jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ignacio F. Graña | Savvas Varsamopoulos | T. Ando | Hiroyuki Maeshima | Nobuyuki Matsuzawa
Source: ArXiv.org (preprint, 2025-03-12)
Field: Materials Science — Materials Chemistry

Materials Discovery With Quantum-Enhanced Machine Learning Algorithms Materials discovery is a computationally intensive process that requires exploring vast chemical spaces to identify promising candidates with desirable properties. In this work, we propose using quantum-enhanced machine learning algorithms following the extremal learning framework to predict novel heteroacene structures with low hole reorganization energy $λ$, a key property for organic semiconductors. We leverage chemical data generated in a previous large-scale virtual screening to construct three initial training datasets containing 54, 99 and 119 molecules encoded using $N=7,16$ and 22 bits, respectively. Furthermore, a sequential learning process is employed to augment the initial training data with compounds predicted by the algorithms through iterative retraining. Both algorithms are able to successfully extrapolate to heteroacene structures with lower $λ$ than in the initial dataset, demonstrating good generalization capabilities even when the amount of initial data is limited. We observe an improvement in the quality of the predicted compounds as the number of encoding bits $N$ increases, which offers an exciting prospect for applying the algorithms to richer chemical spaces that require larger values of $N$ and hence, in perspective, larger quantum circuits to deploy the proposed quantum-enhanced protocols. Leverage (statistics) ; Generalization ; Key (lock) ; Property (philosophy) ; Process (computing) ; Construct (python library) ; Encoding (memory) ; Coding (social sciences) ; Energy (signal processing) Machine Learning in Materials Science
Authors: Yusuke Hashimoto | Xue Jia | Hao Li | Takaaki Tomai
Source: ArXiv.org (preprint, 2025-03-10)
Field: Materials Science — Materials Chemistry

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The data used in MI are derived from both computational and experimental studies; however, their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that is trained on the experimental dataset to the compositional data stored in the computational database. In this study, we use the obtained datasets to construct materials maps, which visualize the relationships between material properties and structural features, aiming to support experimental researchers. The materials map is constructed using the MatDeepLearn (MDL) framework, which implements materials property prediction using graph-based representations of material structure and deep learning modeling. Through statistical analysis, we find that the MDL framework using the message passing neural network (MPNN) architecture efficiently extracts features reflecting the structural complexity of materials. Moreover, we find that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We attribute this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map. Materials informatics ; Structuring ; Artificial neural network ; Property (philosophy) ; Construct (python library) ; Computational model ; Predictive modelling ; Deep learning ; Computational complexity theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Anton Charkin-Gorbulin | Artem Kokorin | Huziel E. Sauceda | Stefan Chmiela | Claudio Quarti | David Beljonne | Alexandre Tkatchenko | Igor Poltavsky
Source: Machine Learning Science and Technology (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Atomic orbits in molecules and materials for improving machine learning force fields Abstract The accurate representation of atoms within their environment forms the backbone of any reliable machine learning force field (MLFF). While modern MLFFs treat atoms of the same type as indistinguishable, their identities can be further resolved by accounting for the composition of their chemical environment. This can improve the parameterization of the MLFF model in chemically diverse systems. In this work, we introduce a novel, data-driven approach designed to find permutation symmetries in isolated and periodic systems, delivering key insights that enable the identification of atomic ‘orbits’, atoms that share consistent chemical and structural environments throughout the dataset. We demonstrate the effectiveness of the orbit representation by incorporating it into the kernel-based symmetric gradient-domain ML (sGDML) model and the equivariant message-passing neural network, MACE. For sGDML, trained on ethanol, 1,8-naphthyridine, D-alanine, and D-histidine adsorbed on graphene, we establish a strong correlation between force prediction accuracy and chemical diversity, quantified by orbit count. The results for the Ac-Phe-Ala5-Lys molecule further underscore the critical role of orbits in force reconstruction across various MLFF architectures. Incorporating orbits into MACE enables us to reduce the model size by an order of magnitude while preserving predictive accuracy, as demonstrated for the CsPbI 3 perovskite slab and graphene with a pyridinic-N defect. Overall, our approach provides a scalable and efficient solution for modeling complex chemical systems with state-of-the-art MLFFs. Molecule ; Nanotechnology ; Atomic force microscopy ; Computer science ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Metin Gürü | Jan Bohlen | Roland C. Aydin | Noomane Ben Khalifa
