earth metals: a comprehensive review on dielectric and electrical properties Alkaline earth metal ; Dielectric ; Doping ; Nanoparticle ; Materials science ; Sol-gel ; Earth (classical element) ; Chemical engineering ; Nanotechnology ; Engineering physics ; Optoelectronics ; Metallurgy ; Metal ; Physics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Yu Xuemei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Enabled Smart Chemical and Materials Intelligence for Sustainable Performance-Enhancing Sports Materials Resource efficiency ; Embodied energy ; Resource (disambiguation) ; Raw material ; Efficient energy use ; Materials processing ; Material efficiency ; Component (thermodynamics) ; Biomimetics Machine Learning in Materials Science ; Polymer Foaming and Composites ; Polymer crystallization and properties
Authors: Leonid Komissarov (10798712) | Nenad Manevski (276406) | Katrin Groebke Zbinden (14322622) | Lisa Sach-Peltason (1747699)
Source: Figshare (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Explainable GraphNeural Networks in Chemistry: CombiningAttribution and Uncertainty Quantification Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of prediction uncertainty are critical for practical applications, for example in iterative lab-in-the-loop scenarios. We systematically evaluate different posthoc feature attribution methods and study their integration with uncertainty quantification in GNNs for chemistry. Our findings reveal a strong synergy: attributing uncertainty to specific input features (atoms or substructures) provides a granular understanding of model confidence and highlights potential data gaps or model limitations. We evaluated several attribution approaches on aqueous solubility and molecular weight prediction tasks, demonstrating that methods like Feature Ablation and Shapley Value Sampling can effectively identify molecular substructures driving prediction and its uncertainty. This combined approach significantly enhances the interpretability and actionable insights derived from chemical GNNs, facilitating the design of more useful models in research and development. Interpretability ; Uncertainty quantification ; Attribution ; Feature (linguistics) ; Graph ; Artificial neural network ; Limit (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ya-Hsueh Chien | Chuang-Chien Chiu | Chiu-Yue Lin | Te Jui Yen | Ching‐Ta Lu
Source:  (article, 2025-07-16)
Field: Materials Science — Polymers and Plastics

Therapeutic Effect Prediction of Focused Ultrasound on Skin Tightening Human skin will gradually loosen with age. Knowing how the skin's firmness can be improved and become beautiful is essential. Many people have this demand. In this study, the focused ultrasound high-energy beam is precisely focused on the deeper skin tissues to heat the target tissues to achieve skin firmness, reduce wrinkles, and promote the effect of collagen proliferation. The experimental results showed that the volume of the focused ultrasound beam on the biomimetic material sensor is proportional to the treatment, and the deep learning neural network can also predict the volume to achieve the purpose of predicting the treatment effect. Focused ultrasound ; Therapeutic ultrasound ; High-intensity focused ultrasound ; Ultrasound ; Therapeutic effect ; Volume (thermodynamics) Textile materials and evaluations
Authors: Qian Li (114642) | Siqi Zhan (18445534) | Zhanjie Liu (41542) | Caibo Dong (13161181) | Hengheng Zhao (11552467) | Tongkui Yue (10041751) | Qingsong Zhao (5934005) | Liqun Zhang (115900) | Ying Li (38224) | Jun Liu (42548)
Source: Figshare (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Developing HybridMachine Learning Frameworks forPolymer Property Prediction Based on Composition and Sequence Features Artificial intelligence (AI) plays a significant role in advancing polymer science and engineering. Considering the critical role of the glass transition temperature (<i>T</i><sub>g</sub>) in determining the physical properties of polymers, this study systematically investigates the influence of their composition and sequence structure on <i>T</i><sub>g</sub> using machine learning (ML) models. To clarify the complex relationship between polymer composition and <i>T</i><sub>g</sub>, the k-nearest neighbor mega-trend diffusion (kNNMTD) method was employed for data augmentation, and various ML models were constructed for <i>T</i><sub>g</sub> prediction. Among them, the Random Forest model demonstrated the best performance for the generated data, achieving an <i>R</i><sup>2</sup> of 0.85 and an RMSE of 0.38. To explore the effect of polymer sequence structure on <i>T</i><sub>g</sub>, we further introduced natural language processing (NLP) techniques to represent polymer sequences. The data was augmented using the Wasserstein generative adversarial network (GAN) with gradient penalty (WGAN-GP) model, and <i>T</i><sub>g</sub> predictions were made using a convolutional neural network-long short-term memory (CNN-LSTM) model. This integrated framework achieved excellent predictive performance, with an <i>R</i><sup>2</sup> of 0.95 and an RMSE of 0.23, and demonstrated strong generalization across different data sets. In summary, this study introduces an innovative application of kNNMTD for augmenting polymer composition data combined with NLP techniques for representing polymer sequences. The proposed ML framework offers a valuable contribution to the advancement of polymer material design and optimization. Sequence (biology) ; Artificial neural network ; Generalization ; Composition (language) ; Property (philosophy) ; Convolutional neural network ; Random forest ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Huiyu Liu (1357284) | Muyao Yu (22531555) | Zikai Xie (22531558) | Xiangyu Zhang (630437) | Yiru Sun (22531561) | Yuyang Ling (19173361) | Zezhao Xu (19011337) | Zidi Wang (13797934) | Na Yu (151735) | Yue-Biao Zhang (1649662) | Ping Huai (681969) | Qisheng Wang (1486984) | Xuming He (256403) | Linjiang Chen (1370121) | Wei-Hong Zhu (1450543) | Shan Jiang (121292)
Source: Figshare (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Data-Driven Discoveryof Polar Organic Cocrystals:Integration of Machine Learning and Automated Screening Polar organic cocrystals hold significant promise for various advanced technological applications. However, their relatively low occurrence emphasizes the difficulties in achieving the desired polar packing arrangements, making their discovery complex and challenging. Here, we introduce a data-driven method that combines machine learning (ML) with high-throughput (HT) automation to speed up the discovery of polar organic cocrystals. Using ML techniques, we identified key factors that influence polar cocrystal formation, allowing for targeted selection of molecular candidates. We examined 13 cocrystal combinations with chloranilic acid (CA), screening 20 solvent systems for each, which enabled a highly efficient search across a broad chemical space. HT automation further enhanced the synthesis and characterization by enabling rapid screening and precise structural validation, while thoroughly exploring the chemical landscape. Experimental results confirmed 13 pairs of CA cocrystals, with 6 crystallizing in polar space groups, resulting in a polar discovery rate of 46%-nearly three times higher than the average in the Cambridge Structural Database (CSD) (∼13.2%). This integrated approach offers a new strategy in polar organic cocrystal research. The findings demonstrate the potential of this method to advance functional molecular materials and pave the way for next-generation applications using polar organic cocrystals. Cocrystal ; Polar ; Automation ; Drug discovery ; Chemical space ; Key (lock) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Wenhao Sun
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Phase Diagrams on-Demand Phase diagrams are essential tools to guide the synthesis of functional materials, but they are almost never available in the frontier chemical spaces where we perform materials discovery and design. Experimental phase diagram assessment can require years of tedious measurement, whereas computing the high-temperature regions of phase diagrams computationally can be extremely costly. Here, we present a strategy to combine the best of both worlds—using computation to assess the energies of solids, and experiments to assess their phase transformations at high-temperature. By using an appropriate thermodynamic referencing scheme to connect their energies, we can rapidly assess phase diagrams and non-equilibrium Gibbs free-energies in complex, multi-component phase spaces. We conclude with a vision for a unified computational and robotic experimental platform that consolidates materials synthesis and processing, robotic calorimetry, ab initio thermodynamics, CALPHAD thermodynamic modeling, and artificial intelligence/machine learning (AI/ML), to generatephase diagrams on demand. CALPHAD ; Phase diagram ; Phase (matter) ; Computation ; Story-driven modeling ; Diagram Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials