jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jakub Kubečka (2828228) | Yosef Knattrup (13293179) | Georg Baadsgaard Trolle (22076383) | Bernhard Reischl (6816974) | August Smart Lykke-Møller (22076386) | Jonas Elm (1390975) | Ivo Neefjes (14013804)
Source: Figshare (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Thermodynamicsof Molecular Binding and Clusteringin the Atmosphere Revealed through Conventional and ML-Enhanced UmbrellaSampling Accurately modeling the binding free energies associated with molecular cluster formation is critical for understanding atmospheric new particle formation. Conventional quantum-chemistry methods, however, often struggle to describe thermodynamic contributions, particularly in systems exhibiting significant anharmonicity and configurational complexity. We employed umbrella sampling, an enhanced-sampling molecular dynamics technique, to compute Gibbs binding free energies for clusters formed from a diverse set of new particle formation precursors, including sulfuric acid, ammonia, dimethylamine, and water. By performing umbrella sampling along the evaporation coordinate, using forces computed at the semiempirical GFN1-xTB level of theory, we effectively capture entropic effects such as vibrational anharmonicities and transitions between different configurational minima, while avoiding errors from symmetry overcounting. In addition, we explored machine-learning-enhanced umbrella sampling simulations using neural network potentials trained on higher-level quantum chemistry data, demonstrating the feasibility of this approach for improving accuracy while maintaining computational efficiency. Our results show improved agreement with experimental values compared to conventional methods. We also present examples of gas-to-particle uptake processes, providing insights into cluster and aerosol–surface chemistry using first-principles approaches rather than commonly used molecular-mechanics force fields. This study demonstrates the importance of accounting for dynamics in predicting molecular binding thermodynamics in complex environments and highlights the potential of combining physics-based simulations with machine learning for reliable and scalable predictions. Molecular dynamics ; Anharmonicity ; Sampling (signal processing) ; Umbrella sampling ; Cluster (spacecraft) ; Metadynamics ; Cluster analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Raffaele Cheula (8848727) | Mie Andersen (1527007)
Source: Figshare (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Transition States Energies from Machine Learning:An Application to Reverse Water–Gas Shift on Single-Atom Alloys Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and <i>ab initio</i> methods such as density functional theory (DFT). Here, we propose a machine learning (ML) model for predicting TS energies based on Gaussian process regression with the Wasserstein Weisfeiler-Lehman graph kernel (WWL-GPR). Applying the model to predict adsorption and TS energies for the reverse water–gas shift (RWGS) reaction on single-atom alloy (SAA) catalysts, we show that it can significantly improve the accuracy compared to traditional approaches based on scaling relations or ML models without a graph representation. Further benefiting from the low cost of model training, we train an ensemble of WWL-GPR models to obtain uncertainties through subsampling of the training data and show how these uncertainties propagate to turnover frequency (TOF) predictions through the construction of an ensemble of microkinetic models. Comparing the errors in model-based vs DFT-based TOF predictions, we show that the WWL-GPR model reduces errors by almost an order of magnitude compared to scaling relations. This demonstrates the critical impact of accurate energy predictions on catalytic activity estimation. Finally, we apply our model to screen other materials, identifying promising catalysts for RWGS. This work highlights the power of combining advanced ML techniques with DFT and microkinetic modeling for screening catalysts for complex reactions like RWGS, providing a robust framework for future catalyst design. Scaling ; Bottleneck ; Density functional theory ; Gaussian ; Kernel (algebra) ; Gaussian process ; Work (physics) ; Graph Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Naoya Ohtsuka | Muhammad Zhafran MOHD ARIS | Toshiyasu Suzuki | Norie Momiyama
Source: ChemRxiv (preprint, 2025-09-22)
Field: Chemistry — Organic Chemistry

Data-guided rational design of additives for halogenation of highly fluorinated naphthalenes: Integrating fluorine chemistry and machine learning Halogenation ; Rational design ; Yield (engineering) ; Fluorine ; Naphthalene ; Feature (linguistics) ; Generality Chemical Synthesis and Reactions ; History and advancements in chemistry ; Fluorine in Organic Chemistry
Authors: Dong Han | Gyoung S. Na | Hyun Woo Kim
Source: ChemRxiv (preprint, 2025-01-24)
Field: Chemistry — Electrochemistry

