jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Silabrata Pahari (11585860) | Chi Ho Lee (7296167) | Denis Johnson (17988609) | David Kumar Yesudoss (20671135) | Parth Shah (621672) | Mark A. Barteau (1814347) | Abdoulaye Djire (6458165) | Joseph Sang-Il Kwon (6894362)
Source: Figshare (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Advancing KineticStudy of Catalytic Reaction: A HybridModeling Approach for Predicting Effective Activation Energies This study addresses limitations of traditional kinetic Monte Carlo (kMC) simulations, particularly their inability to capture latent surface dynamics on electrocatalysts due to complex many-body interactions among adsorbates, reactants, and intermediates. These shortcomings limit their predictive accuracy, often exacerbated by the separate limitations of density functional theory (DFT) in calculating activation energies (<i>E</i><sub>a</sub>) accurately. To overcome these challenges, we introduced a hybrid model that combines advanced machine learning (ML) with first-principles kMC. In this work, our approach defines “effective” activation energies incorporating nuanced physical phenomena that are absent in conventional kinetic models but evident in experiments. This hybrid model leverages these predictive effective activation energies to unveil underlying chemical phenomena occurring on catalyst surfaces. These phenomena include changes in product generation, surface coverage, and the dominant reaction mechanisms over time, which have been validated through experimental outcomes using MXene catalysts. Additionally, the ML component of our model not only provides an empirical fit but also infers underlying parameters that guide the subsequent DFT calculations based on changeable surface coverage over time. Kinetic Monte Carlo ; Kinetic energy ; Limit (mathematics) ; Density functional theory ; Monte Carlo method ; Surface (topology) ; Activation energy Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Sakshi Agarwal (9365510) | Akhlak Mahmood (21182528) | Rampi Ramprasad (1355760)
Source: Figshare (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

PolymerSolubility Prediction Using Large LanguageModels Traditional approaches in polymer informatics often require labor-intensive data curation, time-consuming preprocessing such as fingerprinting, and choosing suitable learning algorithms. Large language models (LLMs) represent a compelling alternative by addressing these limitations with their inherent flexibility, ease of use, and scalability. In this study, we propose a novel approach utilizing fine-tuned LLMs to classify solvents and nonsolvents for polymers, a property critical to polymer synthesis, purification, and diverse applications. Our results show that fine-tuned GPT-3.5 achieves predictive performance comparable to or exceeding traditional machine learning methods, even with limited data sets. The model achieved predictive accuracies of 0.90 and 0.83 for identifying soluble and insoluble solvent–polymer pairs, respectively. Remarkably, these models accurately classify solvents and nonsolvents in entirely unseen scenarios, indicating that they are able to effectively leverage the components embedded in their base models. The operational simplicity and accuracy of LLMs highlight their potential for advancing polymer research. Leverage (statistics) ; Simplicity ; Preprocessor ; Predictive modelling ; Data pre-processing ; Polymer ; Property (philosophy) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Even Nordhagen (21842121) | Henrik Andersen Sveinsson (8585883) | Anders Malthe-Sørenssen (5818910)
Source: Figshare (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Tailoring Frictional Properties of Surfaces UsingDiffusion Models This work introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a denoising diffusion probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning to reduce the iterative nature of surface design processes. Our findings provide a pathway for tailoring surface properties and suggest broader applications in materials science where surface characteristics are critical. Surface (topology) ; Probabilistic logic ; Surface engineering ; Work (physics) ; Diffusion ; Generative grammar ; Surface roughness Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Huichun Xue | Yitong Chen | Lin Zhu | An Du
Source: Electrochimica Acta (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Regulation mechanisms of single-atom doped Ti2CO2 MXene for CO2 reduction: A DFT study Reduction (mathematics) ; Doping ; Atom (system on chip) ; Chemistry ; Materials science ; Optoelectronics ; Computer science ; Mathematics ; Embedded system ; Geometry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Keeratikarn, Keerati | Ortner, Christoph | Frost, Jarvist Moore
Source: arXiv (Cornell University) (preprint, 2025-11-09)
Field: Materials Science — Materials Chemistry

Machine learning intermolecular transfer integrals with compact atomic cluster representations Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations for the molecules of interest. Recent developments have produced effective machine learning force fields, which model the total energy of atomic assemblies. We extend the Atomic Cluster Expansion (ACE) with the correct symmetries for transfer (kinetic-energy) integrals. Combined with a spherical harmonic basis makes, this forms a strong inductive bias and makes for a data efficient model. We introduce coarse-grained and heavy-atom representations, and assess the methodology on representative conjugated semiconductors: ethylene, thiophene, and naphthalene. Intermolecular force ; Degrees of freedom (physics and chemistry) ; Position (finance) ; Charge (physics) ; Basis (linear algebra) ; Transfer (computing) ; Cluster (spacecraft) ; Homogeneous space ; Transfer of learning Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Inorganic Chemistry and Materials
Authors: Jesun Firoz | Franco Pellegrini | Mario Geiger | Darren J. Hsu | Jenna A. Bilbrey | Huey Wen Chou | Maximilian Stadler | Markus Hoehnerbach | Tingyu Wang | Dejun Lin | Emine Küçükbenli | Henry W. Sprueill | Ilyes Batatia | Sotiris S. Xantheas | Michael Ka Ho Lee | Christopher J. Mundy | Gábor Cśanyi | Justin S. Smith | P. Sadayappan | Sutanay Choudhury
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Materials Chemistry

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists and materials scientists. These models facilitate the understanding of matter and the discovery of new molecules and materials. In contrast to GNNs operating on a large homogeneous graphs, GNNs used by CFMs process a large number of geometric graphs of varying sizes, requiring different optimization strategies than those developed for large homogeneous GNNs. This paper presents optimizations for two critical phases of CFM training: data distribution and model training, targeting MACE - a state-of-the-art CFM. We address the challenge of load balancing in data distribution by formulating it as a multi-objective bin packing problem. We propose an iterative algorithm that provides a highly effective, fast, and practical solution, ensuring efficient data distribution. For the training phase, we identify symmetric tensor contraction as the key computational kernel in MACE and optimize this kernel to improve the overall performance. Our combined approach of balanced data distribution and kernel optimization significantly enhances the training process of MACE. Experimental results demonstrate a substantial speedup, reducing per-epoch execution time for training from 12 to 2 minutes on 740 GPUs with a 2.6M sample dataset. Kernel (algebra) ; Leverage (statistics) ; Graph ; Training set ; Kernel method ; Artificial neural network ; Process (computing) ; Iterative and incremental development Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Jian Lin Su | Zi Xuan Cai | Yiqian Mao | Long Chen | Jianan Zhang | Qian Ma | Jian Wei You | Tie Jun Cui
Source:  (article, 2025-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Driven Intelligent Design for Metasurfaces by Using MetaPhyNet Metasurfaces offer a transformative platform for tailoring electromagnetic properties through engineered metaatom arrangements, but the rapidly expanding design space of advanced metasurfaces with different functions has rendered the traditional human-guided methods increasingly ineffective. While intelligent design approaches have emerged, most are data-driven and suffer from data scarcity, poor interpretability, and weak generalization. Here, we introduce a physics-driven intelligent design (PDID) framework, integrating physical priors (coupled mode theory, CMT) into deep neural networks to enhance the interpretability and reduce reliance on large datasets. Compared to conventional methods, PDID achieves two-order-of-magnitude reduction in both computational time and database size, showing its potential as a powerful tool for merging physics with machine learning to drive advancements in computational science, material science, and information technology. Interpretability ; Reduction (mathematics) ; Artificial neural network ; Mode (computer interface) ; Intelligent decision support system ; Deep learning ; Feature (linguistics) ; Intelligent design Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Acoustic Wave Phenomena Research
Authors: Keeratikarn, Keerati | Ortner, Christoph | Frost, Jarvist Moore
Source: ArXiv.org (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Machine learning intermolecular transfer integrals with compact atomic cluster representations Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations for the molecules of interest. Recent developments have produced effective machine learning force fields, which model the total energy of atomic assemblies. We extend the Atomic Cluster Expansion (ACE) with the correct symmetries for transfer (kinetic-energy) integrals. Combined with a spherical harmonic basis makes, this forms a strong inductive bias and makes for a data efficient model. We introduce coarse-grained and heavy-atom representations, and assess the methodology on representative conjugated semiconductors: ethylene, thiophene, and naphthalene. Intermolecular force ; Degrees of freedom (physics and chemistry) ; Position (finance) ; Charge (physics) ; Basis (linear algebra) ; Transfer (computing) ; Cluster (spacecraft) ; Homogeneous space ; Transfer of learning Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Inorganic Chemistry and Materials
Authors: Maximilian Greif (21221430) | Tobias Frömel (2604664) | Thomas P. Knepper (1598893) | Carolin Huhn (1291917) | Stephan Wagner (1802947) | Michael Pütz (5642450)
Source: Figshare (article, 2025-04-30)
Field: Chemistry — Spectroscopy

Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratoriesby Soft Ionization by Chemical Reaction in Transfer–High-ResolutionMass Spectrometry The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography – mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated. Mass spectrometry ; Gas chromatography ; Gas chromatography–mass spectrometry ; Chemical ionization ; Production (economics) ; Ionization ; Sample preparation ; Electron ionization Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Niki Pouyanfar | Niloofar Masoumi | Mahnaz Ahmadi | Seyed Mohammad Ayyoubzadeh | Golrokh Farnam | Elham Asadian | Farshid Sefat | Farshad H. Shirazi | Fatemeh Ghorbani‐Bidkorbeh
Source: Materials Today Chemistry (article, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

Comparison of laboratory-acquired ZIF-8 drug delivery-related parameters with literature-based data via machine learning tools Drug delivery ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Nanotechnology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: David Mareček | Andrew Nelson | Erwin Pfeiler | Maximilian Eder | Florian Bertram | Stefan Kowarik
Source: Physical review. B./Physical review. B (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

High-speed x-ray reflectometry for characterization of thin film growth at high deposition rates We present a high-throughput, real-time study of PTCDI- <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msub> <a:mi mathvariant="normal">C</a:mi> <a:mn>8</a:mn> </a:msub> </a:math> thin film growth using quick x-ray reflectivity (qXRR) with 12 ms time resolution, combined with machine learning-based analysis. qXRR enables monitoring of vacuum deposition at growth rates from 1 to <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:mrow> <c:mn>30</c:mn> <c:mspace width="0.16em"/> <c:mo>Å</c:mo> <c:mo>/</c:mo> <c:mi mathvariant="normal">s</c:mi> </c:mrow> </c:math> , accessing a previously unexplored regime in molecular beam deposition. To efficiently analyze the resulting <f:math xmlns:f="http://www.w3.org/1998/Math/MathML"> <f:mrow> <f:mo>∼</f:mo> <f:mn>20</f:mn> <f:mspace width="0.16em"/> <f:mn>000</f:mn> </f:mrow> </f:math> reflectivity curves, we employ a convolutional neural network trained on a physics-informed multilayer model. This approach robustly extracts key structural parameters—including thickness, roughness, and crystalline versus amorphous content—from noisy data. We quantify interface roughness as a function of both film thickness and growth rate, identifying rapid roughening with a scaling exponent of <h:math xmlns:h="http://www.w3.org/1998/Math/MathML"> <h:mrow> <h:mi>β</h:mi> <h:mo>=</h:mo> <h:mn>0.62</h:mn> </h:mrow> </h:math> with film thickness and a secondary scaling exponent of <i:math xmlns:i="http://www.w3.org/1998/Math/MathML"> <i:mrow> <i:mi>γ</i:mi> <i:mo>=</i:mo> <i:mn>0.21</i:mn> </i:mrow> </i:math> with growth rate. Additionally, we observe a reduction in the coherently ordered film thickness and a rise in amorphous content at higher deposition rates. These results demonstrate that combining qXRR with machine learning provides quantitative access to fast kinetic growth processes, offering a powerful tool for characterization and morphological control in organic thin film fabrication. Thin film ; Amorphous solid ; Deposition (geology) ; Reflectometry ; Characterization (materials science) ; Surface finish ; Scaling ; Surface roughness Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Angus Keto (17728420) | Elizabeth H. Krenske (1333482)
Source: Figshare (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Deconvoluting theEffects of Substituents on ReactionBarriers through Machine Learning: The Case of Brønsted Acid-MediatedNazarov Cyclizations The barrier heights of Nazarov cyclizations are influenced by substituents in ways that can be challenging to predict. We explore the ability of machine learning (ML) models to predict the barriers by learning the activating and deactivating contributions of different substituents on the divinyl ketone. Random forest and graph neural network models are shown to achieve good predictive accuracies, with mean absolute errors of <i>ca</i>. 2 kcal/mol against density functional theory-calculated barriers, after training on a compact data set of 272 reactions using simple and interpretable features that do not require performing quantum mechanical calculations. Substituents’ electronic and steric feature contributions to the barriers are quantified through feature importance analysis using the SHAP model interpretation framework. To achieve a low barrier for cyclization, the most impactful substrate design strategy entails placing substituents α to the carbonyl group to sterically control the accessible reactant conformations and electronically stabilize the forming oxyallyl cation. The analysis also explains low or high barriers for several substrates that do not conform to empirical rules. This work illustrates the ability of ML models to capture complicated synergistic and antagonistic effects on chemical reactivity, and demonstrates how ML model feature importance analysis complements insights available from density functional theory (DFT). Steric effects ; Density functional theory ; Feature (linguistics) ; Training set ; Electronic effect ; Artificial neural network ; Interpretation (philosophy) ; Molecule Machine Learning in Materials Science ; Cyclization and Aryne Chemistry ; Catalytic C–H Functionalization Methods
Authors: Beatriz Peñín | Nil Sanosa | Cecilia Merino‐Robledillo | Aimara García‐Camacho | Diego Sampedro | Julian A. Hueffel | Ignacio Funes‐Ardoiz
Source: Angewandte Chemie International Edition (article, 2025-10-11)
Field: Chemistry — Organic Chemistry

Mapping Boryl Radical Properties and Reactivity Using Machine Learning: The B‐Rad and React‐B‐Rad Maps Boryl radicals have become indispensable in organic synthesis, yet, translating their complex steric and electronic properties into actionable reactivity insights remains challenging. Herein, we present a comprehensive classification of boryl radicals, including a publicly accessible database of 141 neutral 7e-4c boryl radicals, each parametrized by a set of electronic and steric features derived from DFT calculations. Unsupervised machine learning (k-means clustering) and dimensionality reduction (PCA/UMAP) condense this high dimensional descriptor space into the "B-rad map", capturing trends in sterics and electronics among the resulting five clusters. Global electrophilicity (ω) and nucleophilicity (N) indices are overlaid to create a polarity‑annotated guide, while DFT‑computed activation free energies for six benchmark reactions (HAT, radical addition, and XAT for two different substrates) yield the React‑B‑rad maps that directly link intrinsic properties to specific reaction performance. To demonstrate predictive power, supervised machine learning models (random forest) are trained on the descriptors and successfully predict radical reactivity regimes across all reaction types. Overall, this integrated, machine-learning-driven platform can serve as both a practical guide for experimental decision-making and a foundation for data-driven discovery, paving the way towards rational design and virtual screening of boryl-radical reagents for diverse synthetic applications. Reactivity (psychology) ; Steric effects ; Nucleophile ; Benchmark (surveying) ; Electrophile ; Set (abstract data type) ; Dimensionality reduction Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Radical Photochemical Reactions
Authors: Shen Lv | Wenhao Xue | Min Hao | Yuqi Hu | Xian Zhang | Dianfei Chen | Zhi Gao | Ru Zhou | Haitao Bian | Juncheng Jiang
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-11-12)
Field: Chemistry — Spectroscopy

RF-PSO algorithm-assisted design of hydrophobic components in chitosan/silica composite aerogels Aerogel ; Composite number ; Contact angle ; Moisture ; Thermal stability ; Thermal insulation ; Particle swarm optimization Aerogels and thermal insulation ; Hydrogels: synthesis, properties, applications ; Mesoporous Materials and Catalysis