jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anuja Sharma | Owais Ahmad | Somnath Bhowmick | Pritam Chakraborty
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning to Accelerate Full-Field Crystal Plasticity Simulations Over the past century, a lot of research has been done to form innovative materials that can cater to serve different purposes in industries such as aerospace, automotive, marine etc. However, metals still hold a major part of the products developed in these industries and hence, it is important to study the behavior of metals both macroscopically as well as microscopically. Finite Element Method using Crystal Plasticity constitutive model (CPFEM) is one of the popular methods that enables investigating the material behavior at micro level accurately but is computationally expensive. Recent studies suggest that ML (Machine learning) techniques can significantly reduce the computational time when ensembled with methodologies that evaluate the behavior of materials (e.g., phase-field methods, CPFEM, Peridynamics). This paper highlights the ability of ML based full-field simulations of polycrystalline alloys to reduce computational time of CPFEM simulations. A convolution-based LSTM (ConvLSTM) model is used and trained using few time steps of full-field response from CPFEM simulations. Crystal plasticity ; Plasticity ; Field (mathematics) ; Materials science ; Computer science ; Mechanical engineering ; Composite material ; Engineering ; Mathematics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Anh D. Phan | Ngo T. Que | Nguyen T. T. Duyen | Phan Thanh Viet | Quach K. Quang | Baicheng Mei
Source: ArXiv.org (preprint, 2025-07-08)
Field: Materials Science — Materials Chemistry

Bridging Machine Learning and Glassy Dynamics Theory for Predictive Polymer Modeling Understanding and predicting the glassy dynamics of polymers remain fundamental challenges in soft matter physics. While the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory has been successful in describing relaxation dynamics, its practical application to polymers depends on a thermal mapping to connect theory with experiment, which in turn requires detailed thermodynamic data. Such data may not be available for chemically complex or newly designed polymers. In this work, we propose a simple approach that integrates machine learning-predicted glass transition temperatures (Tg) with a simplified thermal mapping based on an effective thermal expansion coefficient to overcome these limitations. This approach can provide quantitatively accurate predictions of relaxation dynamics across a broad range of polymers. Rather than replacing the original thermal mapping, our method complements it by trading formal rigor for computational efficiency and broader applicability in high-throughput screening and materials with limited available data. Moreover, we introduce a physically motivated modification to the thermal mapping that resolves discrepancies in the description of low-Tg polymers. Our results establish a generalizable approach for predictive modeling of glassy polymer dynamics and point toward new directions for theory-guided materials discovery. Bridging (networking) ; Thermal ; Nonlinear system ; Relaxation (psychology) ; Thermal fluctuations ; Glass transition ; Langevin dynamics ; Range (aeronautics) Material Dynamics and Properties ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Kun Wang | Ren Lin | Zhongqiu Liu | Chun Wang | Hao Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Construction of a Neural Network Potential for Fe-Cr-Ni Alloys and its Application in Uniaxial Tension Simulations Uniaxial tension ; Tension (geology) ; Materials science ; Artificial neural network ; Composite material ; Metallurgy ; Computer science ; Artificial intelligence ; Ultimate tensile strength Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Engineering Diagnostics and Reliability
Authors: Dibyendu Maity | Suman Chakrabarty
Source: Research Square (preprint, 2025-07-23)
Field: Materials Science — Materials Chemistry

Challenges in Transferable Prediction of Solvation Free Energy: A Comparative Analysis of Molecular Representations and Machine Learning Methods Solvation ; Energy (signal processing) ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Mathematics ; Molecule ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Joung Sik Suh | Jae Hoon Jang | Byeong‐Chan Suh | Jae‐Yeon Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Reverse Design of Mg-Zn-Mn-Sr-Ca Alloys for Biodegradable Implants by Interpretable Machine Learning and Genetic Algorithm Genetic algorithm ; Computer science ; Materials science ; Algorithm ; Artificial intelligence ; Machine learning Magnesium Alloys: Properties and Applications ; Surface Treatment and Coatings ; Metal and Thin Film Mechanics
Authors: J.L. Valverde | Weidong Zhang | Víctor R. Ferro | Anne Giroir-Fendler
Source: Discover Catalysis. (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Machine learning-guided catalyst design: optimization by combining targeted screening, intrinsic properties and techno-economic criteria for volatile organic compounds (VOC’s) oxidation This work uses machine learning (ML) procedures to investigate the effect of the properties of different cobalt-based catalysts on the oxidation of toluene and propane. The hydrocarbon conversion was modeled using 600 ANNs and 8 supervised regression algorithms. An optimization framework for the input variables was subsequently developed, using the best neural networks to minimize both the catalyst costs and the energy consumption for reaching 97.5% conversion. This optimization analysis showed that for toluene and propane oxidation, the best result was coincidental with that reported elsewhere and with the commercial catalyst, respectively. As expected, the optimization analysis selected the cheapest catalyst (practically negligible influence of the energy cost). The optimization analysis in terms of physical properties of the catalyst showed that the best results obtained with the toluene oxidation would approximately correspond to catalyst Co-C2O4 while those obtained with the propane oxidation did not result to be conclusive. Catalysis ; Propane ; Toluene ; Hydrocarbon ; Work (physics) ; Artificial neural network Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Maxwell C. Schulze | Nina Prakash | Kevin L. Gering | Andrew M. Colclasure | Peter J. Weddle
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals Electrolyte ; Anode ; Electrochemistry ; Computer science ; Materials science ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Ryan J. Miller | Alexander E. Dashuta | Brayden Rudisill | David L. Van Vranken | Pierre Baldi
Source: Journal of the American Chemical Society (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Mechanism-Aware Deep Learning for Polar Reaction Prediction Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. Yet reaction prediction remains a complex problem that is both time-consuming and resource-intensive for chemists to solve. Deep learning offers an appealing solution by enabling high-throughput prediction, but most existing models are trained on the US Patent Office data set and treat reactions as recipes or overall transformations─mapping reactants directly to products with limited mechanistic insight. To address this, we introduce PMechRP (Polar Mechanistic Reaction Predictor), trained on the PMechDB data set of polar elementary steps that capture electron flow and mechanistic detail. To broaden coverage and improve generalization, we augment PMechDB with combinatorially generated reactions and train models spanning transformer, graph, and two-stage Siamese architectures. In addition to reaction prediction models, we also develop ArrowFinder, a new model that directly predicts arrow-pushing mechanisms for a set of reactants and products. Our best-performing approach is a hybrid pipeline that combines an ensemble of Chemformer models with a two-stage Siamese framework, leveraging the accuracy of transformers while filtering away "alchemical" products using the two-step network and generating mechanistic annotations using ArrowFinder. This approach achieves strong predictive accuracy while also providing interpretable predictions. We evaluate performance across multiple benchmarks: PMechDB test splits, a curated USPTO subset from the Open Reaction Database, and a human benchmark of mechanistic pathways from an intermediate-level textbook. Deep learning ; Benchmark (surveying) ; Pipeline (software) ; Training set ; Set (abstract data type) ; Test set ; Data set ; Labeled data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Paolo Lazzaroni | Shubham Sharma | Mariana Rossi
Source: ArXiv.org (preprint, 2025-10-06)
Field: Materials Science — Materials Chemistry

Investigating Anharmonicities in Polarization-Orientation Raman Spectra of Acene Crystals with Machine Learning We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasi-harmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations. Anharmonicity ; Raman spectroscopy ; Polarizability ; Raman scattering ; Spectral line ; Molecular dynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Tonglei Li | Venkata S. Chelagamsetty | Nicolas J. Huls | R. Jordan
Source: Journal of Chemical Theory and Computation (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Manifold Kernelization of Molecular Surface to Encode Quantum Information of Electronic Attributes for Machine Learning A novel concept of encoding electronic quantities on a molecular surface is developed by unsupervised kernel learning. Through optimization of the hyperparameters of Spectral Mixture (SM) kernel functions in conducting Sparse Gaussian Process (SGP) regression of electronic attributes on a surface manifold, the resultant covariance matrix, or kernel, captures the mutual relationships among the electronic quantities as well as the topology of the molecular surface. As such, a kernel, coined MKMS (Manifold Kernelization of Molecular Surface), can be treated as a symmetric positive definite (SPD) matrix to represent a molecule for machine learning. A proper neural network model was implemented to utilize SPD matrices and preserve their collected Riemannian topology for predicting the molecular properties. The prediction results of two solubility data sets support promising potentials of using MKMS to encode quantum information on a molecule and enable machine learning applications. Kernelization ; ENCODE ; Kernel (algebra) ; Topology (electrical circuits) ; Covariance matrix ; Mutual information ; Gaussian process ; Surface (topology) ; Artificial neural network Machine Learning in Materials Science ; Neural Networks and Applications ; Gaussian Processes and Bayesian Inference
Authors: Rinkumoni Chaliha | Manish Kumar Kothakonda | Cheng‐Wei Lee | Jeffrey Law | Qian Yang | Svilen Bobev | Prashun Gorai
Source: ChemRxiv (preprint, 2025-06-10)
Field: Materials Science — Materials Chemistry

