jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dilina Perera | Sara McAllister | Joan J. Cerdà | Thomas Vogel
Source: Journal of Chemical Theory and Computation (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer We use a semisupervised, neural-network-based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole-dipole and Lennard-Jones interactions. As input for the neural network, we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang-Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations. Dipole ; Confusion ; Artificial neural network ; Replica ; Phase diagram ; Statistical physics ; Polymer ; Phase transition ; Materials science ; Computer science ; Particle (ecology) ; Magnetic nanoparticles ; Nanoparticle ; Chemical physics ; Condensed matter physics ; Artificial intelligence ; Physics ; Phase (matter) ; Nanotechnology ; Nuclear magnetic resonance ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Jie Chen | Pengfei Ou | Yuxin Chang | Hengrui Zhang | Xiaoyan Li | Edward H. Sargent | Wei Chen
Source:  (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Adaptive Uncertainty-Aware Deep Learning for Materials Discovery With High-Dimensional Design Inputs Abstract High-dimensional structure and composition spaces present a major challenge in materials discovery due to the difficulty of efficiently navigating vast and complex design space. Additionally, most existing machine learning approaches lack the capability to quantify epistemic uncertainty, which arises from limited data, a critical limitation for materials discovery tasks involving high-dimensional representations such as atomic structures. To address these challenges, we introduce UPNet, an uncertainty-aware atomistic machine learning model within a Bayesian Optimization (BO) framework. UPNet enables automated representation learning directly from high-dimensional atomic structures while providing principled uncertainty quantification through the use of Spectral-normalized Neural Gaussian Process (SNGP). By incorporating a constrained expected improvement acquisition function, our BO framework optimizes multiple evaluation criteria simultaneously. We demonstrate the effectiveness of our approach in catalyst discovery for the CO2 reduction reaction, where it achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization. Our method reduces computational cost and time, achieving a 10× reduction in the number of required simulation calculations. Beyond catalysis, this framework offers a broadly applicable solution for accelerating materials discovery in various domains that involve high-dimensional design inputs and expensive physics-based simulations. Bayesian optimization ; Representation (politics) ; Deep learning ; Process (computing) ; Gaussian process ; Reduction (mathematics) ; Feature (linguistics) ; Bayesian probability ; Uncertainty quantification Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Adithya
Ranjith Kartha (21191213) | Dhanush P. Ajayakumar (21191216) | Muhammad Idris (660839) | Gopi Ragupathy (6876887)
Source: Figshare (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Unlocking the Potentialof Machine Learning in EnhancingQuantum Chemical Calculations for Infrared Spectral Prediction Infrared (IR) spectroscopy is a fundamental tool for analyzing molecular structures and chemical interactions by identifying the vibrational modes of molecules. Traditional quantum mechanical methods, such as density functional theory, are highly accurate but computationally expensive and impractical for large-scale molecular systems. This project investigates the integration of machine learning (ML) techniques to predict IR spectra, offering a promising alternative that significantly reduces computational costs while maintaining high accuracy. Additionally, the project explores the utilization of IR spectra for molecular identification and classification into molecular families, enhancing the practical utility of spectral data in various scientific applications. Using TensorFlow-based ML frameworks, models were developed and trained on a data set derived from high-quality computational chemistry analyzers. These data sets, sourced from computationally optimized geometry and IR spectrum from the Gaussian 16 Program Suite, include extensive molecular geometry data, bond lengths, vibrational modes, and other quantum mechanical properties. The models aim to predict key IR spectral features, such as vibrational frequencies and intensities, while maintaining interpretability by linking chemical and quantum mechanical principles to predictions. The integration of ML with IR spectroscopy provides a scalable as well as accelerated solution for analyzing complex molecular systems. This approach holds potential in fields such as drug discovery, materials science, and chemical engineering, where rapid and accurate spectral predictions are critical. This perspective highlights the advancements achieved, the current challenges, and the future potential of ML in the context of IR spectroscopy, providing a solid foundation for further exploration at the intersection of chemistry and data science. Interpretability ; Context (archaeology) ; Two-dimensional infrared spectroscopy ; Quantum chemistry ; Infrared spectroscopy ; Gaussian ; Quantum ; Infrared ; Intersection (aeronautics) ; Spectroscopy Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Molecular spectroscopy and chirality