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Machine Learning Pipeline for Structure-Property Modeling in Mg-Alloys Using Microstructure and Texture Descriptors Pipeline (software) ; Microstructure ; Texture (cosmology) ; Property (philosophy) ; Materials science ; Artificial intelligence ; Metallurgy ; Computer science ; Mechanical engineering ; Engineering ; Image (mathematics) Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Microstructure and mechanical properties
Authors: Sandeep Gupta | Budesh Kanwer | Udit Mamodiya | Saurabh Shandilya | Deepshikha Bhatia | Nithesh Naik
Source: Journal of Applied Science and Technology Trends (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning Framework for Virtual Screening and Multi-Objective Optimization of Polymer Nanocomposites with Tailored Multifunctional Properties The rational design of polymer nanocomposites with tailored multifunctional properties remains challenging due to complex multi-scale physics and the limitations of traditional empirical approaches, which cannot adequately capture the combinatorial interactions between polymer matrices, nanofillers, and processing conditions. We present a new computational framework for cost-effective virtual screening and optimization of polymer nanocomposites with physically consistent prediction in this series. In a physics-informed neural network, we suggest a combination of the quantum mechanical response, as well as standard molecular dynamics and thermodynamic data. (1) Physics-aware loss functions that incorporate conservation policies and thermodynamic constraints; (2) multiscale descriptor integration of quantum to macroscales; (3) ensemble learning is supplemented by tools to distinguish epistemic and aleatoric uncertainty; and (4) NSGA-III assisted multi-objective optimization coupled with adaptive reference point generation. The neural network architecture consists of multi-branch pathways with 5 hidden layers (256, 512, 512, 256, 128 neurons) using Leaky ReLU activation functions, trained on 23,847 polymer nanocomposite formulations using Adam optimizer (learning rate: 0.001, batch size: 64) with cosine annealing scheduling. The framework achieves prediction accuracies of R² &gt; 0.94 for mechanical properties, R² &gt; 0.91 for thermal characteristics, and R² &gt; 0.88 for electrical conductivity, representing 15-25% improvements over conventional machine learning methods. Virtual screening of 3.2 million candidate formulations identified 1,847 compositions with superior performance. Our NSGA-III optimization identifies Pareto-optimal solutions with 34% higher multifunctional performance than conventional approaches, while reducing experimental validation requirements by 82%. Experimental validation of 127 compositions confirms 89% prediction accuracy within confidence intervals (95% confidence intervals: ±8.3% for mechanical, ±9.1% for thermal, ±11.2% for electrical properties). The present physics-informed machine learning approach enables computational materials design with accounting for the most relevant physical laws and data-driven techniques to discover optimal high-performance polymer nanocomposites yet offers a robust uncertainty quantification to inform risk-conscious design decisions. Artificial neural network ; Simulated annealing ; Nanocomposite ; Polymer ; Polymer nanocomposite ; Deep learning ; Optimization problem ; Thermal Machine Learning in Materials Science
Authors: Wei‐Jiang Zhao | Haiyang Huo | Zhong‐Yuan Lu | Zhao‐Yan Sun
Source: Chinese Journal of Polymer Science (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network Artificial neural network ; Dynamics (music) ; Polymer ; Cloud computing ; Point (geometry) ; Materials science ; Cloud point ; Molecular dynamics ; Linear polymer ; Biological system ; Statistical physics ; Chemical physics ; Computer science ; Chemical engineering ; Artificial intelligence ; Mathematics ; Chemistry ; Engineering ; Physics ; Composite material ; Computational chemistry ; Geometry ; Biology Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Weiyang Li | Wenya Li | Jing Hu | Ping Liu | Haoyu Shi | Jiyang Yu
Source: Journal of Nuclear Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Investigating the corrosion transition mechanisms of zirconium alloys and the potential applications of machine learning in predictive modeling Corrosion ; Zirconium alloy ; Zirconium ; Alloy ; Oxide ; Phase transition ; Nuclear fuel ; Nuclear reactor Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear reactor physics and engineering
Authors: Shuting Xiang | Marc R. Knecht | Anatoly I. Frenkel
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted X-ray absorption analysis of bimetallic catalysts Bimetallic nanoparticles have attracted increasing scientific and technological interest as modules for creating nanoscale materials with unique magnetic, electronic, and chemical properties. The properties of bimetallic NPs are functions of composition, size, shape, stoichiometry, and possibly internal structure (alloy or core-shell-like). Bimetallic nanoparticles have superior properties for catalytic applications. However, it is challenging to understand and control the size, shape, composition, and activity of these nanomaterials. The internal atomic structure of these materials needs to be precisely characterized to understand the structure-function relationship. X-ray absorption fine structure (XAFS) spectroscopy has been a premier tool for analyzing the compositional and structural motifs in bimetallic nanoparticles for several decades. In this review, we discuss the limitations in the ability of XAFS to detect catalytically relevant surface species and focus on recent developments in machine learning-assisted XAFS analysis aimed at overcoming these limitations. Bimetallic strip ; Materials science ; Catalysis ; Absorption (acoustics) ; X-ray ; Nanotechnology ; Chemical engineering ; Optics ; Chemistry ; Composite material ; Engineering ; Physics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Felix Sieber‐Schäfer | Adrian P. E. Kromer | Müge Molbay | Simone Pinto Carneiro | M. Jiang | Anny Nguyen | Joschka T. Müller | Johan Farfan Benito | Olivia M. Merkel
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning on a Small Orthogonal Polymer Library Reveals Functional Insights and Optimizes Pbae Copolymer Synthesis and Performance Copolymer ; Computer science ; Polymer ; Information retrieval ; Materials science ; Composite material Machine Learning in Materials Science
Authors: Xiaohe Wang | Junqing Yang | Gazi Hao | Yubing Hu | Xiaojun Feng | Wei Jiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Thermal Decomposition Mechanism of Tkx-50 Explored by Neural Network Based Molecular Dynamics Simulation Mechanism (biology) ; Molecular dynamics ; Decomposition ; Thermal decomposition ; Artificial neural network ; Dynamics (music) ; Thermal ; Computer science ; Biological system ; Chemistry ; Chemical physics ; Computational chemistry ; Artificial intelligence ; Thermodynamics ; Physics ; Organic chemistry Thermal and Kinetic Analysis
Authors: Truong Nhut Huynh | Hatim Raji | Soheil Saedi | Kim‐Doang Nguyen
Source: Computational Materials Science (article, 2025-06-22)
Field: Materials Science — Materials Chemistry

Deep learning framework for accurate prediction of phase transformation in multicomponent shape memory alloys Shape-memory alloy ; Transformation (genetics) ; Materials science ; Phase (matter) ; Artificial intelligence ; Computer science ; Thermodynamics ; Metallurgy ; Chemistry ; Physics Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience
Authors: Jacopo Moi | Davide Spallarossa | Stefano Bonetti | Raffaella Burioni | Guido Caldarelli
Source: ArXiv.org (preprint, 2025-02-13)
Field: Materials Science — Materials Chemistry

The quest for new materials: the network theory and machine learning perspectives Understanding and predicting the emergence of novel materials is a fundamental challenge in condensed matter physics, materials science and technology. With the rapid growth of materials databases in both size and reliability, the challenge shifts from data collection to efficient exploration of this vast and complex space. A key strategy lies in a smart use of descriptors at multiple scales, ranging from atomic arrangements to macroscopic properties, to represent materials in high-dimensional abstract spaces. Network theory provides a powerful framework to structure and analyze these relationships, capturing hidden patterns and guiding discovery. Machine Learning complements this approach by enabling predictive modeling, dimensionality reduction, and the identification of promising material candidates. By integrating network-based methods with Machine Learning techniques, researchers can construct, analyze, and efficiently navigate the material space, uncovering novel materials with tailored properties. This review explores the synergy between network theory and ML, highlighting their role in accelerating materials discovery through a systematic and interpretable approach. Computer science ; Cognitive science ; Sociology ; Artificial intelligence ; Epistemology ; Mathematics education ; Psychology ; Philosophy Machine Learning in Materials Science
Authors: Dongling Yu | Liangyu Zhu | Xiaobin Cui | Ning Xiang | Lifang Jiao | Dahai Liao
Source: Journal of Electronic Materials (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

A Study of the Deep Learning Potential of Silicon Nitride Materials Under High Temperatures Silicon nitride ; Silicon ; Solid-state physics ; Materials science ; Engineering physics ; Nanotechnology ; Metallurgy ; Engineering ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Thermography and Photoacoustic Techniques
Authors: Anuj Kumar | Tarek Echekki
Source: Data-Centric Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physics-constrained machine learning for reduced composition space chemical kinetics Abstract Modeling detailed chemical kinetics is a primary challenge in combustion simulations. We present a novel framework to enforce physical constraints, specifically total mass and elemental conservation, during the reaction of ML models’ training for the reduced composition space chemical kinetics of large chemical mechanisms in combustion. In these models, the transport equations for a subset of representative species are solved with the ML approaches, while the remaining nonrepresentative species are “recovered” with a separate artificial neural network trained on data. Given the strong correlation between full and reduced solution vectors, our method utilizes a small neural network to establish an accurate and physically consistent mapping. By leveraging this mapping, we enforce physical constraints in the training process of the ML model for reduced composition space chemical kinetics. The framework is demonstrated here for methane, CH 4 , and oxidation. The resulting solution vectors from our deep operator networks (DeepONet)-based approach are accurate and align more consistently with physical laws. Kinetics ; Space (punctuation) ; Composition (language) ; Physics ; Statistical physics ; Computer science ; Classical mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Heat transfer and supercritical fluids
Authors: Xiang Huang | Shaobo Song | Y Chen | Zongze Lv | Xingyi Huang | Shenghong Ju