The Nature of Chemical Bonds in the Age of Artificial Intelligence: Revisiting Electronegativity of Organic Molecules Electronegativity can be considered a data-driven concept that is widely used since Pauling proposed this property. However, updating the electronegativity based on the vast amount of high quality experimental and computational data has been overlooked. Thus, advances in artificial intelligence (AI), with its ability to manage large datasets and identify underlying patterns, necessitate reconsid ering data-driven concepts such as electronegativity. In this work, we present a data-driven method to generate more informative multidimensional electronegativity of organic molecules using graph neural networks. Focusing on two-dimensional electronegativity, we were able to do a more detailed classification of the atoms and their covalent bonds. By replacing the conventional electronegativity with the newly proposed one, we demonstrated the performance improvement in molecular machine learning tasks. We believe that our findings will be useful in understanding of electronegativity and chemical bonds by judiciously applying AI-driven methods to chemical studies. Electronegativity ; Organic molecules ; Molecule ; Computational chemistry ; Chemical bond ; Chemistry ; Chemical physics ; Materials science ; Nanotechnology ; Organic chemistry Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: 貴史 山村 | Takashi Yamamura | 明 住谷 | Akira Sumitani | 卓志 齊藤 | TAKUSHI SAITO
Source: Institutional Repositories DataBase (IRDB) (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temperature measurement using thermochromic liquid crystals with scattered light intensity measurements and machine learning Temperature measurement ; Thermochromism ; Intensity (physics) ; Light intensity ; Liquid crystal ; Light scattering Liquid Crystal Research Advancements ; Phase-change materials and chalcogenides ; Surface Roughness and Optical Measurements
Authors: Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: npj Computational Materials (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy Workflow ; Benchmark (surveying) ; Phase (matter) ; Work (physics) ; Diagram Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Isaac Wilfried Sanou | Julien Baderot | Vincent Barra | Ali Hallal | Léo Mazauric | J. Foucher
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2025-10-22)
Field: Materials Science — Surfaces, Coatings and Films

Publisher’s note: Deep-learning-driven automation of inner and outer object segmentation in SEM/TEM imaging for semiconductor metrology Metrology ; Automation ; Object (grammar) ; Segmentation ; Image segmentation Electron and X-Ray Spectroscopy Techniques
Authors: Siddarth
K. Achar (11061524) | Priyanka B. Shukla (9868426) | Chinmay V. Mhatre (22175395) | Leonardo Bernasconi (1373265) | Caitlyn Y. Vinger (22175398) | J. Karl Johnson (1519432)
Source: Figshare (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Reactive Active Learning: An Efficient Approach forTraining Machine Learning Interatomic Potentials for Reacting Systems Discovering chemical reaction pathways using quantum mechanics is impractical for many systems of practical interest because of unfavorable scaling and computational cost. While machine learning interatomic potentials (MLIPs) trained on quantum mechanical data offer a promising alternative, they face challenges for reactive systems due to the need for extensive sampling of the potential energy surface in regions that are far from equilibrium geometries. Unfortunately, traditional MLIP training protocols are not designed for comprehensive reaction exploration. We present a reactive active learning (RAL) framework that is designed to efficiently train MLIPs to achieve near-quantum mechanical accuracy for reactive systems for situations where one may not have prior knowledge of the possible transition states, reaction pathways, or even the potential products. Our method combines automated reaction exploration, uncertainty-driven active learning, and transition state sampling to build accurate potentials. We demonstrate RAL’s effectiveness across three different systems: uncatalyzed ammonia synthesis (gas-phase), methanimine hydrolysis (solution phase), and methane activation on titanium carbide surfaces (heterogeneous). The resulting MLIPs accurately predict reaction barriers and transition states. For catalysis, we show that RAL-trained MLIPs identify Ti<sub>2</sub>C as the most active methane activation surface (90% decomposition at 1000 K) through C-vacancy mediated mechanisms. The framework’s ability to simulate large systems (∼900 atoms) over nanosecond time scales provides previously inaccessible insights into surface poisoning and reaction networks. We show that reactive exploration is essential for adequately capturing the potential energy surface, with chemical and configurational sampling working synergistically to improve model accuracy. Our results establish general guidelines for training robust reactive potentials and open new possibilities for computational discovery of catalysts and reaction mechanisms. Potential energy surface ; Chemical reaction ; Active learning (machine learning) ; Potential energy ; Molecular dynamics ; Sampling (signal processing) ; Interatomic potential ; Active site ; Decomposition Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Amiya Chowdhury | Acacio Rincón Romero | Eduardo Aguilar-Bejarano | Halar Memon | Grazziela P. Figueredo | Tanvir Hussain
Source: ArXiv.org (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

A Methodological Study on Data Representation for Machine Learning Modelling of Thermal Conductivity of Rare-Earth Oxides Quantitative structure-activity relationship (QSAR) modelling is widely employed in materials science to predict properties of interest and extract useful descriptors for measured properties. In thermal barrier coatings (TBC), QSAR can significantly shorten the experimental discovery cycle, which can take years. Although machine learning methods are commonly employed for QSAR, their performance depends on the data quality and how instances are represented. Traditional, hand-crafted descriptors based on known material properties are limited to represent materials that share the same basic crystal structure, limited the size of the dataset. By contrast, graph neural networks offer a more expressive representation, encoding atomic positions and bonds in the crystal lattice. In this study, we compare Random Forest (RF) and Gaussian Process (GP) models trained on hand-crafted descriptors from the literature with graph-based representations for high-entropy, rare-earth pyrochlore oxides using the Crystal Graph Convolutional Neural Network (CGCNN). Two different types of augmentation methods are also explored to account for the limited data size, one of which is only applicable to graph-based representations. Our findings show that the CGCNN model substantially outperforms the RF and GP models, underscoring the potential of graph-based representations for enhanced QSAR modelling in TBC research. Artificial neural network ; Quantitative structure–activity relationship ; Convolutional neural network ; Representation (politics) ; Gaussian process ; Random forest ; Graph ; Experimental data ; Kriging Machine Learning in Materials Science ; Nuclear Materials and Properties ; Inorganic Chemistry and Materials
Authors: Elias L. Mann | Corin Wagen | Jonathon E. Vandezande | Arien Wagen | Serge Schneider
Source: ArXiv.org (preprint, 2025-04-29)
Field: Materials Science — Materials Chemistry

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation Accurate simulation of atomic systems has the potential to revolutionize the design of molecules and materials. Unfortunately, exact solutions of the Schrödinger equation scale as O(N!) and remain inaccessible for systems with more than a handful of atoms, forcing scientists to accept steep tradeoffs between speed and accuracy and limiting the reliability and utility of the resultant simulations. Recent work in machine learning has demonstrated that neural network potentials (NNPs) can learn efficient approximations to quantum mechanics and resolve this tradeoff, but existing NNPs still suffer from limited accuracy relative to state-of-the-art quantum-chemical methods. Here, we present Egret-1, a family of large pretrained NNPs based on the MACE architecture with general applicability to main-group, organic, and biomolecular chemistry. We find that the Egret-1 models equal or exceed the accuracy of routinely employed quantum-chemical methods on a variety of standard tasks, including torsional scans, conformer ranking, and geometry optimization, while offering multiple-order-of-magnitude speedups relative to legacy methods. We also highlight important lacunae for future NNP research to investigate, and suggest strategies for building future high-quality models with increased scale and generality. Artificial neural network ; Limiting ; Forcing (mathematics) ; Scale (ratio) ; Reliability (semiconductor) ; Variety (cybernetics) ; Work (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum many-body systems
Authors: Zhuang Xu (3828355) | Ziming Wang (403663) | Rui Zhang (13940) | Archana Verma (3821965) | Justin S. Neu (22210010) | Priyotosh Bairagya (22210013) | Sanghyun Jeon (6622553) | Alec M. Damron (22210016) | Joshua M. Rinehart (12886063) | Justine S. Wagner (5641451) | Sina Sabury (10024876) | Xuyi Luo (4512889) | Anna M. Österholm (1681945) | Jianguo Mei (1419895) | Jeffrey S. Moore (1385685) | Wei You (474340) | John R. Reynolds (1288521) | Nicholas E. Jackson (1348692) | Ying Diao (191325)
Source: Figshare (article, 2025-09-08)
Field: Chemistry — Organic Chemistry

UbiquitousChiral Symmetry Breaking of ConjugatedPolymers via Liquid–Liquid Phase Separation Spontaneous chiral symmetry breaking remains a fascination in chemistry, biology, materials science, and even astronomy. Chiral symmetry breaking usually requires intrinsic molecular chirality or extrinsic chiral sources but remains rare in nonchiral systems. Here, we reveal a ubiquitous, entropy-driven chiral symmetry breaking mechanism observed in 22 out of 35 conjugated polymers in the absence of any chiral sourcea phenomenon overlooked for decades. Chiral assemblies spontaneously occur through liquid–liquid phase separation (LLPS) of lyotropic mesophases from isotropic solutions upon a concentration increase. Machine learning identifies the underpinning molecular features validated by further molecular design. The universality of this phenomenon hints at a possible link between LLPS and chiral symmetry breaking in the origin of life, while paving the way for an emerging frontier of chiral electronics. Chiral symmetry breaking ; Symmetry breaking ; Chirality (physics) ; Chiral anomaly ; Spontaneous symmetry breaking ; Polymer ; Lyotropic ; Nambu–Jona-Lasinio model Synthesis and Properties of Aromatic Compounds ; Liquid Crystal Research Advancements ; Surface Chemistry and Catalysis
Authors: Mood Mohan (2871095) | Sreelekha Guggilam (18431821) | Debsindhu Bhowmik (2545723) | Michelle K. Kidder (4542715) | Jeremy C. Smith (192295)
Source: Figshare (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

LeveragingNatural Language Processing and GenerativeModels in Molecular Chemistry: Property Prediction and Novel CompoundGeneration The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log <i>P</i>), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log <i>P</i>, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (<i>R</i><sup>2</sup> = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log <i>P</i> and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log <i>P</i>, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents. Artificial neural network ; Enthalpy of vaporization ; Generative grammar ; Correlation coefficient ; Quantitative structure–activity relationship ; Predictive modelling ; Property (philosophy) ; Molecular descriptor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Hyukwon Kwon | Chonghyo Joo | Man Sig Lee | Jaeho Baek | qi meng | Jae‐Won Lee | Junghwan Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Knowledge-guided machine learning with an operating time index for selectivity prediction and optimization in industrial acetylene hydrogenation Artificial neural network ; Naphtha ; Ethylene ; Particle swarm optimization ; Acetylene ; Bayesian optimization ; Fluid catalytic cracking ; Decision tree ; Sensitivity (control systems) Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Da Bean Han | Gyoung S. Na | Hyun Woo Kim
Source: ChemRxiv (preprint, 2025-01-23)
Field: Chemistry — Electrochemistry

The Nature of Chemical Bonds in the Age of Artificial Intelligence: Revisiting Electronegativity of Organic Molecules Electronegativity can be considered a data-driven concept that is widely used since Pauling proposed this property. However, updating the electronegativity based on the vast amount of high quality experimental and computational data has been overlooked. Thus, advances in artificial intelligence (AI), with its ability to manage large datasets and identify underlying patterns, necessitate reconsid ering data-driven concepts such as electronegativity. In this work, we present a data-driven method to generate more informative multidimensional electronegativity of organic molecules using graph neural networks. Focusing on two-dimensional electronegativity, we were able to do a more detailed classification of the atoms and their covalent bonds. By replacing the conventional electronegativity with the newly proposed one, we demonstrated the performance improvement in molecular machine learning tasks. We believe that our findings will be useful in understanding of electronegativity and chemical bonds by judiciously applying AI-driven methods to chemical studies. Electronegativity ; Organic molecules ; Chemical bond ; Molecule ; Chemical physics ; Computational chemistry ; Chemistry ; Psychology ; Organic chemistry Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Giuseppe M. J. Barca
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

High-Performance, Quantum and AI-Driven Chemical Discovery Addressing today s most pressing technological and scientific challenges from developing new therapeutics and biotechnologies to creating greener chemical processes requires the ability to accurately model matter at large atomistic scales and with high speed. However, current simulation techniques can be prohibitively slow, imprecise, or computationally too costly, resulting in heavy reliance on physical experiments that are time-consuming, expensive, and subject to inherent limitations. In response, we present a pioneering digital chemistry framework that integrates many-GPU high-performance computing, computational quantum chemistry, and machine learning, delivering unprecedented accuracy and scalability in chemical discovery particularly for drug design. By leveraging novel algorithms and software optimizations, we overcome the steep computational barriers that have historically limited quantum chemistry at large molecular scales. Recognized with the 2024 Gordon Bell Prize, our approach enables the first quantum-level simulations of complex bioscale molecular systems with accuracy nearing that of physical experiments. Through exascale supercomputing, agentic AI workflows, and machine learning models trained on quantum-accurate data, we streamline end-to-end simulation and design processes. These intelligent workflows reduce reliance on laboratory experiments, providing a fully automatable, cost-effective, adaptive, and accurate solution for investigating complex molecular phenomena. The result is a powerful platform that accelerates chemical discovery, enhances molecular design, and redefines in silico research opportunities across chemistry, biology, and related fields. Workflow ; Scalability ; Quantum chemical ; Software ; Scientific discovery ; Drug discovery ; Quantum ; Computational model Machine Learning in Materials Science ; Scientific Computing and Data Management ; Quantum Computing Algorithms and Architecture