Charting the Chemical Space of Zintl Phases with Graph Neural Networks and Bonding Insights A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90,000 hypothetical Zintl phases and accurately discover 1809 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2X more accurate than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics. Chemical space ; Graph ; Space (punctuation) ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Oleksandr Stroyuk | Oleksandra Raievska | Dietrich R T Zahn | Christoph J. Brabec
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of multinary chalcogenide quantum dots: combining aqueous chemistry with high-throughput experimentation and machine learning Multinary chalcogenide quantum dots (MCQDs) exhibit unprecedented variability in composition and properties, size tunability, and high tolerance to multiple alloying, doping, and deviations from stoichiometry. This variability enables the synthesis of hundreds of thousands of MCQDs, characterized by a wide range of composition- and size-dependent spectral and photophysical properties, with a high potential for optoelectronic applications. At that, the whole compositional richness of MCQDs can be readily accessed using sustainable aqueous chemistry. The present Perspective focuses on the challenges of navigating the vast compositional space of MCQDs to discover new optoelectronic materials for the absorption, emission, and conversion of light. We argue that the exploration of the compositional versatility of MCQDs requires accelerated research, going beyond the conventional intuition-driven experiments. The acceleration can be achieved by high-throughput parallelized experimentation that yields extensive datasets and enables machine-learning-driven data analysis and automation of the targeted discovery of new MCQDs. Chalcogenide ; Quantum dot ; Quantum ; Range (aeronautics) ; Aqueous solution Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Jihang Xue | Tianjun Luo | Yongquan Jiang | Yan Yang | Kuanpin Gong | Zigang Deng | Qingguo Feng | Weihua Zhang
Source: Journal of Applied Crystallography (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

TransMagNet: prediction of crystal system and space group for crystalline materials based on composition using deep learning The prediction of crystal systems and space groups has been widely used in the estimation of crystalline material properties and structure prediction. Previous research regarding structure determination methods based on X-ray diffraction experiments and density functional theory has attained remarkable efficacy and performance, but these approaches are not applicable to large-scale screening. There are also machine learning models that use Magpie descriptors for space group prediction; for example, that proposed by Liang et al. [ Phys. Rev. Mater. (2020), 4 , 123802] exhibited prediction accuracies ranging between 0.638 and 0.907 for different types of crystals. Here we put forward a branch network model, named TransMagNet, which is based on transformer encoders [Vaswani et al. (2017). Advances in neural information processing systems 30 , pp. 5998–6008] and Magpie linear layers [Ward et al. (2016). npj Comput. Mater. 2 , 16028], to predict the crystal systems and space groups of materials by merely relying on compositional data. Benchmarking on the Materials Project dataset demonstrates a significant performance improvement in space group classification of our model over previous models, with an accuracy spanning from 0.811 to 0.981 and a maximum improvement in prediction accuracy of 6.5%. The model also achieves a significant performance improvement in crystal system prediction, with an accuracy of 0.854. Benchmarking ; Artificial neural network ; Encoder ; Group (periodic table) ; Space (punctuation) ; Crystal (programming language) ; Deep learning ; Diffraction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Geoffrey R. Weal | Maryam Nurhuda | Justin M. Hodgkiss | Paul Hume | Daniel M. Packwood
Source: ChemRxiv (preprint, 2025-03-13)
Field: Materials Science — Polymers and Plastics

Graph neural networks to predict atomic transition charges and exciton couplings in organic semiconductors Exciton couplings between molecules in organic semiconductors are important parameters for simulating exciton diffusion, however they are time-consuming to compute from first-principles. Previous works have developed machine-learned models to predict exciton couplings, however these models have mostly been restricted to specific molecule and cannot generalize over databases of organic materials. In this paper, we present a graph neural network (GNN) that can predict exciton couplings between organic molecules by using atomic transition charges as an intermediary. Our GNN is shown to predict exciton coupling between important fused-ring electron acceptors (FREAs), as well as many other molecules found in the Cambridge Crystallographic Data Centre crystal database. We also show that these predicted couplings can be used for accurate simulations of exciton diffusion. This work therefore overcomes the key limitation of previous machine-learned models for exciton couplings, thereby bringing us closer to the possibility of performing high-throughput virtual screening of organic materials for photovoltaic applications. Exciton ; Semiconductor ; Organic semiconductor ; Graph ; Artificial neural network ; Condensed matter physics ; Materials science ; Physics ; Statistical physics ; Computer science ; Quantum mechanics ; Artificial intelligence ; Theoretical computer science Conducting polymers and applications ; Machine Learning in Materials Science ; Organic and Molecular Conductors Research
Authors: Lili Liang | Yujie Yang | Yutong Gong | Zhiqi Wang | Ke Li | Jianan Su | Jinming Wang | Junjie Wang
Source: Journal of Advanced Ceramics (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Machine learning-guided discovery of stable low-work-function perovskite oxides for advanced catalysis and energy technologies Perovskites with a low work function find extensive applications across various fields. However, the diverse nature of perovskite compounds presents challenges for conventional trial-and-error material screening methods. In this study, we introduced an effective target-driven approach that integrates machine learning (ML) and density functional theory (DFT) calculations. Initially, we explored an exhaustive chemical space encompassing ABO<sub>3</sub>-type single and A<sub>2</sub>BB'O<sub>6</sub>-type double perovskite oxides to identify stable compounds with work functions of AO-terminated (001) surfaces below 2.5 eV using a trained ML model. Subsequently, employing high-precision calculations, we successfully narrowed down the selection to 27 stable perovskite oxides from the initial pool of 23822 candidate materials. Two promising compounds, Ba<sub>2</sub>TiWO<sub>8</sub><sub> </sub>and Ba<sub>2</sub>FeMoO<sub>6</sub>, were then successfully synthesized and characterized experimentally. Furthermore, the first-time synthesized Ba<sub>2</sub>TiWO<sub>8</sub> was found to exhibit catalytic activities for both NH<sub>3</sub> synthesis and NH<sub>3 </sub>decomposition under mild conditions with Ru loading, suggesting its future application in catalysis. Moreover, as Li-ion battery electrode materials, Ba<sub>2</sub>FeMoO<sub>6</sub> exhibited a long-cycle stability at a current density of 10 A g<sup>–1</sup> (10,000 cycles), unlocking a wealth of possibilities for sustainable electrochemical applications of perovskites. Our work demonstrates the efficacy and efficiency of the ML-assisted method in establishing a reliable structure-property relationship for mapping work functions. Perovskite (structure) ; Work (physics) ; Function (biology) ; Catalysis ; Structural material ; Materials science ; Work function ; Nanotechnology ; Chemical engineering ; Metallurgy ; Engineering ; Mechanical engineering ; Chemistry Machine Learning in Materials Science
Authors: St Mardiana | Arxhel S. F. Nanda | I Made Arcana | Ismunandar Ismunandar | Adroit T. N. Fajar | Grandprix T. M. Kadja
Source: Journal of Engineering and Technological Sciences (review, 2025-07-25)
Field: Chemistry — Inorganic Chemistry

Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review Zeolites are widely applied in various fields owing to their outstanding properties. However, our understanding on the nature of zeolite synthesis is not completed yet due to its high dimensional parameters. Machine learning has the ability to unravel fundamental relationships between complex parameters and predict the possible outcomes; thus, it can potentially reveal the nature of zeolite synthesis. This mini review highlights the current use of machine learning to comprehend the black box issue in zeolite synthesis. Conventional syntheses of zeolite were also elaborated to showcase the gap between traditional methods and machine learning approaches in zeolite synthesis. The future prospects of machine learning applications in zeolite synthesis are also discussed. This mini-review may bring crucial insights on the zeolite synthesis process. Rational design ; Biochemical engineering ; Engineering drawing ; Computer science ; Process engineering ; Artificial intelligence ; Chemical engineering ; Mathematics ; Engineering ; Materials science ; Nanotechnology Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science