jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuhao Hu | Xinjian Ouyang | Zhilong Wang | Feng Zhang | Dawei Wang
Source: Journal of Physics Condensed Matter (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Message-passing neural network for magnetic phase transition simulation Predicting magnetic phase transitions traditionally relies on a Hamiltonian model to capture key magnetic interactions. Recent advances in machine learning enables the development of a unified approach that can handle diverse magnetic systems without designing new Hamiltonians for each case. To this end, we employ message-passing neural network (MPNN) potentials to investigate magnetic phase transitions of two-dimensional chromium trihalidesCrX3(X = I, Br, Cl) . We achieve this by introducing a specialized MPNN with the ability to incorporate the magnetic degrees of freedom. This magnetic MPNN incorporates atomic magnetic moments directly into the message-passing process, enabling accurate modeling of potential energy surfaces in magnetic materials. This approach improves on our previous work, which had the same aim but used Behler-Parrinello neural network that relies on hand-crafted descriptors as the underlying universal magnetic Hamiltonian. It also adds the capability to treat magnetic degrees of freedom and atom displacement in a unified way. Using two-dimensionalCrX3as examples and combining the MPNN with the Landau-Lifshitz-Gilbert equation, we simulate ferromagnetic and antiferromagnetic phase transitions as a function of temperature. These results highlight the potential of MPNNs for advancing research in magnetic materials. Hamiltonian (control theory) ; Artificial neural network ; Computer science ; Phase transition ; Degrees of freedom (physics and chemistry) ; Message passing ; Physics ; Antiferromagnetism ; Condensed matter physics ; Statistical physics ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Junhong Li | K. Song | Jun Li
Source: Chemistry (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

CQPES: A GPU-Aided Software Package for Developing Full-Dimensional Accurate Potential Energy Surfaces by Permutation-Invariant-Polynomial Neural Network Accurate potential energy surfaces (PESs) are the prerequisite for precise studies of molecular dynamics and spectroscopy. The permutationally invariant polynomial neural network (PIP-NN) method has proven highly successful in constructing full-dimensional PESs for gas-phase molecular systems. Building upon over a decade of development, we present CQPES v1.0 (ChongQing Potential Energy Surface), an open-source software package designed to automate and accelerate PES construction. CQPES integrates data preparation, PIP basis generation, and model training into a modernized Python-based workflow, while retaining high-efficiency Fortran kernels for processing dynamics interfaces. Key features include GPU-accelerated training via TensorFlow, the robust Levenberg–Marquardt optimizer for high-precision fitting, real time monitoring via Jupyter and Tensorboard, and an active learning module that is built on top of these. We demonstrate the capabilities of CQPES through four representative case studies: CH4 to benchmark high-symmetry handling, CH3CN for a typical unimolecular isomerization reaction, OH + CH3OH to test GPU training acceleration on a large system, and Ar + H2O to validate the active learning module. Furthermore, CQPES provides direct interfaces with established dynamics software such as Gaussian 16, Polyrate 2017-C, VENUS96C, RPMDRate v2.0, and Caracal v1.1, enabling immediate application in chemical kinetics and dynamics simulations. Software ; Artificial neural network ; Fortran ; Key (lock) ; Acceleration ; Energy (signal processing) ; Interface (matter) ; Gaussian ; Invariant (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Chengyu Xiao | Jing Li | Wansu Hua | Yifan Zhang | Han Zhou
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning design of spectral-selective infrared metasurfaces based on Conway patterns Metasurfaces offer precise control over infrared radiation, holding great potential for thermal management, stealth technology, and sensing applications. However, existing computational approaches, especially machine learning-based methods, typically rely on simplistic geometries and limited material selections, constraining design complexity and performance. To overcome these challenges, we introduce a machine learning-driven platform employing Conway-inspired patterns to achieve band-selective infrared metasurfaces. Our system integrates an automated optimization framework combining intricate, rule-based Conway morphologies, standard geometric parameters, and an extensive database of 71 