jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luca Benzi | Diana Nelli | Pascal Andreazza | Riccardo Ferrando | Georg Daniel Förster
Source: Computational Materials Science (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

A deep learning strategy to calibrate heteroatomic interactions in metal alloys A machine-learning-assisted strategy is proposed to calibrate the heteronuclear parameters of the Tight-Binding Second-Moment Approximation (TB–SMA) potential using finite-temperature experimental data. The method involves the use of neural-network surrogate models trained on a large dataset of fictitious binary alloys, generated by randomly sampling TB–SMA parameter sets within physically meaningful intervals. Each surrogate model learns to predict thermodynamic observables — mixing enthalpy and lattice parameter — directly from the potential parameters. Once trained, the networks provide instantaneous predictions, eliminating the need for costly simulations during the optimization loop. The surrogate models are then embedded in a minimization scheme that adjusts the mixed interaction parameters to reproduce experimental thermodynamic data at selected compositions and at given temperatures. This workflow is applied to ten binary alloys formed by Cu, Ni, Pt, Pd, and Rh, obtaining parametrizations that accurately match experimental trends. The approach is general and well adapted to complex multi-element systems as high-entropy alloys. It can be extended to other potential forms and target properties. Binary number ; Experimental data ; Observable ; Minification ; Workflow ; Design of experiments ; Surrogate model ; Estimation theory ; Calibration Machine Learning in Materials Science ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: Balachandra P. Shetty | Sudheer Reddy. J | Viraj G. R | Praveena B. A
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Optimizing polymer classification through FTIR spectra and feature-scaled machine learning models The research is based on the characterization of polymer materials by Fourier Transform Infrared (FTIR) spectroscopy which is a method of characterizing polymers in terms of their characteristic molecular vibration patterns. Three widely used thermoplastics ABS (Acrylonitrile Butadiene Styrene), PP (Polypropylene) and Nylon-66 were selected and analyzed on the basis of FTIR transmittance spectra. Wavenumbers and the values of percent transmittance were obtained and converted into feature vectors. Gaussian noise was used to boost and clean the data. Machine learning methods that were used to classify the materials included Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), random forest, and logistic regression, where 80% of the data were used to train the machine learning model and 20% to test it. Accuracy measures and confusion matrices were used to determine the performance of the models. Random Forest was the best in terms of performance in Nylon-66 classification; however, the overall accuracy was influenced by the error in classifying ABS and PP. Consequently, k-NN (70) and the Logistic Regression (69) have shown a high performance compared to the Random Forest (57) in terms of overall accuracy. This study reveals that even though the Random Forest could be used to classify a particular set of polymers, polymers that have very similar spectral characteristics are still difficult to differentiate, indicating that the work should be improved by increasing the size of the data set and the number of extracted features. Random forest ; Support vector machine ; Pattern recognition (psychology) ; Fourier transform infrared spectroscopy ; Fourier transform ; Gaussian ; Confusion matrix ; Test set ; Set (abstract data type) ; Polymer Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Albert Poater | Susana García‐Abellán | Juan V. Alegre‐Requena | Bartosz Trzaskowski | J. Pablo Martínez
Source: ACS Catalysis (article, 2026-01-19)
Field: Chemistry — Organic Chemistry

CatalySeed: A Reaction Database for Ruthenium-Catalyzed Ethenolysis of Seed Oils with Applications in Machine Learning Ethenolysis of unsaturated seed oils is an atom-efficient metathesis reaction that enables α-olefin production and fine chemical synthesis. By upcycling complex biobased molecules into value-added products, it supports circular chemical processes. In this study, we present a curated data set to support machine learning (ML) analysis of catalytic performance in the ethenolysis of seed oils. Through a detailed classification of 768 entries and 217 catalysts, along with the integration of the ROBERT ML framework, with the CatalySeed database we identify key electronic descriptors that correlate with experimental outcomes. Binary classification models for TON (threshold ≥ 0.75 × 106) and % selectivity (≥90%) achieved strong performance, suggesting that higher Ru partial charge tends to correlate with higher TON, while lower metal d-orbital character is generally associated with higher selectivity. These findings illustrate how this database, available through an open-access web server, enables ML to uncover predictive trends, supporting catalyst design strategies beyond conventional computational approaches for the transformation of renewable feedstocks. Catalysis ; Transformation (genetics) ; Set (abstract data type) ; Chemical database ; Binary number ; Key (lock) ; Binary classification ; Metathesis Synthetic Organic Chemistry Methods ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Ajnabiul Hoque | Nupur Jain | Divya R. Chenna | Raghavan B. Sunoj
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning models for catalytic asymmetric reactions of simple alkenes: from enantioselectivity predictions to chemical insights Enantioselectivity predictions of catalytic asymmetric transformations of alkenes using a class imbalance-aware AttentiveFP machine learning model offer good accuracy and chemically meaningful interpretability. Simple (philosophy) ; Catalysis ; Chemical reaction ; Training set ; Experimental data Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Guojing Xu | Hao Lu | Peixin Liu | Feng Cheng | C.S. Han | XiaoYan SONG
Source: Materials Genome Engineering Advances (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable Machine Learning Predicting Coercivity of Sm‐Co‐Based Alloys ABSTRACT This study has developed a physically interpretable machine learning framework for predicting coercivity of Sm‐Co‐based alloys by integrating principles of permanent magnetic materials. Key features governing coercivity were systematically reconstructed using a developed two‐step symbolic regression algorithm combining frequency statistics, and individual contributions of these reconstructed features were elucidated by sensitivity analysis. A high‐throughput predictive model was set up for coercivity evaluation with exceptional accuracy enabling data‐driven composition design of Sm‐Co‐based permanent magnetic alloys with high coercivity. Taking SmCo 7 ‐based alloys as an example, ternary doping with Ti, In, and Al was identified as optimal for coercivity enhancement. Guided by these predictions, novel multielement doped nanocrystalline Sm‐Co‐based alloys were prepared exhibiting record high coercivity. This work established a paradigm shift from empirical optimization to mechanism‐guided data‐driven design of advanced permanent magnetic materials, demonstrating the potential of interpretable machine learning in materials innovation. Coercivity ; Ternary operation ; Set (abstract data type) ; Robustness (evolution) ; Nanocrystalline material ; Magnet ; Binary number Magnetic Properties of Alloys ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Thijs G.I. VAN WIJK | Esin Aylin Melan | Eleonora THOMAS | Danny E.P. VANPOUCKE
Source: Document Server@UHasselt (UHasselt) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Diamond Materials Research As quantum information science advances, there is a growing demand for materials capable of exhibiting quantum effects. However, atomic-scale modeling is constrained by the computational cost of electronic-structure methods such as density-functional theory (DFT)[1]. We present machine-learning (ML) approaches that enhance standard computational methods and apply them to diamond color centers, computing electronic band structures and vibrational modes of Group IV defects, and the dynamics of hydrogen-related defects. For electronic structure calculations, Density Functional Theory does not suffice and falls short in calculating the excited states. The GW approximation provides solace. This approximation includes the self-energy of the system, resulting in better accuracy. The high computational resources required for this approach do not provide easy access to diamond defect calculations. Delta-Machine Learning (∆-ML) enables a way to reach GW accuracy with DFT level calculations as input. The vibrational modes of a crystal are obtained from the eigenvalues of the dynamical matrix, i.e., the second derivative of the potential energy with respect to nuclear positions. Ab initio calculations of this matrix are computationally expensive and become prohibitive for large systems such as color centers. A combined Density Functional Theory (DFT)-enhanced Machine Learning (ML) approach enables vibrational calculations at ab initio accuracy with significantly reduced computational cost. For molecular dynamics (MD) using DFT, a lot of computational power is required. This is why for MD runs, ML shows potential to decrease both these computational costs, and the time needed for the calculations. Initially, through exploration of ML Interatomic Potentials (MLIP)s [2], an initial ML model is made for predicting forces and energy of HV defects in diamond, being trained on ab initio DFT calculations. This ML model is then used to predict the behaviour and MD of hydrogen-vacancy (HV) defects in diamond. Hereby establishing a proof of principle for working ML models for MD runs on diamond-like systems. Figure 1-Dependence of the GW band gap (left axis) and computational cost (right axis) on the number of included bands, demonstrating the high computational cost associated with converging GW calculations. Density functional theory ; Ab initio ; Eigenvalues and eigenvectors ; Diamond ; Excited state ; Computational resource ; Electronic structure ; Ab initio quantum chemistry methods ; Interatomic potential Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Yufan Chen | Yuxuan Zhang | Haifan Zhou | Ching Ting Leung | Hui Gao
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Use of Machine Learning and Large Language Models in Chemical Information Extraction Rich information in the chemical literature presents unprecedented opportunities for accelerating discovery and optimization in chemistry through data-driven approaches. Nevertheless, converting raw information in the literature into structured databases relies primarily on manual curation, which is time-consuming and costly. In this review, we comprehensively examine recent advances in automatic chemical information extraction from the literature, focusing on image and text modalities. We trace the evolution from early rule-based and machine learning approaches to state-of-the-art methods leveraging large language models (LLMs) and vision language models. We discuss core tasks such as optical chemical structure recognition, reaction diagram parsing, named entity recognition, and experimental procedure extraction, highlighting representative methods, benchmark data sets, and practical challenges such as multimodal integration and data annotation. By systematically comparing these approaches, we identify key trends and persistent limitations and outline promising future directions toward robust, scalable, and automated chemical information extraction frameworks. This review aims to provide a practical guide for researchers seeking to harness machine learning and LLM technologies to accelerate the digital transformation of chemical science. Information extraction ; Benchmark (surveying) ; Key (lock) ; TRACE (psycholinguistics) ; Raw data ; Knowledge extraction Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Computational Drug Discovery Methods
Authors: Xiaotian Xing | Fuming Deng | Hengchen Xu | Chang Dong | Houzhen Chen | Yonghai Lai | Jiale Niu | Lei Zhang | Diaoyu Wang | Ziyi Liu | Qingyan Chen | Jianghe Liao
Source: International Journal of Refractory Metals and Hard Materials (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Deep learning-based temperature field prediction model for diamond synthesis assemblies Finite element method ; Diamond ; Temperature measurement ; Computation ; Field (mathematics) ; Core (optical fiber) ; Temperature gradient Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials
Authors: Leyang Wang | Yilun Gong | Zongrui Pei
Source: Advanced Quantum Technologies (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Quantum and Hybrid Machine‐Learning Models for Materials‐Science Tasks ABSTRACT Quantum computing has become increasingly practical in solving real‐world problems due to advances in hardware and algorithms. In this paper, we aim to design, apply, and evaluate quantum machine learning and hybrid quantum‐classical models in a few practical materials science tasks, i.e., predicting stacking fault energies and solutes that can ductilize magnesium. To this end, we adopt two different representative quantum algorithms, i.e., quantum support vector machines (QSVM) and quantum neural networks (QNN), and adjust them to our application scenarios. We systematically test the performance with respect to the hyperparameters of selected ansatzes. Eventually, we construct quantum models with optimized parameters for regression and classification that predict targeted solutes based on the elemental volumes, electronegativities, and bulk moduli of chemical elements. We identify a few combinations of hyperparameters that yield validation scores of approximately 90% for QSVM and hybrid QNN in both tasks. Hyperparameter ; Quantum ; Quantum computer ; Support vector machine ; Artificial neural network ; Construct (python library) ; Quantum chemical Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Youheng Chen | Jiajia Han | Chen Yang | Cuiping Wang | Xingjun Liu
Source: npj Computational Materials (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

A crystal graph convolutional neural network framework for predicting stacking fault energy in concentrated alloys The stacking fault energy (SFE) in concentrated alloys is highly sensitive to the local atomic environment. Understanding this relationship necessitates extensive sampling of configurations, which significantly increases computational demands. To address this challenge, we propose a graph neural network (GNN)-based framework utilizing unrelaxed bulk and stacking fault structures as inputs to directly predict SFE. We first investigate two key extrapolation capabilities for bulk formation energy prediction: scale extrapolation (predicting formation energy in larger supercells) and compositional extrapolation (predicting formation energy for compositions beyond the training set). Leveraging the validated model, we concurrently predict formation energies of both bulk and stacking fault configurations to compute the SFE. The structural similarity between these configurations enables efficient parameter sharing, accelerating model convergence. The framework demonstrates excellent interpretability and robust compositional extrapolation capabilities in predicting SFEs. Furthermore, leveraging its exceptional compositional extrapolation, we integrate the model with Monte Carlo simulations to successfully predict ordering behavior and solute segregation at stacking faults. Finally, we introduce a hierarchical training strategy that further reduces data requirements. Collectively, our work establishes a unified and efficient framework for robust prediction of planar fault energies in complex concentrated alloys. Extrapolation ; Interpretability ; Stacking ; Convolutional neural network ; Monte Carlo method ; Planar ; Stacking-fault energy ; Artificial neural network Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Kadri Muuga | Lisanne Knijff | Chao Zhang
Source: AI for Science (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion–molecule interactions. Here, we assess the ability of machine-learning (ML) models to infer the MEP, based on the equivariant graph-convolutional neural network architecture PiNet2 and trained on dipole and quadrupole moments. For the established QM9 dataset, we find that including the quadrupole contribution in the ML models substantially improves their ability to recover the MEP compared to dipole-only models. This trend is confirmed on the SPICE dataset, which spans a much broader region of organic chemical space. Together, this study underscores the central role of the quadrupole moment as a fitting target for ML models aiming at rapid access to the MEP. Quadrupole ; Dipole ; Discrete dipole approximation ; Feature (linguistics) ; Electrostatics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Xingliang Li | Wen Ren | Guangwei Liu
Source: International Journal of Information and Communication Technology (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Multi-objective optimisation of cigarette tobacco leaf blend formulation based on sensory-chemical correlation and machine learning Cigarette leaf blend formulation design is a core component in determining product sensory quality.This study proposes a multi-objective optimisation method based on sensory-chemical correlations and machine learning.First, key chemical components of leaf blend samples are systematically collected to construct an initial dataset.Subsequently, multivariate statistical methods such as partial least squares regression are employed to identify the key chemical indicators driving sensory quality.Based on this, a machine learning model based on deep learning is established to accurately predict the key chemical indicators and sensory quality scores of the formulation.Finally, sensory quality, key chemical indicators, and raw material costs are set as optimisation objectives to construct a multi-objective optimisation model.The experimental results show that the multi-objective optimisation model constructed by this method generates 152 Pareto optimal solutions, improving sensory quality by 12%, reducing raw material costs by 19%, and increasing chemical stability by 55%. Correlation ; Tobacco leaf ; Pattern recognition (psychology) ; Support vector machine Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Statistical and Computational Modeling
Authors: Ruiyu Wang | Vanessa J. Meraz | Pratyush Tiwary
Source: Chemical Reviews (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Machine Learning Driven Advances in Molecular Dynamics of Bulk and Interfacial Aqueous Systems Molecular dynamics (MD) simulations have been widely applied to investigate various physical and chemical processes in aqueous and interfacial environments, which are crucial for the design of energy materials and for understanding several chemical processes at the heart of life itself. However, the applicability of MD simulations has been constrained by several inherent challenges, including the accuracy of force fields, limitations in simulation size and time scales. One promising solution to these challenges is the integration of machine learning (ML) methods, both for improved description of the nature of interactions in aqueous systems as well as for enhanced sampling. In this review, we discuss the principles, implementation, and applications of ML force fields (MLFFs) and ML enhanced sampling methods to the study of aqueous, interfacial systems. We discuss five key categories of applications that use MLFFs, ML-enhanced sampling, and ML-driven data analytics. We first discuss how MLFFs are enabling quantum level accuracy at classical level cost for large scale simulations of complex aqueous and interfacial systems, and then we highlight how coupling them with enhanced sampling and advanced data analytics, especially graph based approaches for featurizing such systems, can be used both for enhancing simulations and for understanding them by yielding reliable low dimensional reaction coordinates that improve the interpretation of high dimensional MD data. The discussed applications include investigations into the structure and dynamics of bulk water and aqueous interfaces, proton transfer, catalysis, phase transitions, and the prediction of vibrational spectra. In each case, we highlight how ML-based methods enable simulations that were previously computationally prohibitive and provide new physical insights into aqueous solutions and interfaces. For instance, MLFFs allow nanosecond-scale simulations with thousands of atoms while maintaining quantum chemistry accuracy. Additionally, ML-enhanced sampling facilitates the crossing of large reaction barriers and enables the exploration of extensive configuration spaces. Moreover, ML models trained on simulation data uncover previously overlooked factors, such as the role of solvent dynamics in phase transitions. The combination of MLFFs with enhanced sampling techniques makes the calculation of high-dimensional free energy surfaces feasible, significantly improving our understanding of chemical reactions. Finally, we discuss the current challenges in this field and outline potential future research directions to further advance the integration of ML in MD simulations. Aqueous solution ; Molecular dynamics ; Dynamics (music) ; Molecular machine Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Physical and Chemical Molecular Interactions