jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S Jiang | Michael A. Webb
Source: ChemRxiv (preprint, 2025-03-18)
Field: Materials Science — Materials Chemistry

Physics-Guided Neural Networks for Transferable Property Prediction in Architecturally Diverse Copolymers The architectural, compositional, and chemical complexities of polymers are fundamentally important to their properties; however, these same factors obfuscate effective predictions. Machine learning offers a promising approach for predicting polymer properties, but model transferability remains a major challenge, particularly when data is scarce due to high acquisition costs or the growth of the parameter space. Here, we examine whether integration with polymer physics theory effectively enhances the transferability of machine learning models to predict properties of architecturally and compositionally diverse polymers. To do so, we first generate ToPoRg-18k--a dataset reporting the moments of the distribution of squared radius of gyration for 18,450 polymers with diverse architectures, molecular weights, compositions, and chemical patterns. We then systematically assess the performance of several different models on a series of transferability tasks, such as predicting properties of high molecular weight systems from smaller ones or predicting properties of copolymers from homopolymers. We find that a tandem model, GC-GNN, which combines a graph neural network with a fittable model based on ideal Gaussian chain theory, surpasses both standalone polymer-physics and graph neural network models in predictive accuracy and transferability. We also demonstrate that predictive transferability varies with polymer architecture due to deviations from the ideal Gaussian chain assumption. Furthermore, the integration with theory endows GC-GNN with additional interpretability, as its learned coefficients correlate strongly with polymer solvophobicity. Overall, this study illustrates the utility of combining polymer physics with data-driven models to improve predictive transferability for architecturally diverse copolymers, showcasing an extension of physics-informed machine learning for macromolecules. Property (philosophy) ; Copolymer ; Artificial neural network ; Physics ; Artificial intelligence ; Computer science ; Epistemology ; Polymer Machine Learning in Materials Science ; Polymer composites and self-healing
Authors: Kehan Guo | Zhen Liu | Zhichun Guo | Bozhao Nan | Olexandr Isayev | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source:  (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions Reaction yield prediction underpins computer-aided synthesis prediction (CASP). Formulated as a regression problem that takes both reactants and products as input, this task has been extensively studied using machine learning methods, based on handcrafted fingerprint features, SMILES encoded by Transformers, and molecular graphs encoded by Graph Neural Networks. However, a major limitation of these methods is their inability to effectively capture and model the underlying uncertainties, arising both from the inherently stochastic nature of chemical reaction processes and from inconsistencies or noise in how yields are measured and reported. What makes this seemingly simple regression problem even more challenging is the lack of any principled way to account for the underlying uncertainties, due to missing or unrecorded experimental process (commonly happens in chemical labs). Yield (engineering) ; Regression ; Process (computing) ; Artificial neural network ; Task (project management) ; Simple (philosophy) ; Graph ; Chemical reaction Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Rongchang Jiang | Dandan Qu | Ke Wang | Guijie Liu | Xianzhen Dou | Shaohua Lu | Shijie Xie | Wanzhi Wen | A. Xu | Bo Liang
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Multiple Chemical Residues in Pure Cotton Fabric Using Hyperspectral Imaging and Deep Learning This study introduces a rapid and non-destructive method for detecting multiple chemical residues in pure cotton fabrics, utilizing hyperspectral imaging, discrete wavelet transform (DWT), and a one-dimensional convolutional neural network (1D-CNN) to address limitations of traditional techniques, which have low efficiency and destructive nature. Initially, spectral data were collected from pure cotton fabric samples using a hyperspectral imaging system operating within the 400–1000 nm wavelength range , with each sample yielding one averaged spectrum from 100 pixels. The dataset was split 75:25 for training (9,000 spectra) and testing (3,000 spectra), using stratified sample-wise partitioning to prevent spectral leakage. Subsequently, DWT was applied for data dimensionality reduction, eliminating redundant information while retaining critical spectral characteristics. An optimized 1D-CNN model was developed to automatically extract spectral features and classify multiple residues, including formaldehyde, 4-aminobiphenyl, nonylphenol, melamine, and ethanol. The experimental findings demonstrated that the DWT preserved high information entropy (13.02) even after dimensionality reduction to 75 features. Furthermore, its computational efficiency significantly surpassed that of competitive adaptive reweighted sampling and kernel principal component analysis. In conjunction with DWT for dimensionality reduction, the 1D-CNN model achieved an overall classification accuracy of 99.76% and an F1 score of 0.99, exceeding the performance of the support vector machine and random forest models by 2.03% and 38.53%, respectively. This methodology facilitates non-destructive and rapid detection, achieving over 50% increase in detection speed while maintaining high accuracy in identifying formaldehyde residue. It represents an effective technological solution for ensuring textile quality and safety. Future work will focus on designing lightweight network architectures, implementing data augmentation strategies, and expanding the model’s applicability to detect multiple pollutants, aiming to enhance its practical deployment and generalization capability. Hyperspectral imaging ; Pattern recognition (psychology) ; Principal component analysis ; Dimensionality reduction ; Discrete wavelet transform ; Support vector machine ; Convolutional neural network ; Random forest Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Renjie Xia | Wei Li | Yi Cheng | Liangxu Xie | Xiaojun Xu
Source: Biochemical and Biophysical Research Communications (review, 2025-04-11)
Field: Materials Science — Materials Chemistry

Molecular surfaces modeling: Advancements in deep learning for molecular interactions and predictions Computational biology ; Molecular model ; Deep learning ; Nanotechnology ; Chemistry ; Computer science ; Artificial intelligence ; Biology ; Materials science ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Phuoc-Chung Van Nguyen | Van-Thinh To | Ngoc-Vi Nguyen Tran | Tieu-Long Phan | Tuyen Ngoc Truong | Thomas Gärtner | Daniel Merkle | Peter F. Stadler
Source: Digital Discovery (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

SynCat: molecule-level attention graph neural network for precise reaction classification SynCat is a cross-attention GNN that embeds molecules, detects reagents, and estimates reaction-center embeddings via permutation-invariant pairwise aggregation. It delivers improved reaction-classification performance compared with RXNFP and DRFP. Pairwise comparison ; Artificial neural network ; Pattern recognition (psychology) ; Graph ; Deep neural networks Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Trevor Price (2304112) | Saurabh Sivakumar (17138543) | Matthew S. Johnson (1633756) | Judit Zádor (1292451) | Ambarish Kulkarni (1357866)
Source: Figshare (article, 2025-04-13)
Field: Materials Science — Materials Chemistry

Automated Pynta-Based Curriculum for ML-AcceleratedCalculation of Transition States Microkinetic models (MKMs) are widely used within the computational heterogeneous catalysis community to investigate complex reaction mechanisms, to rationalize experimental trends, and to accelerate the rational design of novel catalysts. However, constructing these models requires computationally expensive and manually tedious density functional theory (DFT) calculations for identifying transition states for each elementary reaction within the MKM. To address these challenges, we demonstrate a novel protocol that uses the open-source kinetics workflow tool Pynta to automate the iterative training of a reactive machine learning potential (rMLP). Specifically, using the silver-catalyzed partial oxidation of methanol as a prototypical example, we first demonstrate our workflow by training an rMLP to accelerate the parallel calculation of DFT-quality transition states for all 53 reactions, achieving a 7× speedup compared to a DFT-only strategy. Detailed analysis of our training curriculum reveals the shortcomings of using an adaptive sampling scheme with a single rMLP model to describe all reactions within the MKM simultaneously. We show that these limitations can be overcome using a balanced “reaction class” approach that uses multiple rMLP models, each describing a single class of similar transition states. Finally, we demonstrate that our Pynta-based workflow is also compatible with large pretrained foundational models. For example, by fine-tuning a top-performing graph neural network potential trained on the OC20 dataset, we observe an impressive 20× speedup with an 89% success rate in identifying transition states. This work highlights the synergistic potential of integrating automated tools with machine learning to advance catalysis research. Workflow ; Speedup ; Cheminformatics ; Transition state ; Graph ; Artificial neural network ; Adaptation (eye) ; Class (philosophy) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Lidong You | Zhilin Long | Yong Pei | Yingjie Cui | Rutong Wan
Source: Journal of Non-Crystalline Solids (article, 2025-03-09)
Field: Materials Science — Materials Chemistry

A comparative study on the structure and properties in the Cu-Zr system based on the combination of machine learning force field and molecular dynamics Force field (fiction) ; Molecular dynamics ; Field (mathematics) ; Dynamics (music) ; Computer science ; Artificial intelligence ; Statistical physics ; Materials science ; Biological system ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Biology Machine Learning in Materials Science ; Microstructure and mechanical properties ; Metallic Glasses and Amorphous Alloys
Authors: Michele Reticcioli | Firat Yalcin | Simon Trivisonne | Carla Verdi
Source:  (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Machine Learning for Polaronic Materials: TiO2(110) at the nanoscale Nanoscopic scale ; Scale (ratio) ; Stability (learning theory) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; 2D Materials and Applications
Authors: Dirk Ahrens | Sebastian Wilczek | Charles Robert Koch
Source:  (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

Applications of Machine Learning Algorithms in Plasma Catalysis: Data-Driven Experiments and Simulations Power-to-X processes are crucial for sustainable chemical production but face the major challenge of high energy consumption. To efficiently capture and process greenhouse gases like CO2, advanced technologies are required to convert them in an energy-efficient manner. enaDyne adopts the innovative approach of non-thermal plasma catalysis, where molecules are excited using electrically generated cold plasma. This process enables the synthesis of green basic chemicals, such as ethylene or e-fuels, without significant energy losses, such as those caused by waste heat. Process (computing) ; Energy (signal processing) ; Plasma ; Production (economics) ; Greenhouse gas ; Greenhouse ; Efficient energy use Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Plasma Applications and Diagnostics
Authors: Nicolas Onofrio | Fedor P.M. Goumans
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Atomistic Modeling of Electrodes, Electrolytes, and Interfaces for Next-Generation Batteries with the Amsterdam Modeling Suite The ongoing development of high-performance lithium, sodium-ion and solid-state batteries requires a fundamental understanding of materials at the atomic level, particularly for advancing electrode design, optimizing electrolytes, and engineering stable interfaces. In this contribution, we highlight how the Amsterdam Modeling Suite (AMS) supports cutting-edge research in battery materials by enabling accurate, efficient simulations of key physico-chemical properties. We showcase how AMS combines density functional theory (DFT) and reactive molecular dynamics to study key processes such as ion transport in fast ionic conductors, interfacial stability, and electrolyte decomposition. The AMS driver, together with its powerful Python interface, enables automated high-throughput screening and optimization of materials for improved battery performance. Our multiscale modeling capabilities support on-the-fly training of machine learning potentials, seamless integration with ASE models, and advanced force fields—including ReaxFF, eReaxFF, and a polarizable force field for accurate electrolyte simulations. With these integrated tools, AMS empowers researchers to accelerate R&amp;D efforts and bridge the gap between fundamental insights and real-world battery applications. Figure 1 Suite ; Battery (electricity) ; Key (lock) ; Python (programming language) ; Multiscale modeling ; Modeling and simulation Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Ryoji Asahi | Kōji Shimizu | S. Watanabe | Alex Kutana
Source: Research Square (preprint, 2025-04-23)
Field: Materials Science — Materials Chemistry

Representing Born effective charges with equivariant graph convolutional neural networks Equivariant map ; Convolutional neural network ; Graph ; Computer science ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Pure mathematics Machine Learning in Materials Science ; Topic Modeling ; Quantum many-body systems
Authors: Brian Low (2512972) | Tingting Zhao (1295883) | Xingfang Li (2544160) | Tao Huan (1350477)
Source: Figshare (article, 2025-08-05)
Field: Chemistry — Spectroscopy

Machine Learning-AssistedRecognition of EnvironmentalSulfur-Containing Chemicals in Nontargeted Mass Spectrometry Analysisof Inadequate Mass Resolution Sulfur (S)-containing compounds can be unambiguously identified by their distinctive isotope patterns in mass spectrometry (MS) when the instrument has a mass resolution exceeding 500,000. However, many environmental research laboratories that perform nontargeted analysis rely on high-resolution mass spectrometry (HRMS) instruments, such as quadrupole time-of-flight mass spectrometry (QTOF MS). These HRMS instruments typically operate at a mass resolution of less than 50,000. At such limited resolution, confidently recognizing sulfur isotope patterns is challenging. This work develops a machine learning (ML) strategy for recognizing and predicting the number of S present using HRMS at a mass resolution as low as 25,000. We benchmarked our ML strategy on experimental data, where 200 S-containing standard compounds were mixed into complex environmental samples. In positive electrospray ionization (ESI) mode, our ML strategy achieved accuracies ranging from 87.4 to 95.0% for S recognition and accuracies ranging from 86.3 to 96.6% for S number prediction. Notably, the ML method performed similarly well in negative ESI mode. Our ML strategy was further evaluated on an external experimental water dataset where it correctly recognized the presence of S for all 24 previously reported 2-mercaptobenzothiazole disinfection byproducts (DBPs). The developed ML strategy was implemented into SulfurFinder, an R program, to facilitate automated data cleaning, S recognition, and S number prediction in HRMS data. SulfurFinder combined with HPLC-HRMS analysis of a wastewater sample tentatively identified 169 potential S-containing features. Of these, three were confirmed as S-containing pharmaceuticals. An additional S-containing drug was also putatively annotated using molecular networking. The development of SulfurFinder significantly boosts the capability of conventional HRMS to address the challenge of S recognition in the era of exposomics, supporting a wide range of environmental applications. Mass spectrometry ; Resolution (logic) ; Analytical Chemistry (journal) ; Electrospray ionization ; Isotope ; Mass spectrometry imaging ; High resolution ; Electrospray ; Ranging Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Daniel S. Levine | Nicholas T. Liesen | Lauren Chua | James Diffenderfer | Helgi I. Ingólfsson | Matthew P. Kroonblawd | Nitesh Kumar | Amitesh Maiti | Supun S. Mohottalalage | Muhammed Shuaibi | Brian Van Essen | Brandon M. Wood | C. Lawrence Zitnick | Samuel M. Blau | Evan R. Antoniuk
Source: ArXiv.org (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

The Open Polymers 2026 (OPoly26) Dataset and Evaluations Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformative advances across medicine, consumer products, and energy technologies. While machine learning (ML) models have been trained on millions of quantum chemical atomistic simulations for materials and/or small molecular structures to enable efficient, accurate, and transferable predictions of chemical properties, polymers have largely not been included in prior datasets due to the computational expense of high quality electronic structure calculations on representative polymeric structures. Here, we address this shortcoming with the creation of the Open Polymers 2026 (OPoly26) dataset, which contains more than 6.57 million density functional theory (DFT) calculations on up to 360 atom clusters derived from polymeric systems, comprising over 1.2 billion total atoms. OPoly26 captures the chemical diversity that makes polymers intrinsically tunable and versatile materials, encompassing variations in monomer composition, degree of polymerization, chain architectures, and solvation environments. We show that augmenting ML model training with the OPoly26 dataset improves model performance for polymer prediction tasks. We also publicly release the OPoly26 dataset to help further the development of ML models for polymers, and more broadly, strive towards universal atomistic models. Polymer ; Solvation ; Density functional theory ; Quantum chemical ; Monomer ; Quality (philosophy) ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Jiawei Mao | Yinglu Jia | Gaoyang Gou | Shi Liu | Xiao Cheng Zeng
Source: Chinese Physics Letters (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Enabling Intrinsic Antiferroelectricity in Two-dimensional NbOCl <sub>2</sub> : Molecular Dynamics Simulations based on Deep Learning Interatomic Potential Abstract Compared to the well-studied two-dimensional (2D) ferroelectricity, the appearance of 2D antiferroelectricity is much rarer, where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented. Using NbOCl 2 monolayer with competing ferroelectric (FE) and antiferroelectric (AFE) phases as a 2D material platform, we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl 2 monolayer under experimentally accessible shear strain, along with new functionality associated with electric field-induced AFE-to-FE phase transition. Specifically, the complex configuration space accommodating FE and AFE phases, polarization switching kinetics, and finite temperature thermodynamic properties of 2D NbOCl 2 are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model. Moreover, room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl 2 monolayer. The transition from AFE to FE phase in 2D NbOCl 2 can be triggered by a low critical electric field, leading to a double polarization–electric ( P – E ) loop with small hysteresis. A new type of optoelectronic device composed of AFE-NbOCl 2 is proposed, enabling electric “writing” and nonlinear optical “reading” logical operation with fast operation speed and low power consumption. Antiferroelectricity ; Dipole ; Polarization (electrochemistry) ; Molecular dynamics ; Phase transition ; Monolayer ; Ferroelectricity 2D Materials and Applications ; Perovskite Materials and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Jiangnan Xie | Xiaolong Zheng | Zhihua Ying | Hui Zheng | Liang Zheng
Source: Computational Materials Science (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Enhancing material property prediction via a dual stream model fusing spatial and topological information Dual (grammatical number) ; Property (philosophy) ; Topology (electrical circuits) ; Computer science ; Data mining ; Artificial intelligence ; Mathematics ; Combinatorics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Andrew Rosen
Source: Angewandte Chemie (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Capturing the Complexities of Catalyst–Support Interactions with the Help of Machine Learning Abstract The structure of metal nanoparticles is central to their catalytic activity, but metal–support interactions are difficult to model via quantum‐mechanical calculations. Using a machine‐learned potential to model supported silver nanoparticles, it has been shown that the idealized nanoparticle shapes commonly invoked in the literature do not reflect experiments for diameters below 8 nm, as reported by Maxson and Szilvási. Nanoparticle ; Key (lock) ; Silver nanoparticle Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Complex Network Analysis Techniques