Authors: K. Liu | Wenxia Xu | Feng Liang
Source:  (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Mechanical Property Prediction of Porous Ceramic Materials Based on Convolutional Neural Network The property prediction of porous ceramic materials is a crucial step in material performance assessment, directly influencing performance analysis and material design. Currently, the commonly used methods for predicting the mechanical properties of porous ceramic materials mostly rely on physical models and experimental methods. Although they can provide certain prediction accuracy, when dealing with complex material structures and large-scale data, they face high time costs and material consumption. This paper proposes a method for predicting the mechanical properties of porous ceramic materials based on deep learning. It uses neural network models to automatically analyze and predict the features of material image data, combines deep learning and image processing technologies, and develops a complete framework including data preprocessing, model training, validation and testing. The model is based on various formats of data, including 3D and 2D images, processes large-sized image data through sliding window technology, and predicts their physical properties. Multiple neural network architectures are adopted, and through retraining and model optimization, the key features such as porosity and pore size distribution of the materials are analyzed, and the prediction accuracy of different model architectures is compared. The research results show that this deep learning method exhibits excellent prediction accuracy of up to 90% in the feature prediction of porous ceramic materials, and significantly improves the efficiency of data processing. This research provides a new approach for the mechanical property prediction of complex porous structure materials. Property (philosophy) ; Convolutional neural network ; Porosity ; Ceramic ; Artificial neural network ; Deep learning ; Feature (linguistics) ; Performance prediction Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; Applied Advanced Technologies
Authors: Anita Halder | Durga Paudyal | Stefano Sanvito | Martin Takáč | Huseyin Ucar
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Discovery of Hard Magnetic Materials Using High-Throughput Computation and Screening Throughput ; Computation ; High-throughput screening ; Computer science ; Machine learning ; Artificial intelligence ; Biology ; Algorithm ; Bioinformatics ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Clayton Ellis (22881095) | Xinyi Fang (6679268) | Christopher Balzer (4390693) | Timothy Quah (4899439) | M. Scott Shell (267372) | Glenn H. Fredrickson (1436974) | Mengyang Gu (7371956)
Source: Figshare (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Fast PhasePrediction of Charged Polymer Blends byWhite-Box Machine Learning Surrogates Compatibilized polymer blends are a complex yet versatile and widespread category of materials. When the components of a binary blend are immiscible, they are typically driven toward a macrophase-separated state, but with the introduction of electrostatic interactions, they can be either homogenized or shifted to microphase separation. However, both experimental and simulation approaches face significant challenges in efficiently exploring the vast design space of charge-compatibilized polymer blends, encompassing chemical interactions, architectural properties, and composition. In this work, we introduce a white-box machine learning approach integrated with polymer field theory to predict the phase behavior of these systems, which is significantly more accurate than conventional black-box machine learning approaches. The random phase approximation (RPA) calculation is used as a testbed to determine polymer phases. Instead of directly predicting the polymer phase output of RPA calculations from a large input space by a machine learning model, we build a parallel partial Gaussian process model to predict the most computationally intensive component of the RPA calculation that involves only polymer architecture parameters as inputs. This approach substantially reduces the computational cost of the RPA calculation across a vast input space with nearly 100% accuracy for the out-of-sample prediction, enabling rapid screening of polymer blend charge-compatibilization designs. More broadly, the white-box machine learning strategy offers a promising approach for the dramatic acceleration of polymer field-theoretic methods for mapping out polymer phase behavior. Polymer blend ; Polymer ; Gaussian process ; Binary number ; Polymer architecture ; Component (thermodynamics) ; Random phase approximation ; Phase (matter) ; Field (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Kerrin Janssen | Jan M. Wollschläger | Jonny Proppe | Andreas H. Göller
Source: ChemRxiv (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

When Machine Learning Models Learn Chemistry II: Applying WISP to Real-World Examples In our previous work, we introduced WISP (Workflow for Interpretability Scoring using matched molecular Pairs), which enables users to quantitatively assess the performance of explainability methods for machine learning models. In this work, we focus on more complex tasks, such as yield prediction, pKi values for inhibition of coagulation Factor Xa and AMES mutagenicity, where the explanations of the predicted property need to capture more intricate interaction patterns between structural motifs of either the reaction partner, the protein or DNA. Expanding upon Part I of the "When Machine Learning Learns Chemistry" series, we demonstrate additional functionalities of the WISP workflow. Alongside the model and descriptor-agnostic atom attributor, WISP integrates a SHAP-based and an RDKit-based attribution method, enabling the comparison of multiple explainability approaches, as demonstrated on the Factor Xa dataset. This work also showcases WISP’s capability to evaluate explanations for classification tasks such as AMES mutagenicity. The application of WISP to the AMES mutagenicity dataset reveals that the respective machine learning model fails to learn the underlying chemical relationships, instead relying primarily on numerical correlations. When applied to the yield dataset, WISP highlights specific cases where explainability methods that usually perform well fail to provide meaningful insights. This highlights WISP’s ability to detect such limitations in trained models, providing valuable insights to guide targeted improvements in model development and data quality. Interpretability ; Deep learning ; Focus (optics) ; Property (philosophy) ; Training set ; Factor (programming language) Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Risboy Djuraev | Yulduz Umarova | Ziynat Olimjonova | Mukaddas Karimova | Shokhista Abduganiyeva | Lobarkhan Karakhanova
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Harnessing deep learning for predicting nonlinear optical responses in 0D and 2D materials The integration of deep learning with nonlinear optics opens new frontiers in understanding and predicting nonlinear optical responses of advanced 0D and 2D materials. Experimental data across diverse materials and deep learning frameworks correlate microscopic material properties with measured nonlinear responses. A paradigm of nonlinear optics combined with deep learning enables accurate computation of microscopic nonlinear responses from bulk material observables. A unified deep-structured learning architecture predicts the nonlinear optical response of 0D and 2D semiconductor materials at arbitrary wavelengths, capturing multiphoton processes up to the third order. Through model interpretation, insights on the impact of atomic features and common descriptors emerge, enabling accurate predictions based on a single lowest-order nonlinear response. Industry-wide applications accelerate research in nonlinear optics, quantum information, and related photonic fields. Nonlinear optics concerns phenomena where the optical response to an electromagnetic field depends on its intensity. Suspended 2D materials possess near-unity 100 GW/cm^2 |&chi;^(3)| nonlinearities across wavelengths from THz to visible and exhibit ballistic charge transport at room temperature through 20-nm channels. Individual colocalized dark-field scattering and surface-enhanced Raman scattering studies reveal strong electromagnetic field enhancement in sub-nm gaps between 2D heterostructures, with enhancement factors exceeding 10^7. Materials display transverse to longitudinal acoustic mode conversion dictated by incommensurability, along with intrinsic elastic energy dissipation up to 40 GHz. In deep-structured—or deep—learning, cascading of simple neural-network units compares the correctness of an input, such as text in translation-application software, against the guess of the output or ideal label that the network would like to obtain. Introducing a recursion operator at the network’s reasoning heart, a neural program interpreter learns from input-output pairs to induce simple algorithms. Nonlinear system ; Nonlinear optics ; Photonics ; Deep learning ; Field (mathematics) ; Raman scattering ; Scattering ; Electromagnetic field 2D Materials and Applications ; Mechanical and Optical Resonators ; Strong Light-Matter Interactions
Authors: www.rasitdinc.com
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Machine Learning: The Catalyst Accelerating Small Molecule Drug Discovery Machine Learning: The Catalyst Accelerating Small Molecule Drug Discovery The traditional process of small molecule drug discovery is notoriously challe... Small molecule ; Drug discovery ; Catalysis ; Process (computing) ; Molecule Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Svitlana Ponomarova | Oleksandr Ponomarov | Yurii Koval
Source: ArXiv.org (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Predict the Curie Temperature of Fe- and Pt-Based Alloys Various techniques can be employed to determine the temperature of magnetic transformation, whether it be the Curie or Neel temperature. The standard procedure typically involves creating alloys with defined compositions and performing measurements experimentally. Alternatively, these temperatures can be predicted based on a material known physical and chemical properties prior to experiments. We identified an optimal feature set and selected the most effective algorithm. Our findings show that the Voting Ensemble model, when combined with Monte Carlo cross-validation, achieves the highest prediction accuracy. The normalized root mean squared error serves as the primary performance metric. For implementation, we utilize the Azure Machine Learning framework for its robust computational and integration capabilities. This approach offers an efficient and reliable strategy for designing and predicting the Curie temperature of ternary alloys. The paper also highlights potential applications of the model and its extensions for other systems. Curie temperature ; Ternary operation ; Monte Carlo method ; Mean squared error ; Feature (linguistics) ; Néel temperature ; Root mean square ; Set (abstract data type) Machine Learning in Materials Science ; Magnetic Properties and Applications ; X-ray Diffraction in Crystallography
Authors: Ye min Thant | Methawee Nukunudompanich | Chu‐Chen Chueh | Manabu Ihara | Sergei Manzhos
Source: Artificial Intelligence Chemistry (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a digital computer is unwieldy (remote locations; small, mobile, and autonomous devices, extreme conditions, etc.). Neural networks (NN) implemented in such circuits, however, must contend with circuit noise and the non-uniform shapes of the neuron activation function (NAF) due to the dispersion of performance characteristics of circuit elements (such as transistors or diodes implementing the neurons). We present a computational study of the impact of circuit noise and NAF inhomogeneity in regression problems as a function of NN architecture and training regimes. We focus on one application that requires high-throughput ML: materials informatics, using as representative problem ML of formation energies vs. lowest-energy isomer of peri-condensed hydrocarbons, formation energies and band gaps of double perovskites, and zero point vibrational energies of molecules from QM9 dataset. We show that in these applications, NNs generally possess low noise tolerance with the model accuracy rapidly degrading with noise level. Single-hidden layer NNs, and NNs with larger-than-optimal sizes are somewhat more noise-tolerant. Models that show less overfitting (not necessarily the lowest test set error) are more noise-tolerant. Importantly, we demonstrate that the effect of activation function inhomogeneity can be palliated by retraining the NN using practically realized shapes of NAFs. Overfitting ; Artificial neural network ; Noise (video) ; Activation function ; Function (biology) ; Electronic circuit ; Set (abstract data type) ; Transistor Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Jiaqi Liu | Daiheng Zhang | Lixin Yang | F. H. Liu | Tongtong Shi | Yujia Wang | Yun‐Long Tang | Yin‐Lian Zhu | Xiuliang Ma
Source: Chemical Engineering Journal (article, 2025-09-10)
Field: Chemistry — Electrochemistry

Machine learning-guided discovery and stabilization of anomalous Ni4+ oxides with superior electrocatalysis for flow batteries Electrocatalyst ; Vanadium ; Oxide ; Metastability ; Transition metal ; Electrode ; Flow battery ; Kinetics ; Kinetic energy Electrochemical Analysis and Applications ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Yinzhou Liu | Weidong Zheng | Haoqiang Ai | Lin Cheng | Ruiqiang Guo | Xiaohan Song
Source: ACS Applied Materials & Interfaces (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Machine Learning for Performance Prediction and Optimization of Polymer Composites: Unveiling the Dominant Role of Thermally Conductive Pathways Polymer composites with multifiller systems have gained significant attention due to their potential to enhance both thermal conductivity (TC) and mechanical properties. However, traditional methods for predicting composite properties often struggle to account for the complex effects of filler distribution and thermally conductive pathways formation, particularly at varying filler volume fractions. In this study, we developed a novel dense sphere-packing algorithm to construct geometric models of multifiller composites, generating a database of 1024 representative volume element (RVE) models using COMSOL. Random Forest Regression (RFR) and Convolutional Neural Network (CNN) models were trained to predict TC and Young's modulus (<i>E</i>). Recognizing the limitation of traditional machine learning algorithms in capturing thermally conductive pathways, we defined a series of descriptors related to it, which improved prediction accuracy. Furthermore, a transformer-based generative model, along with an auxiliary algorithm, was employed to generate RVE structures with thermally conductive pathways at low total filler volume fractions (vol). We found that at low vol value, the presence of thermally conductive pathways markedly enhanced TC, while their influence on <i>E</i> remains negligible. This combined strategy of traditional machine learning, deep learning, and generative modeling offers an efficient and accurate framework for predicting and designing polymer composites, offering valuable insights for the development of next-generation composite materials that are optimized for both thermal and mechanical performance. Electrical conductor ; Composite number ; Convolutional neural network ; Filler (materials) ; Polymer ; Representative elementary volume ; Artificial neural network ; Support vector machine ; Volume (thermodynamics) Thermal properties of materials ; Machine Learning in Materials Science ; Composite Material Mechanics