jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pallavi Dandekar | Aditya Singh Ambesh | Tuhin Suvra Khan | Shelaka Gupta
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted approximation of descriptors (CO and OH) binding energy on Cu-based bimetallic alloys Data driven machine learning (ML) based methods have the potential to significantly reduce the computational as well as experimental cost for the rapid and high-throughput screening of catalyst materials using binding energy as a descriptor. In this study, a set of eight widely used ML models classified as linear, kernel and tree-based ensemble models were evaluated to predict the binding energy of catalytic descriptors (CO* and OH*) on (111)-terminated Cu<sub>3</sub>M alloy surfaces using the readily available metal properties in the periodic table as features. Among all the models tested, the extreme gradient boosting regressor (xGBR) model showed the best performance with the root mean square errors (RMSEs) of 0.091 eV and 0.196 eV for CO and OH binding energy predictions on (111)-terminated A<sub>3</sub>B alloy surfaces. Moreover, the xGBR model gave the highest <i>R</i><sup>2</sup> scores of 0.970 and 0.890 for CO and OH binding energies. The time taken by the ML predictions for 25 000 fits for each model was varied between 5 and 60 min on a 6 core and 8 GB RAM laptop, which was very negligible as compared to DFT calculations. Our ML model showed remarkable performance for accurately predicting the CO and OH binding energies on a (111)-terminated Cu<sub>3</sub>M alloy with a mean absolute error (MAE) of 0.02 to 0.03 eV compared to DFT calculated values. The ML predicted binding energies can be further used with an <i>ab initio</i> microkinetic model (MKM) to efficiently screen A<sub>3</sub>B-type bimetallic alloys for the formic acid decomposition reaction. Bimetallic strip ; Binding energy ; Energy (signal processing) ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Physical chemistry ; Metallurgy ; Physics ; Atomic physics ; Metal ; Quantum mechanics Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; CO2 Reduction Techniques and Catalysts
Authors: Hussein Ali Kadhim Kyhoiesh | Hassan E. Abd Elsalam | Azal S. Waheeb | Khalid J. Al-Adilee | Munthir A. Abdulhussain | Dakhil N. Taha | Ahmed A. Al-Kubaisi | Islam H. El Azab
Source: Indian Journal of Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

A machine learning study to predict long range order/disoroder of organic semiconductors: a study on maintaining delicate crystallinity balance Crystallinity ; Boosting (machine learning) ; Random forest ; Gradient boosting ; Organic semiconductor ; Thermal ; Balance (ability) ; Embedding Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Thermal properties of materials
Authors: Xin Yu | Peng Liu | Z. P. Xie | Wansheng Mi | Pengyue Gao | Hong Zhao | Jian Lv | Yanchao Wang | Yanming Ma
Source: ArXiv.org (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures Even though thermodynamic energy-based crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through kinetically controlled pathways, creating a critical gap between theoretical predictions and experimental synthesis. Here, we propose a synthesizability-driven CSP framework that integrates symmetry-guided structure derivation with a Wyckoff encode-based machine-learning model, allowing for the efficient localization of subspaces likely to yield highly synthesizable structures. Within the identified promising subspaces, a structure-based synthesizability evaluation model, fine-tuned using recently synthesized structures to enhance predictive accuracy, is employed in conjunction with ab initio calculations to systematically identify synthesizable candidates. The framework successfully reproduces 13 experimentally known XSe (X = Sc, Ti, Mn, Fe, Ni, Cu, Zn) structures, demonstrating its effectiveness in predicting synthesizable structures. Notably, 92,310 structures are filtered from the 554,054 candidates predicted by GNoME, exhibiting great potential for promising synthesizability. Additionally, eight thermodynamically favorable Hf-X-O (X = Ti, V, and Mn) structures have been identified, among which three HfV$_2$O$_7$ candidates exhibit high synthesizability, presenting viable candidates for experimental realization and potentially associated with experimentally observed temperature-induced phase transitions. This work establishes a data-driven paradigm for machine-learning-assisted inorganic materials synthesis, highlighting its potential to bridge the gap between computational predictions and experimental realization while unlocking new opportunities for the targeted discovery of novel functional materials. Bridging (networking) ; Crystal structure prediction ; Metastability ; Ab initio ; Realization (probability) ; Linear subspace ; Work (physics) Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Jing Wei | Jianli Zhao
Source: IET conference proceedings. (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Prediction and optimization of metal alloy strength and wear resistance using machine learning With the continuous development of manufacturing industry, alloying metals that improve metal properties have become a research hotspot in the field of materials science. In order to accurately predict and optimize the strength and wear resista nce of metal alloys, a machine learning based metal alloy performance prediction model was constructed using the random forest algorithm, and an improved Stacking algorithm was used to optimize the model on this basis. The results showed that the optimized Stacking algorithm model controlled the prediction error within 5%, and the machine learning based prediction model achieved a strength prediction accuracy of 93.87% for high entropy alloys. In summary, the use of machine learning for predicting and optimizing the strength and wear resistance of metal alloys has effectively improved prediction accuracy, providing important support and reference for predicting the properties of metal alloys in the manufacturing field. Alloy ; Materials science ; Metallurgy ; Wear resistance ; Metal Metal Alloys Wear and Properties ; Lubricants and Their Additives ; Advanced Machining and Optimization Techniques
Authors: Deng, Yanchen | Zhao, Chendong | Li, Yixuan | Tang, Bijun | Wang, Xinrun | Zhang, Zhonghan | Lu, Yuhao | Yang, Penghui | Huang, Jianguo | Xiao, Yushan | Guan, Cuntai | Liu, Zheng | An, Bo
Source: ArXiv.org (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast alloys. MATAI integrates a curated alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop. The framework estimates key mechanical propertie, sincluding density, yield strength, ultimate tensile strength, and elongation, directly from composition, using multi-task learning and physics-informed inductive biases. Alloy design is framed as a constrained optimization problem and solved using a bi-level approach that combines local search with symbolic constraint programming. We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials, where it rapidly identifies candidates that simultaneously achieve lower density (<4.45 g/cm3), higher strength (>1000 MPa) and appreciable ductility (>5%) through only seven iterations. Experimental validation confirms that MATAI-designed alloys outperform commercial references such as TC4, highlighting the framework's potential to accelerate the discovery of lightweight, high-performance materials under real-world design constraints. Property (philosophy) ; Constraint (computer-aided design) ; Inverse ; Ductility (Earth science) ; Inverse problem ; Key (lock) ; Engineering design process ; Artificial neural network Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Zhuang Zhuo | Ying Li | Wenduo Chen | Hai‐Ming Zhao
Source: Journal of Physics Conference Series (article, 2025-03-01)
Field: Chemistry — Organic Chemistry

Formulation Design of Frontal Polymerization Systems for Epoxy Resins Based on Machine Learning Abstract Frontal polymerization (FP) is a self-propagating reaction, which propagates through a material in a self-sustaining manner, resembling a wave front. It is considered a promising alternative curing strategy for epoxy resins due to its low energy requirements only in the initiation process. However, achieving stable polymerization and controlling material properties remain challenging. We utilize a combination of machine learning and experimental validation to analyze the frontal polymerization (FP) system of epoxy resins and predict their material properties. Our findings indicate that incorporating comonomers with higher radical reactivity or adding appropriate diluents can significantly enhance reactivity and reduce viscosity. When combined with carbon fiber, Bisphenol A diglycidyl ether demonstrates a remarkably high elastic modulus, underscoring its suitability as an optimal matrix and filler choice. The developed model achieves a coefficient of determination (R 2 ) of 0.90, affirming the applicability and effectiveness of machine learning algorithms in FP of epoxy resins. This approach offers a robust strategy for enhancing the performance of epoxy resins. Epoxy ; Polymerization ; Materials science ; Polymer science ; Composite material ; Computer science ; Polymer chemistry ; Polymer Photopolymerization techniques and applications ; Advanced Computing and Algorithms ; Additive Manufacturing and 3D Printing Technologies
Authors: Deng, Yanchen | Zhao, Chendong | Li, Yixuan | Tang, Bijun | Wang, Xinrun | Zhang, Zhonghan | Lu, Yuhao | Yang, Penghui | Huang, Jianguo | Xiao, Yushan | Guan, Cuntai | Liu, Zheng | An, Bo