materials. Leveraging a deep neural network for forward predictions and particle swarm optimization for efficient inverse design, our approach successfully produces diverse, high-performance metasurfaces. These designs exhibit superior radiative cooling and stealth properties within the critical 5-8 μm atmospheric transparency window. Generated mosaic-like patterns are further optimized through image filtering and symmetry processing, enhancing fabrication feasibility and minimizing polarization dependence. This comprehensive design paradigm significantly broadens the available design space, facilitating the discovery of novel metasurface structures with multifunctional capabilities in optics and thermal management. Infrared ; Cellular automaton ; Spectral properties ; Automaton ; Computer science ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Artificial intelligence ; Physics ; Computational chemistry ; Optics ; Biology Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Ali Ayoub | Lucian A. Lucia
Source: Sustainability (article, 2025-11-11)
Field: Materials Science — Biomaterials

Sustainable Cationic Polyelectrolytes from Agri-Forestry Biomass: Conventional Chemistry to AI-Optimized Reactive Extrusion Cationic polyelectrolytes, characterized by positively charged functional groups, play an essential role in industries ranging from food solutions, water treatment, medical, cosmetic, textiles and agriculture due to their electrostatic interactions, biocompatibility, and functional versatility. This paper critically examines the transition from petroleum-based synthetic polymers such as poly(diallyldimethylammonium chloride) and cationic polyacrylamides to sustainable natural alternatives derived from agri-forestry resources like starch derivatives and cellulose. Through a cradle-to-gate life cycle assessment, we highlight the superior renewability, biodegradability, and lower carbon footprint of bio-based polycations, despite challenges in agricultural sourcing and processing. This study examines cationization processes by comparing the environmental limitations of traditional chemical methods, such as significant waste production and limited scalability, with those of second-generation reactive extrusion (REX), which enables solvent-free and rapid modification. REX also allows for adjustable degrees of substitution and ensures uniform charge distribution, thereby enhancing overall functional performance. Groundbreaking research and optimization achieved through the integration of artificial intelligence and machine learning for parameter regulation and targeted mechanical energy management underscore REX’s strengths in precision engineering. By methodically addressing current limitations and articulating future advancements, this work advances sustainable innovation that contributes to a circular economy in materials science. Carbon footprint ; Cationic polymerization ; Polyelectrolyte ; Agriculture ; Extrusion ; Work (physics) ; Green chemistry ; Sustainable production ; Polymer Advanced Cellulose Research Studies ; Polymer-Based Agricultural Enhancements ; biodegradable polymer synthesis and properties
Authors: Gabriel R Francas | Anouar-Akacha Delenda | Jacob W. Martin | Colin Bousige | Irene Suarez‐Martinez | Nigel A. Marks | Chris Ewels
Source: Carbon (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Influence of screw dislocations on stacking order in graphite This work examines how screw dislocations disrupt ideal stacking in graphenic materials, using a machine learning interatomic potential to model screw dislocation dipoles within different periodic cells. A novel tool is developed to assess local interlayer registration to quantify and visualise regions exhibiting stacking order. Using molecular dynamics simulations, we demonstrate that single screws exhibit greater stability in rhombohedral stacking compared to AA stacking, whereas double screws exhibit greater stability in Bernal AB stacking. The investigation reveals several mechanisms through which the ideal stacking configuration is achieved despite the presence of screw dislocations, including shearing, bond length distortion, and buckling. Furthermore, an upper threshold for the density of screw dislocations is calculated, beyond which ideal stacking cannot be realised, potentially offering an explanation for certain forms of turbostratic carbon. The findings also indicate that single screw dislocations hinder Bernal AB stacking in graphite, whereas double screw dislocations support Bernal stacking, which is significant for understanding the formation processes of graphite. • Single screws are stable in ABC graphite, double screws stable in AB graphite. • Above critical screw density graphite becomes buckled, turbostratic. • New code quantifies stacking registration quality and type in graphenic materials. • Machine-learning interatomic potentials benchmarked for stacking in graphite. Stacking ; Partial dislocations ; Dipole ; Dislocation ; Ideal (ethics) ; Graphite ; Stacking fault Graphene research and applications ; Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites
Authors: Sadman Sadeed Omee | Lai Wei | Sourin Dey | Jianjun Hu
Source: ArXiv.org (preprint, 2025-06-12)
Field: Materials Science — Materials Chemistry

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control Crystalline materials can form different structural arrangements (i.e. polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they were synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. We propose ParetoCSP2, a multi-objective genetic algorithm for polymorphism CSP that incorporates an adaptive space group diversity control technique, preventing over-representation of any single space group in the population guided by a neural network interatomic potential. Using an improved population initialization method and performing iterative structure relaxation, ParetoCSP2 not only alleviates premature convergence but also achieves improved convergence speed. Our results show that ParetoCSP2 achieves excellent performance in polymorphism prediction, including a nearly perfect space group and structural similarity accuracy for formulas with two polymorphs but with the same number of unit cell atoms. Evaluated on a benchmark dataset, it outperforms baseline algorithms by factors of 2.46-8.62 for these accuracies and improves by 44.8\%-87.04\% across key performance metrics for regular CSP. Our source code is freely available at https://github.com/usccolumbia/ParetoCSP2. Initialization ; Crystal structure prediction ; Population ; Convergence (economics) ; Group (periodic table) ; Benchmark (surveying) ; Polymorphism (computer science) ; Crystal structure Crystallization and Solubility Studies
Authors: Gabriela da Rosa | Mauro de Castro | Víctor Miguel García Velásquez | Santiago Pintos | J.M. Benedetto | Leandro Grille | S Valla | Luis M. Alvarez Salas | Victoria Calzada | Pablo D. Dans
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Aptamers Meet Structural Bioinformatics, Computational Chemistry, and Artificial Intelligence ABSTRACT Aptamers—short single‐stranded DNA or RNA—are the latest biomolecules to fall within reach of powerful structure‐prediction pipelines that blend bioinformatics, computational chemistry, and artificial intelligence. These tools now enable high‐throughput exploration of aptamer conformational landscapes, a prerequisite for rational design and optimization of their exceptional target affinity and specificity. Next‐generation sequencing has democratized library analysis, allowing any laboratory to handle millions of variants. Hybrid workflows currently offer the most reliable secondary and tertiary structure models, and explicit treatment of conformational flexibility is proving indispensable for mapping binding‐competent states. Yet every predictive tier—from classic free‐energy minimization to deep learning—still underrepresents chemically modified nucleotides, the very substitutions that grant therapeutic aptamers nuclease resistance and pharmacokinetic longevity. Capturing the structural and dynamical consequences of these modifications remains the key unsolved problem. Progress, therefore, hinges on two fronts: richer parameterization and training data that encompass modified bases, and tighter coupling of in silico screens with biophysical and structural validation. Bridging these gaps will convert the current wave of computational advances into clinically relevant aptamer‐based drugs ready to be delivered to the patients. This article is categorized under: Structure and Mechanism &gt; Molecular Structures Data Science &gt; Computer Algorithms and Programming Data Science &gt; Artificial Intelligence/Machine Learning Aptamer ; Workflow ; Flexibility (engineering) ; Bridging (networking) ; Nuclease ; Pipeline (software) ; Coupling (piping) Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; RNA and protein synthesis mechanisms
Authors: Rashid R. Valiev | Rinat T. Nasibullin | Dage Sundholm | Theo Kurtén
Source: The Journal of Physical Chemistry Letters (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Machine-Learning Predictions of Rate Constants of Internal Conversion Using Electronic and Structural Descriptors We present a machine learning approach to predict internal conversion rate constants (<i>k</i><sub>IC</sub>) based on molecular electronic and structural descriptors. Our approach addresses the challenge of accurately estimating <i>k</i><sub>IC</sub> between the first excited singlet state (<i>S</i><sub>1</sub>) and the ground state (<i>S</i><sub>0</sub>), which is an important deactivation channel of photophysical processes. Using a cost-efficient method that assumes excitation-energy transfer to the vibrations of the X-H bonds (X = C, N, O), we generated a data set for <i>k</i><sub>IC</sub> of more than 5000 molecules, including porphyrins and [8]circulenes using parameters calculated at the time-dependent density functional theory (TDDFT) level. We have used both the CatBoost (CB) model and a neural network (NN) including transformer-based and graph-based architectures. Using the CB and NN models we obtained high precision with <i>R</i><sup>2</sup> values of about 0.99. The models successfully predict <i>k</i><sub>IC</sub> even for new molecules when they are trained with energy-augmented data sets based on electronic and structural descriptors. We also introduce graph-based X-H bond descriptors to predict rate constants for various chemical classes. The calculated rate constants indicate the promising potential of these descriptors to predict <i>k</i><sub>IC</sub> using machine learning to bypass computationally demanding quantum mechanical calculations to determine the photophysical properties. Reaction rate constant ; Excited state ; Artificial neural network ; Density functional theory ; Singlet state ; Internal conversion ; Ground state ; Constant (computer programming) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Bowen Han | Yongqiang Cheng
Source: Machine Learning Science and Technology (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data<sup>*</sup> Abstract The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forces directly from atomic coordinates. This approach dramatically reduces computation time and minimizes the need for technical knowledge. In this paper, we produce a phonon database comprising nearly 5000 inorganic crystals to benchmark the performance of several leading uMLIPs. We further assess these models in real-world applications by using them to analyze experimental inelastic neutron scattering data collected on a variety of materials. Through detailed comparisons, we identify the strengths and limitations of these uMLIPs, providing insights into their accuracy and suitability for fast calculations of phonons and related properties, as well as the potential for real-time interpretation of neutron scattering spectra. Our findings highlight how the rapid advancement of AI in science is revolutionizing experimental research and data analysis. Benchmarking ; Benchmark (surveying) ; Phonon ; Computer science ; Inelastic neutron scattering ; Inelastic scattering ; Neutron ; Neutron scattering ; Computation ; Experimental data ; Interatomic potential ; Statistical physics ; Machine learning ; Artificial intelligence ; Scattering ; Physics ; Computational science ; Algorithm ; Molecular dynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Prakash Thakolkaran | Yiwen Zheng | Yaqi Guo | Aniruddh Vashisth | Siddhant Kumar
Source: Digital Discovery (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks An attention-based machine learning model identifies dangling mass as a key predictor of thermal conductivity in covalent organic frameworks, offering insights into structure–property relationships for enhanced material design. Key (lock) ; Thermal conductivity ; Covalent bond ; Artificial intelligence ; Materials science ; Computer science ; Data science ; Chemistry ; Composite material ; Organic chemistry ; Computer security Thermal properties of materials ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Keyuan Chen | Xingkao Zhang | Li Ma | J J Ye | Qi Qiu | Haoxiang Zhang | Rong Ju | Yudong Sui | Xiaohua Yu | Jing Feng
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum The deep potential (DP) is an innovative approach based on deep learning that uses ab initio calculation data derived from density functional theory (DFT), to create high-accuracy potential functions for various materials. Pl... | Find, read and cite all the research you need on Tech Science Press Platinum ; Computer science ; Materials science ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Yihuang Kang | Jean W. Zu
Source: Annals of Nuclear Energy (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Gamma-ray buildup factor calculation via an automated machine learning framework with integrated analysis Hyperparameter ; Consistency (knowledge bases) ; Artificial neural network ; Factor (programming language) ; Support vector machine ; Face (sociological concept) ; Variety (cybernetics) ; Key (lock) Radiation Shielding Materials Analysis ; Radiation Therapy and Dosimetry ; Boron Compounds in Chemistry