Source: ACS Applied Materials & Interfaces (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Efficient Discovery and Rational Design of Thermally Conductive Polymers As electronic chip technology advances toward miniaturization, integration, and high-frequency capabilities, along with the continuous increase in device power density, the high thermal conductivity (TC) of polymers becomes increasingly important in thermal management for flexible electronics and optoelectronics. However, the poor thermal conduction of polymers is always an obstacle. To this end, this paper proposed a novel machine learning-assisted framework that simultaneously realizes the rapid screening and rational design of polymers with high thermal conductivity (TC > 0.40 W m<sup>-1</sup> K<sup>-1</sup>). The deep neural network model was trained to map the relationships between the microstructures and TCs of polymers, and it enabled high-throughput screening for highly thermally conductive candidates in the predefined exploration space. Further, we extracted some promising fragments from the discovered polymers and designed a series of innovative, highly thermally conductive structures by combining the Monte Carlo tree search algorithm and molecular generation rules. Ultimately, we demonstrated the beneficial impact of chain stiffness on TC, relating it to the chain conformation and the distribution of bond strengths in the amorphous systems. This two-pronged strategy, transitioning from discovery toward design, provides a rational and efficient avenue for accelerating the exploitation of polymers with high TC and other desired properties. Rational design ; Polymer ; Electrical conductor ; Thermal conductivity ; Thermal conduction ; Conductive polymer Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Kiran Nyaupane | Poshan Belbase | Narayan Prasad Adhikari
Source: AIP Advances (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Prediction of Tc in superconducting materials: A machine learning approach Knowing whether a material becomes a superconductor or not is of utmost importance, given the interesting physics behind it and also from a practical point of view. Similarly, predicting the critical temperature of a superconductor is also important. In this work, we carried out a machine learning approach to predict the critical temperature of a superconductor. Different machine learning methods, namely elastic net regressor, linear regressor, decision tree regressor, random forest regressor, multilayer perceptron regressor, and extreme gradient boosting (XGB) regressor, are applied to two different datasets containing physical properties and chemical composition of 21 263 superconductors. The primary objective was to predict the critical temperature of these superconductors by leveraging the aforementioned algorithms and their underlying statistical methodologies. Among the models, the random forest regressor demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 9.35 K on the dataset containing chemical properties. This was closely followed by the XGB regressor, which attained an RMSE of 9.97 K on the physical dataset, with other algorithms exhibiting relatively higher RMSE values. The critical temperatures are estimated based on the chemical composition and physical properties, such as thermal conductivity, atomic radius, valence, electron affinity, and atomic mass, of the materials considered independently. Multilayer perceptron ; Mean squared error ; Superconductivity ; Gradient boosting ; Decision tree ; Random forest ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Materials Characterization Techniques
Authors: Jingwei Zhang | Siyu Chen | Haisheng Ren | Zerong Li
Source: Industrial & Engineering Chemistry Research (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Enhancing the Predictive Kinetics of Intramolecular H-Migration Reactions of Ether Peroxy Radicals by Integrating Machine Learning with Quantum Chemistry: A Comparative Study of Generic Rate Rules and Machine Learning Techniques Accurate reaction rate constants are essential for automated mechanism generators, yet high-level quantum chemistry methods remain computationally prohibitive. This study addresses this challenge for intramolecular H-migration in ether peroxy radicals, which are important in low-temperature combustion mechanisms. We investigated 102 reactions using M06-2X/6-311++G(2df,2p) calculations and canonical transition state theory. While we developed generic rate rules for 42 reaction classes, they proved inadequate for accurate predictions. Therefore, we built machine learning models with reaction-specific descriptors to predict the rate constants. We compared tree-based algorithms (eXtreme Gradient Boosting and Light Gradient Boosting Machine) against nontree-based algorithms (Multivariate Linear Regression and Gaussian Process Regression). The Light Gradient Boosting Machine emerged as superior with a root mean square error of 0.396 in ln k values. Feature importance and SHapley Additive exPlanation analysis revealed that structural characteristics and energetic properties were the most influential factors affecting reaction rates. Our systematic comparison demonstrates that machine learning methods significantly outperform generic rate rules for kinetic modeling of ether peroxy radical H-migration reactions. Radical ; Kinetics ; Intramolecular force ; Chemistry ; Quantum chemistry ; Quantum ; Computer science ; Organocatalysis ; Reaction mechanism ; Organic chemistry ; Catalysis ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Free Radicals and Antioxidants