Source: arXiv (Cornell University) (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast alloys. MATAI integrates a curated alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop. The framework estimates key mechanical propertie, sincluding density, yield strength, ultimate tensile strength, and elongation, directly from composition, using multi-task learning and physics-informed inductive biases. Alloy design is framed as a constrained optimization problem and solved using a bi-level approach that combines local search with symbolic constraint programming. We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials, where it rapidly identifies candidates that simultaneously achieve lower density (&lt;4.45 g/cm3), higher strength (&gt;1000 MPa) and appreciable ductility (&gt;5%) through only seven iterations. Experimental validation confirms that MATAI-designed alloys outperform commercial references such as TC4, highlighting the framework's potential to accelerate the discovery of lightweight, high-performance materials under real-world design constraints. Property (philosophy) ; Constraint (computer-aided design) ; Inverse ; Ductility (Earth science) ; Inverse problem ; Key (lock) ; Engineering design process ; Artificial neural network Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Hsu-Kai Cheng | Po-Yu Yang | Jyh-Pin Chou | Chun-Wei Pao
Source: PRX Energy (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Predicting Complex Materials Energetics with a Scalable Compact Nine-Qubit Quantum Machine-Learning Model Accurate machine-learning (ML) prediction of material energies typically relies on large models with thousands to hundreds of thousands of parameters and scales poorly with the system size. Here, we present a compact hybrid classical-quantum ML framework that predicts the energies of complex materials using fewer than ten qubits, regardless of system size. By compressing local atomic descriptors into a fixed-size feature spectrum, we enable amplitude encoding of chemically diverse systems into shallow quantum circuits. We implement quantum convolutional neural networks (QCNNs) and quantum deep neural networks (QDNNs) with fewer than <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mn>100</a:mn> </a:math> trainable parameters and achieve prediction errors below 0.05 eV/atom on chemically complex alloys ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi>Co</c:mi> </c:math> <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:msub> <e:mi/> <e:mn>25</e:mn> </e:msub> </e:math> <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:mi>Ni</g:mi> </g:math> <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"> <i:msub> <i:mi/> <i:mn>25</i:mn> </i:msub> </i:math> ( <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"> <k:mi>Hf</k:mi> </k:math> <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mi>Ti</m:mi> </m:math> <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline"> <o:mi>Zr</o:mi> </o:math> ) <q:math xmlns:q="http://www.w3.org/1998/Math/MathML" display="inline"> <q:msub> <q:mi/> <q:mn>50</q:mn> </q:msub> </q:math> ) and lithium-intercalated metal-organic frameworks. Our results demonstrate the feasibility of scalable quantum assisted materials modeling and introduce a resource-efficient route toward quantum machine learning for atomistic simulations. Quantum ; Scalability ; Artificial neural network ; Encoding (memory) ; Feature (linguistics) ; Complex system ; Convolutional neural network ; Quantum system ; Quantum computer Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Mohamed Salem | Moustafa Youssef
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of 2D Optoelectronic Materials via Interpretable Graph Neural Networks and High-Throughput DFT Screening Electronegativity ; Convolutional neural network ; Density functional theory ; Artificial neural network ; Semiconductor ; Virtual screening ; Band gap ; Coupling (piping) ; Key (lock) Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Jose Siguenza | Bharath Ramsundar
Source: ArXiv.org (preprint, 2025-10-19)
Field: Materials Science — Materials Chemistry

DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library Neural networks that incorporate geometric relationships respecting SE(3) group transformations (e.g. rotations and translations) are increasingly important in molecular applications, such as molecular property prediction, protein structure modeling, and materials design. These models, known as SE(3)-equivariant neural networks, ensure outputs transform predictably with input coordinate changes by explicitly encoding spatial atomic positions. Although libraries such as E3NN [4] and SE(3)-TRANSFORMER [3 ] offer powerful implementations, they often require substantial deep learning or mathematical prior knowledge and lack complete training pipelines. We extend DEEPCHEM [ 13] with support for ready-to-use equivariant models, enabling scientists with minimal deep learning background to build, train, and evaluate models, such as SE(3)-Transformer and Tensor Field Networks. Our implementation includes equivariant models, complete training pipelines, and a toolkit of equivariant utilities, supported with comprehensive tests and documentation, to facilitate both application and further development of SE(3)-equivariant models. Equivariant map ; Property (philosophy) ; Field (mathematics) ; Encoding (memory) ; Artificial neural network ; Tensor (intrinsic definition) ; Deep learning Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Ganesh Raghavendran | Bing Han | Fortune Adekogbe | Shuang Bai | Bingyu Lu | William Wu | Minghao Zhang | Ying Shirley Meng
Source: Journal of Microscopy (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Deep learning assisted high‐resolution microscopy image processing for phase segmentation in functional composite materials In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilisation of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area. This work proposes a novel workflow for FFT-based segmentation, periodic component detection and phase segmentation from raw high-resolution Transmission Electron Microscopy (TEM) images using a trained U-Net segmentation model. The developed model can expedite the detection of components and their phase segmentation, diminishing the temporal and cognitive demands associated with scrutinising an extensive array of TEM images, thereby mitigating the potential for human errors. This approach presents a novel and efficient image analysis approach with broad applicability beyond the battery field and holds potential for application in other related domains characterised by phase and composition distribution, such as alloy production. Artificial intelligence ; Segmentation ; Computer science ; Workflow ; Computer vision ; Image segmentation ; Deep learning ; Image processing ; Pattern recognition (psychology) ; Image (mathematics) Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Peter Mastracco | Kehang Yu | Xin Wang | C. Murillo | Carlos Melchor | Calvin H. Belcher | Julie M. Schoenung | Enrique J. Lavernia | Stacy M. Copp
Source: Advanced Engineering Materials (article, 2025-04-18)
Field: Materials Science — Biomaterials

Machine Learning‐Guided Discovery of Factors Governing Deformation Twinning in Mg–Y Alloys Magnesium (Mg) alloys are promising lightweight structural materials whose limited strength and room‐temperature ductility limit applications. Precise control of deformation‐induced twinning through microstructural alloy design is being investigated to overcome these deficiencies. Motivated by the need to understand and control twin formation during deformation in Mg alloys, a series of magnesium‐yttrium (Mg–Y) alloys are investigated using electron backscatter diffraction (EBSD). Analysis of EBSD maps produces a large dataset of microstructural information for &gt;40000 grains. To quantitatively determine how processing parameters and microstructural features are correlated with twin formation, interpretable machine learning (ML) is employed to statistically analyze the individual effects of microstructural features on twinning. An ML classifier is trained to predict the likelihood of twin formation, given inputs including grain microstructural information and synthesis and deformation conditions. Then, feature selection is used to score the relative importance of these inputs for twinning in Mg–Y alloys. It is determined that using information only about grain size, grain orientation, and total applied strain, the ML model can predict the presence of twinning and that other parameters do not significantly contribute to increasing the model's predictive accuracy. Herein, the utility of ML for gaining new fundamental insights into materials processing is illustrated. Crystal twinning ; Materials science ; Deformation (meteorology) ; Metallurgy ; Composite material ; Microstructure Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Hydrogen Storage and Materials
Authors: Yuxin Chang | Ian Benlolo | Yang Bai | Christian Reimer | Pengfei Ou | Isaac Tamblyn | Edward H. Sargent
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

High-Entropy Alloy Electrocatalysts Screened Using Machine Learning Informed by Quantum-Inspired Similarity Analysis The discovery of new electrocatalysts can be aided by density functional theory (DFT) computation of overpotentials based on the energies of chemical intermediates on prospective adsorption sites. We hypothesize that, in training a machine learning model on DFT data, one could improve accuracy by introducing a quantitative measure of similarity among adsorption sites. When we augment a graph neural network-based machine learning workflow using similarity as an input feature, we find that the required training data set size is decreased from 1600 to 800, leading to a 2× acceleration: the number of DFT calculations required to train to a given level of accuracy is cut in half. This approach identifies Fe 0.125 Co 0.125 Ni 0.229 Ir 0.229 Ru 0.292 as a promising oxygen reduction reaction catalyst with overpotential 0.24 V, outperforming a Pt/C benchmark. We examine, by studying experimentally four additional HEAs, the predictive powerof the computational approach. Figure 1 Alloy ; Materials science ; Entropy (arrow of time) ; Quantum ; Computer science ; Statistical physics ; Nanotechnology ; Artificial intelligence ; Metallurgy ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques