jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mushtaq Ali (3598514) | Sylvia Vanderheiden (2870681) | Christoph W. Grathwol (22026942) | Konrad Krämer (22026945) | Pascal Friederich (1753135) | Nicole Jung (1843408) | Stefan Bräse (1457791)
Source: Figshare (article, 2025-08-10)
Field: Materials Science — Materials Chemistry

Advancing AqueousSolubility Prediction: A MachineLearning Approach for Organic Compounds Using a Curated Data Set Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications, from drug development to materials sciences. In this work, we present a model for the prediction of aqueous solubility that leverages a curated data set merged from four distinct sources. This data set encompasses a diverse range of organic compounds, providing a robust foundation for our investigation of solubility prediction. Our approach involves employing a variety of machine learning and deep learning models that combine an extensive array of chemical descriptors, fingerprints, and functional groups. This methodology is designed to address the complexities of solubility prediction and is tailored to achieve high accuracy and generalization. We tested the finalized model on a diverse data set of 1282 unique organic compounds from the Huuskonen data set. The results of our analysis demonstrate the success of our model, which, given an <i>R</i><sup>2</sup> value of 0.92 and an MAE value of 0.40, outperforms existing prediction methods for aqueous solubility on one of the most diverse data sets in the field. Solubility ; Set (abstract data type) ; Data set ; Variety (cybernetics) ; Aqueous solution ; Deep learning ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Susumu Minami | Yukie Maruyama | Yoshimasa ABE | T Nakayama | Takahiro SHIMADA
Source: Institutional Repositories DataBase (IRDB) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

能動的機械学習とハイスループット第一原理計算によるデータ駆動型非線形強誘電物性のひずみ最適化 Strain engineering is a crucial approach in the engineering field to optimize various physical properties of materials by applying mechanical strain loading. However, it is extremely challenging to find out the best conditions of strain with unprecedented physical properties in the vast strain space consisting of six components. Here, we developed a technical framework that enables efficient exploration of physical properties in the vast strain space based on machine learning (i.e., artificial neural networks), active learning, and high-throughput first-principles calculation. We demonstrated the active learning technique to successfully and efficiently construct an accurate machine learning model for ferroelectric PbTiO₃ with minimal first-principles datasets (only 3.7% of the vast strain-space). Our machine learning model can accurately predict the nonlinear mechanical deformation and electromechanical response in the three components of normal strain loading. We also carried out strain optimization of piezoelectric response using the machine learning model and found that a large piezoelectric response is five times larger than without strain loading. We showed that the physical property explorer framework constructed in this study makes it possible to optimize strain for various material properties in a vast strain space by calculating only a small number of data points. These results suggest paving the way for constructing nonlinear piezoelectric constitutive equations for novel piezoelectric devices via strain engineering. Piezoelectricity ; Nonlinear system ; Field (mathematics) ; Property (philosophy) ; Strain (injury) ; Deformation (meteorology) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Model Reduction and Neural Networks
Authors: Subbiah Thamotharan | M. Judith Percino | Diego M. Gil
Source: Frontiers in Chemistry (article, 2025-12-17)
Field: Chemistry — Physical and Theoretical Chemistry

Editorial: Noncovalent interactions in N/O heterocycles Noncovalent interactions are often described as the "soft glue" of chemistry, yet their collective influence is anything but weak (Desiraju et al., 2011;Schneider, 2022). In nitrogen-and oxygen-containing heterocycles, these forces range from classical hydrogen bonds to σ-hole contacts (Frontera and Bauzá, 2021;Siddiqui et al., 2024) Second, N/O heterocycles remain exceptionally adaptable scaffolds, capable of engaging in charge-assisted hydrogen bonding, chalcogen bonding, π-stacking, and a range of electrostatic interactions. Third, coupling high-quality crystallographic data with modern theoretical methods is now indispensable for quantifying energetics, directionality, and interaction cooperativity. Increasingly, integrative methodologies that combine crystallography, computational chemistry, and structural database mining are driving new advances in supramolecular chemistry, drug discovery, and materials science (Spackman and Jayatilaka, 2009;Groom et al., 2016;Jelfs, 2022).C-H•••N, C-H•••O, C-H•••Cl, C-H•••S, π-πImportantly, the works in this Research Topic highlight how the study of weak interactions is evolving from simple qualitative descriptions toward predictive design principles. As computational power, theoretical techniques, and machine learning tools continue to grow (Tretiakov et al., 2025), we anticipate accelerated development of rational strategies for constructing functional molecular solids, biomimetic assemblies, and targeted smallmolecule therapeutics. Non-covalent interactions ; Supramolecular chemistry ; Hydrogen bond ; Chalcogen ; Molecular recognition ; Range (aeronautics) ; Rational design ; Coupling (piping) Crystallography and molecular interactions ; Supramolecular Chemistry and Complexes ; Machine Learning in Materials Science
Authors: Hongyi Zheng
Source:  (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

GEP-Enhanced GNN Predictions for Bandgap in Perovskite Materials To address the critical challenge of bandgap prediction in perovskite materials, this study proposes a novel prediction framework that integrates multi-source feature se lection with graph neural networks (GNNs). Based on a self-constructed database of 698 ABX3-type perovskite crystals, the compositional influence on bandgap values is systematically analyzed, identifying the substitution of halogen at the X site as the dominant regulatory factor. To improve model efficiency and interpretability, a three-stage feature selection strategy is used: Pearson correlation analysis, gradient-boosting decision tree (GBDT) importance scoring, and sequential validation to extract six physically meaningful descriptors from the high dimensional feature space. Building on these insights, we develop a geometry and property-enhanced graph neural network (GEP GNN) model, where the selected features are embedded into the node attributes of the crystal graph, enabling deep integration of structural and physicochemical information. Experimental results demonstrate that the proposed method reduces the mean absolute error (MAE) in bandgap prediction by 40.9% compared to conventional GNNs, significantly outperforming baseline models. Band gap ; Perovskite (structure) ; Feature selection ; Feature (linguistics) ; Artificial neural network ; Graph ; Decision tree ; Crystal (programming language) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Youhei Yamaji
Source: Journal of the Physical Society of Japan (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Self-Energy Spectroscopy and Artificial Neural Network The analysis of spectroscopy data has played an important role in untangling the complex dynamics of many-body electrons in quantum materials and making their emergent properties understandable. Spectroscopy measurements provide us with the responses of the many-body electrons in materials when energy and momentum are injected. These responses have been analyzed using a single-particle picture augmented by self-energy, which quantifies the deviation from simple free fermion excitation. While the self-energy is not directly observed in spectroscopy, it has been extracted from the obtained spectra by solving inverse problems. Especially, for superconductors, the analysis of self-energy is a key to understanding the origin of the superconductivity. The recent rise of machine learning has served to update the self-energy analysis of spectroscopy data and opened a new avenue for understanding the entangled nature of manybody electrons. In this article, self-energy analysis using the flexibility of neural networks is reviewed and positioned in the research trajectory from Bardeen–Cooper–Schrieffer superconductors to copper-oxide high-temperature superconductors. Artificial neural network ; Spectroscopy ; Computer science ; Energy (signal processing) ; Materials science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Quantum and electron transport phenomena ; Theoretical and Computational Physics
Authors: Tetsuo Asakura | Asuna Hagiya | Takashi Kataoka | Keiji Komatsu | Keisuke Sato
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Design and Characterization of Laco1- X Y X O3 by Machine Learning Characterization (materials science) ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Pigment Synthesis and Properties
Authors: Kim, Chiho | Xiong, Wei | Mahmood, Akhlak | Ramprasad, Rampi | Tran, Huan
Source: arXiv (Cornell University) (preprint, 2025-10-31)
Field: Materials Science — Polymers and Plastics

AI-Driven Design of poly(ethylene terephthalate)-replacement copolymers Poly(ethylene terephthalate) (PET), a widely used thermoplastic in packaging, textiles, and engineering applications, is valued for its strength, clarity, and chemical resistance. Increasing environmental impact concerns and regulatory pressures drive the search for alternatives with comparable or superior performance. We present an AI-driven polymer design pipeline employing virtual forward synthesis (VFS) to generate PET-replacement copolymers. Inspired by the esterification route of PET synthesis, we systematically combined a down-selected set of Toxic Substances Control Act (TSCA)-listed monomers to create 12,100 PET-like polymers. Machine learning models predicted glass transition temperature (Tg), band gap, and tendency to crystallize, for all designs. Multi-objective screening identified 1,108 candidates predicted to match or exceed PET in $T_{\rm g}$ and band gap, including the ``rediscovery'' of other known commercial PET-alternate polymers (e.g., PETG, Tritan, Ecozen) that provide retrospective validation of our design pipeline, demonstrating a capability to rapidly design experimentally feasible polymers at a scale. Furthermore, selected, entirely new (previously unknown) candidates designed here have been synthesized and characterized, providing a definitive validation of the design framework. Pipeline (software) ; Polymer ; Copolymer ; Monomer ; Thermoplastic polymer ; Set (abstract data type) Polymer crystallization and properties ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Thantip Roongcharoen | Giorgio Conter | Giacomo Melani | Luca Sementa | Alessandro Fortunelli
Source: Journal of Chemical Theory and Computation (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Extrapolation Techniques in Database Construction for Machine-Learning Potentials: Achieving Subchemical Accuracy in Sampling Conformal Funnels in Catalytic Processes We present a computational workflow, the conformal sampling of catalytic processes enhanced with extrapolation techniques (CSCP-X), for constructing machine-learning interatomic potentials (MLIPs) to accelerate the exploration of first-principles potential energy surfaces for complex catalytic reactions. The MLIPs developed within the enhanced CSCP framework achieve subchemical accuracy error (∼0.03 eV) with respect to the energetics of the generating first-principles DFT approach, that is the targeted accuracy for predictive catalyst screening, while maintaining the same computational efficiency and throughput of the original CSCP method, still requiring only up to two iterative active learning steps and with a very modest increase in the size of the dataset. The CSCP-X approach is demonstrated on the methanol (CH<sub>3</sub>OH) decomposition reaction pathway on (111) and (100) metal facets of eight metal catalysts (TM = Fe, Co, Ni, Cu, Pd, Ag, Au, and Pt). By combining mechanistic invariance with mathematical series extrapolation techniques, we achieve the ability to fully exploit the idea of "conformal funnels" in reaction processes. The generated MLIPs not only predict very accurately reaction energy barriers via nudged elastic band (NEB) transition state searches but also prove to be able to uncover alternative reaction paths and changes in the reaction mechanism with reduced energy barriers, reaching mechanistic transferability <i>en par</i> with the best physics-based models. CSCP-X offers itself as an operative, robust, and highly efficient pathway for accelerating the discovery of novel catalytic materials through high-throughput catalyst screening. Extrapolation ; Computer science ; Catalysis ; Throughput ; Chemistry ; Computational chemistry ; Statistical physics ; Physics ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; X-ray Diffraction in Crystallography
Authors: Yoshiro Ikeda | Yuki Akura | R. Yamasaki | Yuki Matsunaga | Lijun Liu | Masaki Shimofuri | Amit Banerjee | Toshiyuki Tsuchiya | Jun Hirotani
Source: Applied Physics Letters (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Utilizing machine learning in the three-omega method to predict thermophysical properties with low variation The three-omega method offers a precise and simple technique for measuring the thermal conductivities of a wide range of materials. Utilizing nonlinear data enhances the determination of multiple thermophysical properties at the micro- and nanoscales. However, challenges such as dependence on initial guesses and convergence to local minima render nonlinear fitting processes unstable. This study introduces a machine learning-based approach that addresses these limitations by pre-learning a globally optimal solution. We developed a methodology that processes three-omega signals using machine learning to determine various thermophysical properties. The approach involved compiling results from the analytical three-omega model and augmenting them with Gaussian noise to create a robust machine learning model. Subsequently, a neural network was trained on this dataset. Testing confirmed the model's accurate predictions of volumetric heat capacity and thermal conductivity. Comparative analyses between machine learning-based and conventional fitting methods on the sample data extracted from the test set demonstrated the superior stability of the machine learning model in predicting thermophysical properties. Furthermore, experimental validation was carried out by applying our machine learning model to real-world data from glass and liquid samples. The correspondence between the predicted values and the theoretical curve of the third harmonic voltage affirmed the model's applicability and accuracy in practical scenarios. These findings validate the potential of our machine learning model for robustly predicting thermophysical properties based on actual measured data. Omega ; Variation (astronomy) ; Materials science ; Computer science ; Artificial intelligence ; Thermodynamics ; Machine learning ; Physics Thermal properties of materials ; Radiative Heat Transfer Studies ; Heat Transfer and Optimization
Authors: Kim, Chiho | Xiong, Wei | Mahmood, Akhlak | Ramprasad, Rampi | Tran, Huan
Source: ArXiv.org (article, 2025-10-31)
Field: Materials Science — Polymers and Plastics

AI-Driven Design of poly(ethylene terephthalate)-replacement copolymers Poly(ethylene terephthalate) (PET), a widely used thermoplastic in packaging, textiles, and engineering applications, is valued for its strength, clarity, and chemical resistance. Increasing environmental impact concerns and regulatory pressures drive the search for alternatives with comparable or superior performance. We present an AI-driven polymer design pipeline employing virtual forward synthesis (VFS) to generate PET-replacement copolymers. Inspired by the esterification route of PET synthesis, we systematically combined a down-selected set of Toxic Substances Control Act (TSCA)-listed monomers to create 12,100 PET-like polymers. Machine learning models predicted glass transition temperature (Tg), band gap, and tendency to crystallize, for all designs. Multi-objective screening identified 1,108 candidates predicted to match or exceed PET in $T_{\rm g}$ and band gap, including the ``rediscovery'' of other known commercial PET-alternate polymers (e.g., PETG, Tritan, Ecozen) that provide retrospective validation of our design pipeline, demonstrating a capability to rapidly design experimentally feasible polymers at a scale. Furthermore, selected, entirely new (previously unknown) candidates designed here have been synthesized and characterized, providing a definitive validation of the design framework. Pipeline (software) ; Polymer ; Copolymer ; Monomer ; Thermoplastic polymer ; Set (abstract data type) Polymer crystallization and properties ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Sherif Abdulkader Tawfik (3820180)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Computational MaterialScience Has a Data Problem We present an overdue questioning of the computational material science data: Is it suitable for training machine learning models? By examining the energy above the convex hull (<i>E</i><sub><i>h</i></sub>), the electronic bandgap, and the formation energy data in the Materials Project dataset, we find that <i>E</i><sub><i>h</i></sub> is an unsteady quantity, as are DFT-computed voltages, because the present materials in the database do not constitute sufficient representation of the chemical spaces that are necessary to account for crystal decomposition. We also show that there are discrepancies in the reported electronic bandgap values in the Materials Project database and that the formation energy data can potentially shift due to changing the optimization parameters that reduce the energy of the structure below the value deposited in the database. Representation (politics) ; Convex hull ; Energy (signal processing) ; Value (mathematics) ; External Data Representation ; Convex optimization ; Crystal (programming language) ; Data modeling Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Kimiya Khandestan | Azar Sabukbar | Bahareh Rahimian Zarif | Nahid Haghnazari | Naser Harzandi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of Carbon Quantum Dots Using Mold and Fluorescence-Based Detection of Metronidazole Antibiotic Employing Experimental Design and Machine Learning Approaches Quantum dot ; Fluorescence ; Carbon quantum dots ; Mold ; Metronidazole ; Nanotechnology ; Materials science ; Antibiotics ; Computer science ; Optoelectronics ; Optics ; Physics ; Microbiology ; Composite material ; Biology Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Thantip Roongcharoen (9519915) | Giorgio Conter (19504148) | Giacomo Melani (13169979) | Luca Sementa (1491847) | Alessandro Fortunelli (1268154)
Source: Figshare (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Extrapolation Techniques in Database Constructionfor Machine-Learning Potentials: Achieving Subchemical Accuracy inSampling Conformal Funnels in Catalytic Processes We present a computational workflow, the conformal sampling of catalytic processes enhanced with extrapolation techniques (CSCP-X), for constructing machine-learning interatomic potentials (MLIPs) to accelerate the exploration of first-principles potential energy surfaces for complex catalytic reactions. The MLIPs developed within the enhanced CSCP framework achieve subchemical accuracy error (∼0.03 eV) with respect to the energetics of the generating first-principles DFT approach, that is the targeted accuracy for predictive catalyst screening, while maintaining the same computational efficiency and throughput of the original CSCP method, still requiring only up to two iterative active learning steps and with a very modest increase in the size of the dataset. The CSCP-X approach is demonstrated on the methanol (CH<sub>3</sub>OH) decomposition reaction pathway on (111) and (100) metal facets of eight metal catalysts (TM = Fe, Co, Ni, Cu, Pd, Ag, Au, and Pt). By combining mechanistic invariance with mathematical series extrapolation techniques, we achieve the ability to fully exploit the idea of “conformal funnels” in reaction processes. The generated MLIPs not only predict very accurately reaction energy barriers via nudged elastic band (NEB) transition state searches but also prove to be able to uncover alternative reaction paths and changes in the reaction mechanism with reduced energy barriers, reaching mechanistic transferability <i>en par</i> with the best physics-based models. CSCP-X offers itself as an operative, robust, and highly efficient pathway for accelerating the discovery of novel catalytic materials through high-throughput catalyst screening. Extrapolation ; Catalysis ; Potential energy ; Throughput ; Sampling (signal processing) ; Transferability ; Conformal map ; Computational complexity theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Xi Yuan | Juntao Zhou | Dan Xie | Nian Wang | Lianghai Hu
Source: TrAC Trends in Analytical Chemistry (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

Molecular imprinting toward living systems: From cells to extracellular vesicles Extracellular vesicles ; Molecularly imprinted polymer ; Imprinting (psychology) ; Molecular imprinting ; Living systems ; Molecular recognition Analytical chemistry methods development ; Nanofabrication and Lithography Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation