jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Hoffmann | A. Kazimir | T. Oestereich | Luisa Kaermer | Felipe Engelberger | J. Meiler | Christina Lamers
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

A Quantum Lens on Molecular Design: A Machine-Learned Energy Function from Interacting Quantum Atoms Abstract Accurate predictions of the interactions (covalent bonds and non-covalent contacts between atoms) in a molecular system require scalable, accurate, and interpretable energy functions . While classical force fields and knowledge-based energy functions struggle to capture key electronic effects, quantum chemistry approaches such as density functional theory (DFT) provide the necessary accuracy but remain computationally demanding. Furthermore, gaining insight into interactions requires energy decomposition schemes. The Interacting Quantum Atoms (IQA) scheme is exceptionally attractive, offering a chemically intuitive, electron density (ED) topologically based separation into intra- and interatomic contributions, however its high computational cost remains a significant barrier for application to larger systems or tasks like ligand screening in drug discovery. We address these limitations by introducing a novel machine learning (ML) framework to predict accurate energies derived from the IQA scheme together with a comprehensive dataset of molecular systems and their calculated IQA decomposed energies . It enables the rapid and accurate prediction of DFT single point energies and dissects these energies in a physically meaningful and chemically intuitive manner. Our method predicts all intra-atomic energies and inter-atomic interaction energies (covalent and non-covalent) within a defined distance cutoff, providing an energy function that decomposes the total energy into specific atomic contributions. This advance makes the IQA method viable for analyzing interaction energies in applications previously inaccessible due to computational expense, such as elucidating ligand-binding mechanisms and informing rational drug design. Density functional theory ; Function (biology) ; Quantum ; Energy (signal processing) ; Potential energy ; Point (geometry) ; Quantum chemistry ; Molecular dynamics ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tianchuang Gao | Yehao Long | Tongdi Zhang | Jing Zhong | Lijun Zhang
Source: Microstructures (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Integrating 3D CNN and phase-field simulation for TiAlN coating property prediction <i>via</i> 3D microstructure Microstructure evolution in service significantly influences the properties of advanced materials. Numerical simulation can effectively capture microstructure development and provide abundant high-fidelity data. However, effective 3D microstructure-informed property prediction methods are lacking due to the complexity and richness of 3D microstructural data. In this work, a novel approach combining phase-field simulation and a 3D convolutional neural network is proposed to explore the composition-process-structure-property relationship in Ti1-xAlxN coatings. A large dataset of 4,962 simulated 3D microstructures under various heat treatment conditions was first generated using phase-field simulations. Then, a reconstructible feature extraction model was trained to compress each 48 × 48 × 48-grid microstructure into a 128-dimensional latent vector with a reconstruction accuracy of up to 99%. Using the extracted features, a microstructure-based hardness prediction model was constructed, achieving a low prediction error of 1.6 GPa (ca. 5.3% error for an average hardness of 30.8 GPa). The results demonstrate the effectiveness of 3D microstructure-informed deep learning for accurate property prediction, providing a promising tool for the data-driven design of high-performance materials. Microstructure ; Property (philosophy) ; Convolutional neural network ; Feature (linguistics) ; Coating ; Artificial neural network Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Shape Memory Alloy Transformations
Authors: Xi Liu | Da Zhou | Yuming Su
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

AEP-Net: Angle-aware Electrostatic Potential Point Network for Molecular Property Prediction Molecular representation learning is a fundamental task in computational chemistry and drug discovery. While graph neural networks (GNNs) have significantly advanced molecular representation learning, they rely solely on conventional molecular graph, inherently failing to capture critical three-dimensional electronic structure, which are crucial determinants of molecular interactions. To address this, we propose the Angle-aware Electrostatic Potential point Network (AEP-Net), a novel paradigm that explicitly integrates high-resolution 3D Electrostatic Potential (ESP) represented as point clouds. AEP-Net represents molecules as collections of Atomic-centered Electrostatic-potential Subgraphs (AES). We design an angle-aware convolution mechanism that explicitly captures 3D spatial geometric features through learnable directional bases. Each AES processes its internal ESP points via a field-aware interaction layer, employing a triple weighting mechanism based on angular similarity, spatial proximity, and electrostatic values to capture fine-grained local electrostatic patterns. Features from different AESs are aggregated at the atomic level through global pooling, preserving the integrity of the local ESP environment while enabling a scalable architecture. Subsequently, a hierarchical hybrid network deeply integrates multi-scale features at the point cloud, atomic, and molecular levels, Representation (politics) ; Weighting ; Scalability ; Point (geometry) ; Convolution (computer science) ; Graph ; Property (philosophy) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zachery Crandall | Dulitha Prasanna Kulathunga | Lun An | Jie Zhang | Austin Thompson | Tyler B. Hall | Hailey M. Bates | Long Qi | Theresa L. Windus
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

CyRxnOpt: Generalized Interface for Benchmarking Reaction Optimization Algorithms Numerous cutting-edge machine learning algorithms have been developed in recent years for efficiently optimizing chemical processes. While many are open-source, evaluating and applying them to chemical processes remains challenging due to varying, non-standard implementations and interfaces. Additionally, comprehensive comparisons of optimization algorithms are crucial for researchers to make informed decisions about which algorithm will perform best for their process, but there is little information comparing how each algorithm performs on differing chemical processes. CyRxnOpt has been developed as a general, Python-based interface to install, use, and switch between reaction optimization algorithms under a single software interface. By integrating optimizers with the CyRxnOpt interface, many state-of-the-art reaction optimization algorithms can be more easily used in other software and workflows. To demonstrate the use and flexibility of CyRxnOpt, it was used to access four optimization algorithms and benchmark their performance on standard optimization functions and reaction models built from experimental datasets, providing a benchmarking framework which we plan to expand to more optimization algorithms. The initial benchmarking for this limited set of models show that EDBO+ and AMLRO demonstrate robust performance. Benchmarking ; Benchmark (surveying) ; Flexibility (engineering) ; Interface (matter) ; Software ; Set (abstract data type) ; Implementation Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: A. Poddar | Bhrigu Chakraborty | Pratim Kumar Chattaraj
Source: ACS Physical Chemistry Au (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems In electronic structure theory, the field is poised at an inflection point; density-based formulations such as density functional theory (DFT), conceptual DFT, and their time-dependent extensions provide compact, physically grounded representations of many-electron systems, while soft computing methods (viz., machine learning, deep learning, and related data-driven approaches) offer transformative tools for pattern recognition, model development, and accelerated exploration of chemical space. This perspective articulates a vision for tightly coupling density-based theory with modern soft computing, arguing that densities and density-derived information-theoretic and reactivity descriptors are especially well suited as inputs, physical constraints, and interpretive anchors for data-driven models. We propose a practical, hierarchical roadmap that spans standardized data generation (including densities and time-resolved quantities), systematic development of density-based descriptors, model architectures that embed physical constraints, rigorous uncertainty quantification, and interpretability techniques that map learned representations onto chemical intuition. We further outline key application domains and potential research directions that can guide future developments in this emerging area. By forging density-informed soft computing, integrated hierarchically with quantum-mechanical and density functional approaches, the community can accelerate a paradigm shift in which density-aware soft computing becomes a routine and reliable instrument for chemical modeling and innovation. Interpretability ; Key (lock) ; Field (mathematics) ; Perspective (graphical) ; Transformative learning ; Soft computing Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Y.J. Chao | Junfan An | Yuwen Zhao | Na Deng | Yahui Du | Gang Yao | Zhihua Zhou
Source: Applied Materials Today (article, 2026-03-24)
Field: Chemistry — Inorganic Chemistry

Toward clean indoor air: Progress and perspectives of MOFs-based photocatalysts for VOCs elimination HVAC ; Indoor air quality ; Commercialization ; Scope (computer science) ; Efficient energy use ; Indoor air Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Chenyan Wang | Mouyang Cheng | Ji Chen
Source: ArXiv.org (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Beyond overcomplication: a linear model suffices to decode hidden structure-property relationships in glasses Establishing reliable and interpretable structure-property relationships in glasses is a longstanding challenge in condensed matter physics. While modern data-driven machine learning techniques have proven highly effective in establishing structure-property correlations, many models are criticized for lacking physical interpretability and being task-specific. In this work, we identify an approximate linear relation between structure profiles and disorder-induced responses of glass properties based on first order perturbation theory. We analytically demonstrate that this relationship holds universally across glassy systems with varying dimensions and distinct interaction types. This robust theoretical relationship motivates the adoption of linear machine learning models, which we show numerically to achieve surprisingly high predictive accuracy for structure-property mapping in a wide variety of glassy materials. We further devise regularization analysis to further enhance the interpretability of our model, bridging the gap between predictive performance and physical insight. Overall, this linear relation establishes a simple yet powerful connection between structural disorder and spectral properties in glasses, opening a new avenue for advancing their studies. Interpretability ; Bridging (networking) ; Variety (cybernetics) ; Regularization (linguistics) ; Linear model ; Linear response theory ; Perturbation (astronomy) ; Relation (database) Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Phase-change materials and chalcogenides
Authors: Yifan Sun | Hironobu Nakamura | Masaya Kumagai | Yuji Ohishi | Ken Kurosaki
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions Uranium dioxide has been widely used as a nuclear fuel in commercial light water reactors due to its high uranium density and chemical stability. However, its relatively low thermal conductivity is not optimal from the viewpoints of fuel integrity and safety margins, particularly during loss-of-coolant accidents. Although the development of accident-tolerant fuels with higher thermal conductivity is strongly desired, many potential uranium compounds remain unexplored due to constraints associated with handling radioactive materials. To efficiently screen promising uranium compounds with high thermal conductivity, past studies have leveraged machine-learning models to accelerate the discovery process. In this study, we experimentally examine the model's predictions by fabricating UCo and measuring its high-temperature thermophysical properties. Our results show that the thermal conductivity of UCo predicted by machine learning is in good agreement with the experimental measurements. Despite slight discrepancies, additional SHAP analysis suggests that the model's decision logic is consistent with known physical trends. Overall, this study fills a gap in reported thermophysical properties of UCo and provides experimental support for machine-learning-assisted screening of uranium compounds relevant to advanced fuel development. Thermal conductivity ; Uranium ; Uranium dioxide ; Nuclear fuel ; SPARK (programming language) ; Support vector machine ; Thermal Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Yongning Wei | Zhengchen Peng | Yifan Zuo | Qianfeng Yang | Feilan Li | Jingming Ning | Luqing Li
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Rapid Assessment of Green Tea Storage Quality Using Copper-Amino Acid Nanozyme Sensor Arrays Catechin ; Partial least squares regression ; Green tea ; Correlation coefficient ; Relative standard deviation ; Green tea extract Advanced Nanomaterials in Catalysis ; Tea Polyphenols and Effects ; Biosensors and Analytical Detection
Authors: Xia Yang | Beile Chen | Nianjie Liang | Zifeng Huang | Hongcai Yang | Kexin Zhang | Han Yang | Haichang Guo | Wujuan Yan | Yuxi Wang | Guangxu Ju | F. Xu | Nan Tang | Xinqiang Wang | Weikun Ge | Jinlong Du | Zhe Cheng | Peng Gao | Bai Song | Bo Shen
Source: Research Square (preprint, 2026-04-10)
Field: Materials Science — Materials Chemistry

Ultrahigh thermal conductance between III-nitride semiconductor and diamond via interfacial atomic reordering Phonon ; Diamond ; Thermal conductivity ; Semiconductor ; Thermal ; Interfacial thermal resistance ; Wide-bandgap semiconductor ; Thermal conduction ; Conductance Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Qian Gao | Junmin Chen | Kuang Yu
Source: ArXiv.org (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Refinement and Performance Benchmark for Range-Separated Water Force Field In our previous work, we developed a CCSD(T)-level range-separated water force field that combines the power of physics-driven and machine learning models. However, it was found that expensive CCSD(T)/CBS calculations lead to limited number of QM data as well as the missing of force labels, both of which lead to training instability issues. Bulk properties show large variations that cannot be resolved by simply reducing the fitting error in small cluster QM dataset. Such instability in bulk phase simulation is a universal problem in the training of machine learning potentials (MLPs), and is particularly severe at CCSD(T) level of theory.In this work, using our range-separated water model as an example, we aim to overcome these limitations by developing a new training workflow. It is composed by several techniques including: 1. an active learning protocol that ensures more thorough sampling in different temperatures and densities; 2. an intermediate force label technique employing machine learning density functional; and 3. an ensemble knowledge distillation (EKD) method. These techniques significantly stabilize the resulting water model, consistently achieving sub-chemical accuracies in both cluster energies and experimental properties. Benchmarks are carried out for various properties including densities, radial distribution functions (RDFs), dielectric constants, diffusivity, and infrared spectra, all showing state-of-the-art (SOTA) performances and proving the effectiveness of the training protocol. Benchmark (surveying) ; Stability (learning theory) ; Field (mathematics) ; Force field (fiction) ; Sampling (signal processing) ; Cluster (spacecraft) ; Phase (matter) ; Missing data Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: R. Seda Tığlı Aydın | Fevziye Eğilmez | Ceren Kaya
Source: Polymers (article, 2026-01-26)
Field: Materials Science — Biomaterials

Effect of Artificial Neural Network Design Parameters for Prediction of PS/TiO2 Nanofiber Diameter In this study, polystyrene (PS) and PS/TiO<sub>2</sub> nanofibers were fabricated through electrospinning and quantitatively characterized to analyze and predict fiber diameters. To advance predictive methodologies for materials design, artificial neural network (ANN) models based on multilayer perceptron (MLP) and radial basis function (RBF) architectures were developed using system- and process-level parameters as inputs and the fiber diameter as the output. Two data classes were constructed: Class 1, consisting of PS/TiO<sub>2</sub> nanofibers, and Class 2, containing both PS and PS/TiO<sub>2</sub> nanofibers. The architectural optimization of the ANN models, particularly the number of neurons in hidden layers, had a critical influence on the correlation between predicted and experimentally measured fiber diameters. The optimal MLP configuration employed 40 and 20 neurons in the hidden layers, achieving mean square errors (MSEs) of 4.03 × 10<sup>-3</sup> (Class 1) and 7.01 × 10<sup>-3</sup> (Class 2). The RBF model reached its highest accuracy with 30 and 250 neurons, yielding substantially lower MSE values of 1.42 × 10<sup>-32</sup> and 2.75 × 10<sup>-32</sup> for Class 1 and Class 2, respectively. These findings underline the importance of methodological rigor in data-driven modeling and demonstrate that carefully optimized ANN frameworks can serve as powerful tools for predicting structural features in nanostructured materials, thereby supporting rational materials design and synthesis. Artificial neural network ; Radial basis function ; Fiber ; Multilayer perceptron ; Mean squared error ; Nanofiber ; Perceptron ; Polystyrene Electrospun Nanofibers in Biomedical Applications ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Masaru Kondo | Mohamed S. H. Salem | Shinobu Takizawa
Source: Journal of Synthetic Organic Chemistry Japan (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Multiparameter Optimization for Efficient Screening of Reaction Conditions in Flow and Electrochemical Synthesis Optimization of reaction conditions is indispensable yet time-consuming in developing new reactions. Advanced modalities such as flow and electrochemical reactions often require substantially more experiments to identify optimal conditions compared to conventional batch processes. While machine learning approaches can efficiently predict suitable conditions, they typically rely on specialized informatics expertise and large, carefully curated datasets. In this review, we highlight the application of data-driven multiparameter screening with small datasets-more accessible to experimental chemists-to the exploration of conditions in flow and electrochemical reactions. Yield mapping with Gaussian process regression enabled the visualization of predicted yields across the design space for an organocatalytic flow domino reaction, allowing the identification of high-yielding conditions with only ten experiments. Similarly, Bayesian optimization (BO)-based multiparameter screening of electrooxidation conditions successfully identified optimal high-yielding conditions within twelve experiments. Categorical BO incorporating one-hot encoding identified six key parameters-including mixer selection-for an organocatalytic flow biaryl synthesis, achieving substantial yield improvement in fifteen experiments. In addition, constrained BO applied to the electrochemical flow synthesis of amino acids accomplished two simultaneous objectives-high yield and reduced energy consumption-with only nine experiments. Yield (engineering) ; Bayesian optimization ; Categorical variable ; Flow (mathematics) ; Identification (biology) ; Process (computing) ; Electrochemistry ; Visualization Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Tao Yang | Jiaqing Zhang | Haotian Chen | Xiaoze Yuan | Yu Zhou | Wenping Guo | Xingchen Liu | Xiaotong Liu | Jinjia Liu
Source: The Journal of Physical Chemistry Letters (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Data-Driven Deciphering Structure–Mössbauer Spectroscopy Relationships in Iron-Based Compounds Accurate identification of material structures is crucial for establishing reliable structure-property relationships, yet this task is often hindered by the coexistence of multiple metastable phases and their facile transformations under reaction conditions. Although spectroscopic techniques are powerful tools for probing structural motifs, the lack of comprehensive reference data remains a critical bottleneck for experimental analysis. Here, we propose a unified strategy that integrates structure prediction, thermodynamic stability calculations, machine learning models, and Mössbauer spectroscopy simulations, tailored for iron-based intermetallic compounds. By constructing quantitative relationships between local structural features and Mössbauer parameters with machine learning, and by leveraging a theoretical spectral database to interpret experimental spectra, we successfully identified candidate iron sulfide metastable phases that were previously unresolved. This data-driven framework provides a robust complement to experimental approaches, enabling more accurate and efficient identification of metastable phases in dynamically evolving systems. Metastability ; Bottleneck ; Identification (biology) ; Complement (music) ; Spectroscopy ; Intermetallic Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography