jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isaiah
O. Betinol (11949779) | Yutao Kuang (11949782) | Junshan Lai (1589566) | Christopher Yousofi (21301532) | Jolene P. Reid (6235811)
Source: Figshare (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Machine Learning Enables a Top-Down Approach to MechanisticElucidation in Asymmetric Catalysis General reaction behavior is rarely reported in asymmetric catalysis, not simply because it is difficult to achieve, but also due to the methods used for its identification and study. Traditional approaches involve compartmentalization, where the impact of individual components is initially analyzed, followed by assimilation using simple response and structure matching techniques. However, extending this method to accommodate complex conditions and diverse reactions proves challenging. Here, we present a data-driven method that relies on clusterwise linear regression to derive and predictively apply general mechanistic models of enantioinduction, with minimal human intervention. When applied to the palladium-catalyzed decarboxylative asymmetric allylic alkylation (DAAA) reaction, unexpected interactions governing enantioselectivity are revealed, supported by high-level computations and additional experiments. Our results demonstrate this workflow as a powerful tool for automating mechanistic elucidation and effectively identifying general reaction performance. Workflow ; Enantioselective synthesis ; Computation ; Identification (biology) ; Matching (statistics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis
Authors: Ikuma Kohata | Ryo Yoshikawa | Kaoru Hisama | Christophe Bichara | Keigo Otsuka | Shigeo Maruyama
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Edge Dynamics in Iron-Cluster Catalyzed Growth of Single-Walled Carbon Nanotubes Revealed by Molecular Dynamics Simulations Based on a Neural Network Potential Carbon nanotube ; Molecular dynamics ; Dynamics (music) ; Cluster (spacecraft) ; Catalysis ; Nanotechnology ; Enhanced Data Rates for GSM Evolution ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Computer science ; Physics ; Organic chemistry ; Telecommunications Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Bin Liu | Zhiguo Tian | А. А. Баринов | Moran Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhanced heat conduction in diamond/copper composites via interconnected structures: Machine learning molecular dynamics simulation Thermal conduction ; Copper ; Molecular dynamics ; Materials science ; Composite material ; Diamond ; Dynamics (music) ; Chemistry ; Metallurgy ; Physics ; Computational chemistry Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Cody Bezik | Jeffrey G. Ethier | Aniruddh Vashisth | Vikas Varshney
Source: Macromolecules (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

A Hybrid Simulation-Machine Learning Framework for Predicting Thermal Degradation in High-Performance Polyimides Accurately predicting the onset of thermal degradation (Td) in polyimides is critical to materials development in high-temperature aerospace and electronics applications. Direct measurement of Td via thermogravimetric analysis requires costly synthesis, necessitating alternate routes to rapidly screen monomer chemistries for promising new materials. In this work, we develop an integrated framework combining reactive molecular dynamics with machine learning models to rapidly and accurately estimate the 5% mass loss Td of polyimide chemistries. Beginning from a curated data set of polyimide repeat units with experimentally validated Td values, we perform reactive molecular dynamics with ReaxFF to simulate thermal degradation as the polymers are heated from 300 to 4000 K. We use the simulations to create features related to the onset of thermal degradation, which we combine with chemical fingerprint information obtained from RDKit to serve as the input to XGBoost models which are trained to predict Td. We find the performance of such models is highly dependent on the choice of features, and the means by which such features are combined. While purely chemistry-based models perform well, we find we can significantly enhance the accuracy of Td prediction by training meta-models, in which the predictions of separate XGBoost models trained on chemistry information and simulation information, respectively, are used to featurize new machine learning models to make final predictions. Our work provides a foundation upon which new chemistries can be evaluated for thermal degradation performance, leading to the generation and design of new polyimide chemistries with very high Td. Polyimide ; Degradation (telecommunications) ; Thermal ; Molecular dynamics ; ReaxFF ; Polymer ; Monomer ; Set (abstract data type) Machine Learning in Materials Science ; Polymer crystallization and properties ; Synthesis and properties of polymers
Authors: Shengkai Yu | Peng Bi | Jingsong Liu | Enzhu Li | Yong Yi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Investigation on Cation Diffusion Mechanisms in Ni0.2Cu0.3Zn0.5Fe2O4 Ferrite and Its Homogeneous Interfaces Homogeneous ; Ferrite (magnet) ; Diffusion ; Molecular dynamics ; Lattice (music) ; Ion ; Ceramic Magnetic Properties and Synthesis of Ferrites ; Electrical and Thermal Properties of Materials ; Metallic Glasses and Amorphous Alloys
Authors: Vincent CH Chung
Source: Spiral (Imperial College London) (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Materials discovery using machine learning based on materials database and informatics The discovery of new materials has been pivotal to technological advancements but is currently bottlenecked by the experimental validation of proposed hypothetical compounds. There are several obstacles to realizing the full potential of data-driven approaches, namely 1) identification of synthesizable materials 2) availability of high-quality data, and 3) omitted practical consideration. To address these challenges, a dataset on solid-state synthesis of ternary metal oxides was manually curated. This data offers information on whether materials have been solid-state synthesized in the literature, along with some important synthesis conditions. We then applied this dataset in various ways that can help accelerate materials discovery. We first showed that a small set of high-quality data can be used to identify outliers in text-mined datasets. We then trained a transductive positive-unlabeled learning model to predict the solid-state synthesizability of the ternary metal oxides. Compared to previous studies that predict the general synthesizability, the model offers information on false positive rates. In chapter 4, we trained models using different datasets and features to predict solid-state heating temperature and atmosphere. Comparison of models performance shows that the best overall models were trained using a combination of the two datasets. We also demonstrated that precursor features generated using high-quality data can eliminate the need for precursor information from other sources that are scattered, non-uniform, or incomplete. In chapter 5, we conducted a screening study that incorporated precursor hazards as a criteria. Precursor hazards are often neglected during materials screening but are important for synthesis planning, lab workers’ safety, and environmental sustainability. Overall, the work in this thesis provides a set of high-quality datasets to supplement text-mined datasets for solid-state synthesis by illustrating approaches to select hypothetical compositions, offering advice in choosing solid-state synthesis conditions, and raising awareness of practicalities when applying data-driven approaches. Materials informatics ; Identification (biology) ; Set (abstract data type) ; Informatics ; Outlier ; Ternary operation ; Synthetic data ; Deep learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Krupa Prajapati | Vikas A. Desai | Mustafa Alhaji Isa | Riki Tailor | Bhadresh Sudani | Jignesh H. Pandya
Source: Journal of Computer-Aided Molecular Design (article, 2025-11-04)
Field: Chemistry — Organic Chemistry

Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent Antimicrobial ; Quantitative structure–activity relationship ; Lipinski's rule of five ; Pharmacophore ; In silico ; Molecular dynamics ; Docking (animal) ; Glycosidic bond ; Affinities Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Rafiuzzaman Pritom | Md Mahbubul Islam
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Generative AI-Enabled Electrolyte Discovery for Next-Generation Alkali Metal Batteries The advancement of AI technology has led to groundbreaking discoveries in materials science, revolutionizing everything from property prediction to the design of novel materials. By utilizing the ability to analyze vast datasets, identifying intricate interactions between atoms, and implementing graph-based machine learning algorithms, significant improvement has been achieved in the field of materials discovery. In this study, we present a systematic approach to accelerate the discovery of next-generation electrolytes for Li/Na batteries by integrating advanced machine learning with quantum mechanical calculations. We developed a Generative Adversarial Network (GAN) framework incorporating Graph Neural Network (GNN)-based generator and discriminator models to generate novel electrolyte candidates. The GAN was trained on an approximately one million molecules subset obtained from the GDB-11 database, enabling the generation of 30,000 unique and chemically valid molecules. Additionally, we trained a GNN-based Message Passing Neural Network (MPNN) property prediction model using the QM9 dataset, which contains ~133,000 molecules with key quantum mechanical properties, including G4MP2 formation enthalpy ( E f ), GW HOMO energy ( ∈ GW HOMO ), and GW LUMO energy ( ∈ GW LUMO ). The trained MPNN achieved high predictive accuracy, with 96.8%, 96.8% and 96.7% accuracy on the training, validation, and testing sets, respectively. Using this model, we predicted the properties of the newly generated molecules and screened 30 promising candidates based on the criteria of favorable formation energy and wide electrochemical stability window. Interestingly, all selected molecules featured a common nitrile (-C≡N) functional group. The selection process was further refined through first-principles Density Functional Theory (DFT) calculations to evaluate key electronic and thermodynamic properties, including HOMO, LUMO, oxidation potential, reduction potential, and formation enthalpy. These simulations enabled the identification of high-performing electrolyte molecules with exceptionally wide electrochemical stability windows, which makes them highly promising candidates for Li/Na batteries. Our results highlight the potential of these AI-driven electrolyte candidates as viable alternatives to conventional organic electrolytes, paving the way for the development of high-performance, stable, and efficient electrolyte materials for next-generation energy storage systems. Figure 1 Discriminator ; Artificial neural network ; Generative grammar ; Process (computing) ; Generator (circuit theory) ; Field (mathematics) ; Chemical space ; Quantum Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: A. Parrales | Sung-Hyuk Cha | J.M. Angeles | Daysi Elusaí Millán-Ocampo | R. López-Sesenes | J.A. Hernández
Source: International Journal of Electrochemical Science (article, 2025-11-27)
Field: Materials Science — Metals and Alloys

Artificial neural network modeling of electrochemical noise for predicting corrosion resistance of AA2024-AZ31 alloy in chloride solutions with cerium(III) nitrate hexahydrate In this study, artificial neural networks were proposed and applied to estimate the noise resistance ( R n ) of the AA2024–AZ31 alloy exposed to 0.05 M NaCl with 0, 2, 4, 6, 8, and 10 mM of cerium(III) nitrate hexahydrate. Three pseudo-random number generators (MCG16807, MLF6331, and Mersenne Twister), two preprocessing strategies (normalization and standardization), and several transfer functions were evaluated to determine their impact on predictive accuracy. The Mersenne Twister method, combined with standardization, produced the most consistent results. The best performance was achieved with the dSiLU transfer function and seven neurons with a determination correlation ( R 2 ) of 0.998 and an RMSE of 33.363. Meanwhile, the Sinc-Squared function yielded results comparable to the best model. • Electrochemical noise modeling by ANN with different sampling, normalization, and transfer function strategies. • Three pseudo-random number generators (MCG16807, MLF6331, Twister) were evaluated for data sampling. • Normalization versus standardization compared for predictive performance on electrochemical resistance. • dSiLU and Sinc Squared transfer functions demonstrated suitability for modeling electrochemical noise with ANN. Electrochemical noise ; Transfer function ; Artificial neural network ; Electrochemistry ; Mean squared error ; Normalization (sociology) ; Noise (video) ; Alloy Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications
Authors: Zongguo Wang | Ziyi Chen | Yang Yuan | Yangang Wang
Source: ArXiv.org (preprint, 2025-01-27)
Field: Materials Science — Materials Chemistry

CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of AI AI-based CrySPAI Crystal Prediction Software tool and its unique features. Crystal structure prediction ; Computer science ; Artificial intelligence ; Software ; Machine learning ; Crystal structure ; Crystallography ; Chemistry ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Mamookho Elizabeth Makhatha | Patrick Ehi Imoisili | Sebetlela Letlhabula | Tien‐Chien Jen
Source: Journal of Natural Fibers (article, 2025-07-16)
Field: Materials Science — Polymers and Plastics

Comparative Modeling and Prediction of the Mechanical Strength of Chemically Modified Bamboo Fibers Using Artificial Neural Networks and Response Surface Methodology Artificial neural networks (ANN) in artificial intelligence (AI) have been utilized to solve an array of engineering challenges. At the same time, response surface methodology (RSM) is a modeling strategy to maximize the response or responses when two or more quantitative components are involved. This inquest presents a comparative investigation between ANN and RSM modeling and prediction of the tensile strength (TS) of chemically modified natural fibers (NF). In this investigation, ethanol (C₂H₆O) was utilized to modify bamboo fibers (BmF) at varied durations, followed by potassium permanganate (KMnO4) in acetone solution at different concentrations and time ranges. ANN and RSM via Box-Behnken design (BBD) were employed to predict the impact of these chemical treatment parameters on the TS of the modified BmF. The treatment variables significantly affect the TS of BmF as indicated by the analysis of variance (ANOVA), which reveals the statistical significance of the model at a level of p < .0001. The calculated coefficients of R2 (correlation coefficient), RMSE (root mean square error), and MAE (mean absolute error) for each output parameter were used to compare the prediction abilities of the ANN and RSM. The outcomes show that a well-trained ANN is superior to the RSM-BBD model. The current investigation has exhibited the efficacy of the ANN and RSM-BBD modeling methodology in promptly attaining suitable mechanical property values, curtailing manufacturing expenses, and preserving resources. Bamboo ; Artificial neural network ; Response surface methodology ; Materials science ; Composite material ; Structural engineering ; Biological system ; Engineering ; Computer science ; Artificial intelligence ; Machine learning Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Advanced Cellulose Research Studies
Authors: Lifeng Zheng (2387356) | Zhongye Wang (21547121) | Jiawei Chen (186337) | Shaoyi Hou (21547124) | Shiyu Dong (5806403) | Lulu Fu (184614) | Jianan Fan (20150401) | Wei Li (7081) | Po Sun (4394470) | Jing Ma (24574)
Source: Figshare (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Prediction ofVertical Excitation and Emission Energiesfor Optoelectronic Molecules: An Automated Workflow Combining High-ThroughputComputations and Machine Learning Organic optoelectronic materials with localized or delocalized excitation features are widely used in various optoelectronic devices. A data-driven automated workflow was implemented for rapidly predicting excited state properties and screening of candidate molecules. A database was built with a collection of 1223 samples in a wide chemical space, including acceptor/donor complexes (such as PM6-Y6) and their derived subsystems. The ground state Dr index was demonstrated to be an important descriptor for evaluating the charge transfer tendency and predicting the Dr* index at the excited state through machine learning (ML) models. The ML-derived relationship among the Dr descriptor, excitation energy, and emission energy was applied to reproduce experimental results without the need for time-consuming calculations of excited states. The π-conjugated units containing three to six fused rings and N/S heteroatoms were found to have a stronger charge transfer tendency than the other functional units, highlighting the great potential of these chromophores in optoelectronic materials. Excited state ; Workflow ; Delocalized electron ; Excitation ; Computation ; Charge (physics) ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Glenn
Keith Kim Clothier (22262452) | Daniel Taton (1553824) | Simon Harrisson (1356411)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Biomaterials

ExploitingMachine Learning and Automated Synthesisin Continuous Flow for Process Optimization of the OrganocatalyzedRing-Opening Polymerization of l‑Lactide The ring-opening polymerization (ROP) of lactide is a well-established route for the synthesis of polylactide (PLA), a degradable and biobased polymer with applications in biomedical materials, packaging, and additive manufacturing. However, optimizing reaction conditions for efficient PLA synthesis remains challenging, with its production currently dwarfed by traditional petrochemical derived commodity polymers. In this work, we employ a continuous flow reactor to systematically explore a reaction space defined by catalyst concentration, residence time, and initiator concentration for the organocatalyzed ROP of l-lactide performed at room temperature, using 1,8-diaza­bicyclo​[5.4.0]​undec-7-ene (DBU) as catalyst, benzyl alcohol as initiator and dichloromethane as solvent. Through high-throughput experimentation coupled with online characterization, a robust data set was generated and processed with a Kernel-Based Regularized Least Squares (KRLS) model to capture system kinetics and the dependencies between initial conditions and polymer characteristics. Multiobjective Pareto optimization was subsequently used to identify conditions that maximize polymer production rate, and these conditions were experimentally validated to confirm the model accurately predicts optimal reaction conditions. Pareto-optimized parameters yield a high conversion and well-controlled PLA with low dispersities. This study highlights the advantages of continuous flow polymerization for precise control over reaction kinetics and demonstrates the potential of machine-learning-assisted optimization for efficient and scalable PLA-based materials’ exploration. Polymerization ; Flow chemistry ; Petrochemical ; Continuous flow ; Yield (engineering) ; Step-growth polymerization ; Lactide ; Process (computing) biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Toby Boyne | Juan S. Campos | Becky D. Langdon | Jixiang Qing | Yilin Xie | Shiqiang Zhang | Calvin Tsay | Ruth Misener | Daniel W. Davies | Kim E. Jelfs | Sarah Boyall | Thomas M. Dixon | Linden Schrecker | Jose Pablo Folch
Source: ArXiv.org (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to the machine learning community as they tend to require cleaning, thorough understanding of the chemistry, or are simply not available. In this paper, we introduce a novel dataset for yield prediction, providing the first-ever transient flow dataset for machine learning benchmarking, covering over 1200 process conditions. While previous datasets focus on discrete parameters, our experimental set-up allow us to sample a large number of continuous process conditions, generating new challenges for machine learning models. We focus on solvent selection, a task that is particularly difficult to model theoretically and therefore ripe for machine learning applications. We showcase benchmarking for regression algorithms, transfer-learning approaches, feature engineering, and active learning, with important applications towards solvent replacement and sustainable manufacturing. Benchmarking ; Feature selection ; Active learning (machine learning) ; Process (computing) ; Focus (optics) ; Multi-task learning ; Feature (linguistics) ; Task (project management) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning and Data Classification