jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikhail Lazarev | Andrey Ustyuzhanin
Source: arXiv (Cornell University) (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

Symbolic regression for defect interactions in 2D materials Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences. Interpretability ; Symbolic regression ; Artificial neural network ; Regression ; Regression analysis ; Symbolic data analysis ; Class (philosophy) Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Advanced Graph Neural Networks
Authors: Giacomo Melani (13169979) | Thantip Roongcharoen (9519915) | Giorgio Conter (19504148) | Luca Sementa (1491847) | Alessandro Fortunelli (1268154)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Machine-Learning-AcceleratedConformal Sampling ofMethanol Catalytic Conversion on Bimetallic Systems We present an approach to accelerate the construction of reaction energy diagrams and mechanistic pathways of novel bimetallic catalytic systems by exploiting information on a known case, and we test it on the CuPd system for the catalytic decomposition of methanol. Based on machine-learning and conformal techniques for building training databases, our proposal realizes the multicomponent extension of the conformal sampling of catalytic process (CSCP) approach and maintains its same characteristic features of accuracy, efficiency, and throughput, thus in principle enabling a high-throughput screening of catalytic processes across general alloy compositions. Moreover, the so-derived CSCP reactive MLIP describes equally well the pure (Cu, Pd) and bimetallic (CuPd) catalysts, thus enabling a high-throughput screening for the given catalytic process. Bimetallic strip ; Catalysis ; Methanol ; Conformal map ; Sampling (signal processing) ; Process (computing) ; Alloy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Dylan Budnick | Benhour Amirian | Abhijit Brahme | Haitham El-Kadiri | Kaan Inal
Source: International Journal of Plasticity (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

U-PolyConformer: Spatiotemporal machine learning for microstructure engineering • A novel spatiotemporal CNN-Transformer machine learning framework is proposed. • The first framework that captures full-field stress and strain evolution under arbitrary loading conditions. • Exhibits strong generalization; accurately capturing loading conditions, microstructures, and hardening outside the training distributions. • A novel microstructure optimization framework is employed to enhance formability by delaying localization. Accelerating the prediction of mechanical behaviour in heterogenous materials is critical for large-scale microstructure optimization and realizing functionally optimized materials. While existing machine learning approaches have demonstrated an ability to accelerate predictions for the full-field mechanical response of a wide range of heterogenous microstructures, they have been largely limited to monotonic loading conditions. This paper introduces U-PolyConformer, a spatiotemporal machine learning framework that combines U-Net convolutional neural networks with transformer layers, capable of capturing the full-field stress and strain evolution under monotonic and random walk loading conditions. Trained on a large dataset of crystal plasticity finite element method (CPFEM) simulations with FCC polycrystals, the model accurately captures complex phenomena, including strain localization and stress unloading. The U-PolyConformer achieves a 7,900x speed-up over the ground-truth CPFEM simulations while producing high-fidelity results in both interpolative and extrapolative regimes. Comprehensive evaluations demonstrate the U-PolyConformer’s capacity to generalize outside the training distribution to novel microstructures, loading conditions, and strain hardening behaviours. To highlight the model’s potential as a surrogate for accelerating computational materials engineering workflows, a microstructure optimization framework based on static recrystallization is introduced and used to delay the onset of localization. This framework is successfully used to identify the grains which initiate the onset of localization, illustrating how the proposed model and optimization framework may be used for identifying and exploring property-performance relationships. Microstructure ; Formability ; Monotonic function ; Finite element method ; Hardening (computing) ; Crystal plasticity ; Strain hardening exponent ; Plasticity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Metallurgy and Material Forming
Authors: Rebecca Brew | I. Nelson | Meruyert Binayeva | Amlan Nayak | W. Kyle Simmons | Joseph J. Gair | Corin Wagen
Source: ChemRxiv (preprint, 2025-04-10)
Field: Materials Science — Materials Chemistry

Wiggle150: Benchmarking Density Functionals And Neural Network Potentials On Highly Strained Conformers Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving non-equilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometries—generated via metadynamics and scored using DLPNO-CCSD(T)/CBS reference energies—exhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed–accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving non-equilibrium systems and guide the development of new density functionals and neural network potentials. Benchmarking ; Conformational isomerism ; Artificial neural network ; Density functional theory ; Materials science ; Statistical physics ; Computational chemistry ; Chemical physics ; Computer science ; Physics ; Business ; Artificial intelligence ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science
Authors: Aman Jindal (4045019) | Philipp Schienbein (4022753) | Banshi Das (5122946) | Dominik Marx (640393)
Source: Figshare (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Computing BulkPhase IR Spectra from Finite ClusterData via Equivariant Neural Networks Calculating accurate IR spectra from molecular dynamics simulations is crucial for understanding structural dynamics and benchmarking simulations. While machine learning has accelerated such calculations, leveraging finite-cluster data to compute condensed-phase IR spectra remains unexplored. In this work, we address a fundamental question: Can a machine learning model trained exclusively on electronic structure calculations of finite-size clusters reproduce the bulk IR spectrum? Using the atomic polar tensor as a target training property, we demonstrate that the corresponding equivariant neural network accurately recovers the bulk IR spectrum of liquid water, establishing the key link between finite-cluster data and bulk properties. Artificial neural network ; Spectral line ; Cluster (spacecraft) ; Tensor (intrinsic definition) ; Phase (matter) ; Key (lock) ; Equivariant map ; Infrared Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Mikhail Lazarev | Andrey Ustyuzhanin
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Symbolic regression for defect interactions in 2D materials Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences. Interpretability ; Symbolic regression ; Artificial neural network ; Regression ; Regression analysis ; Symbolic data analysis ; Class (philosophy) Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Advanced Graph Neural Networks
Authors: Fabian Grünewald | Leif Seute | Riccardo Alessandri | Melanie König | P. Kroon
Source: ChemRxiv (preprint, 2025-02-21)
Field: Chemistry — Physical and Theoretical Chemistry

CGsmiles: A Versatile Line Notation for Molecular Representations Across Multiple Resolutions Coarse-grained (CG) models simplify molecular representations by grouping multiple atoms into effective particles, enabling faster simulations and reducing the chemical compound space compared to atomistic methods. Additionally, models with chemical specificity, such as Martini, may extrapolate to cases where experimental data is scarce, making CG methods highly promising for high-throughput (HT) screenings and chemical space exploration. Yet no rigorous data formats exist for the crucial aspect of describing how the atoms are grouped (i.e., the mapping). As CG models advance toward true HT capabilities, the lack of mappings and indexing capabilities for the growing number of CG molecules poses a significant barrier. To address this, we introduce CGsmiles, a versatile line notation inspired by the popular Simplified Molecular Input Line Entry System (SMILES) and BigSMILEs. CGsmiles encodes the molecular graph and particle (atom) properties independent of their resolution and incorporates a framework that allows seamless conversion between coarse- and fine-grained resolutions. By specifying fragments that describe how each particle is represented at the next finer resolution (e.g. CG particles to atoms), CGsmiles can represent multiple resolutions and their hierarchical relationships in a single string. In this paper, we present the CGSmiles syntax and analyze a benchmark set of 407 molecules from the Martini force field. We highlight key features missing in existing notations that are essential for accurately describing CG models. To demonstrate the utility of CGsmiles beyond simulations, we construct two simple machine-learning models for predicting partition coefficients, both trained on CGsmiles-indexed data and leveraging information from both CG and atomistic resolutions. Finally, we briefly discuss the applicability of CGsmiles to polymers, which particularly benefit from the multiresolution nature of the notation. Notation ; Line (geometry) ; Computer science ; Programming language ; Mathematics ; Arithmetic ; Geometry Various Chemistry Research Topics ; Diatoms and Algae Research
Authors: Yahya Rabbani | Ardemis A. Boghossian
Source: The Electrochemical Society Interface (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Advancements in the Surface Functionalization of Carbon Nanotubes for Optoelectronic Modulation and Sensing Applications Single-walled carbon nanotubes (SWCNTs) demonstrate tunable optoelectronic properties that enable diverse applications in biosensing. These properties are often tailored by altering the functionalization of the SWCNT surface. This work provides an overview of established and emerging approaches for modifying the chemistry of SWCNTs for applications in optical sensors. We review covalent and non-covalent approaches based on chemical modification, small molecule functionalization, surfactant and lipid solubilization, polymer wrapping, and biomolecule conjugation to modulate optical properties, especially the intrinsic, near-infrared photoluminescence (fluorescence) of SWCNTs. Finally, we discuss emerging approaches based on machine learning for designing functionalization strategies. These advancements in surface functionalization provide an avenue not only for tailoring SWCNTs with precision for specific applications, but also for elucidating the underlying mechanisms that govern their optoelectronic properties. Surface modification ; Carbon nanotube ; Biomolecule ; Polymer ; Photoluminescence ; Biosensor ; Covalent bond Carbon Nanotubes in Composites ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Sherif Abdulkader Tawfik
Source: ChemRxiv (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

Computational material science has a data problem The ever-increasing amount of data that is generated by computational material science tools has propelled the invention of new machine learning models, and subsequently assisted in the discovery of new materials. Here we present an overdue questioning of the data itself: is it suitable for training machine learning models? By examine the energy above the convex hull, electronic bandgap and formation energy data in the Materials Project dataset, we find that energy above the convex hull is an unsteady quantity, which is because the present materials the database do not have sufficient representation of the chemical spaces that is necessary to account for crystal decomposition. The unsteadiness of Eh also applies to DFT-computed voltages, because the computed voltage is the average of voltages obtained from the known cation-deficient stable materials. We also show the discrepancies in the reported electronic bandgap values in the Materials Project database, and the formation energy data can potentially shift due to arbitrary changes in interlayer distances of layered materials, or finding optimisation parameters that reduce the energy of the structure below the value deposited in the database. We discuss possible approaches to mitigate these data problems. Computer science ; Data science ; Management science ; Economics Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Qiang Shen | Licheng Zhu | Zhijian Shen | Tianzeng Ru | Minghui Guo | Jun Ren | Tao Wu | Zhanqiang Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Capacity Prediction of Cobalt Nickel Phosphide Supercapacitors Based on Machine Learning Supercapacitor ; Capacitance ; Electrode ; Current density ; Electrolyte ; Artificial neural network ; Nickel ; Current (fluid) Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Duk−Young Jung | Hyeon Yang | Dae Joon Kang | Donghyeon Kim | Sung‐Hee Roh | Jiyong Kim
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Chemistry — Inorganic Chemistry

ML-based adsorption isotherm prediction of metal-organic frameworks for carbon dioxide and methane separation adsorbent screening The efficient separation of carbon dioxide (CO2) and methane (CH4) is crucial for chemical processes, including biogas upgrading and natural gas purification. Metal-organic frameworks (MOFs) have gained significant attention as promising adsorbents for these processes due to their high porosity and tunable structures. Estimating the adsorption capacity of MOFs is essential for screening high performing adsorbents. While molecular simulations are commonly used to estimate the adsorption capacities, their computational intensity acts as a bottleneck in screening MOF adsorbents. In this study, we propose a machine learning (ML)-based framework for the high-throughput prediction of adsorption isotherms for CO2 and CH4 in MOFs. A graph neural network (GNN) model was developed to predict adsorption capacities, effectively replacing the time-consuming molecular simulations. The GNN model processes the structural graphs of MOFs, capturing their spatial configurations, such as surface structure and pore characteristics, which are closely related to adsorption performance. Based on the adsorption capacities predicted by the GNN model, an isotherm model was derived to characterize the adsorption behavior of the MOFs. This framework enables the rapid and reliable screening of MOFs with superior adsorption performance for CO2/CH4 separation, offering a computationally efficient approach that paves the way for broader applications of MOFs in carbon dioxide separation technologies. Adsorption ; Methane ; Metal-organic framework ; Carbon dioxide ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Wesley F. Reinhart | Susan B. Sinnott
Source: MRS Bulletin (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Moving in the right direction: Emerging ideas in fitting accurate potentials Abstract The development of accurate interatomic potentials remains a cornerstone challenge in computational materials science. This article examines the transformative shift from physics-based potentials to machine learning interatomic potentials, highlighting how emerging methodologies are revolutionizing the field. We discuss how data-driven approaches, incorporating advanced optimization and automated workflows adapted from the machine learning community, enable the creation of powerful potentials that capture complex atomic interactions with unprecedented accuracy and flexibility. These advances allow for dynamic adaptation to diverse chemical environments and competing requirements, moving beyond the limitations of traditional physics-based potentials. Looking forward, we consider the potential of foundation models and the concept of “universal potentials,” envisioning a future where a single, adaptable model framework could accurately model a wide range of materials and phenomena. Graphical abstract Transformative learning ; Workflow ; Cornerstone ; Adaptation (eye) ; Range (aeronautics) ; Foundation (evidence) Machine Learning in Materials Science ; Fault Detection and Control Systems ; Force Microscopy Techniques and Applications
Authors: Giacomo Melani | Thantip Roongcharoen | Giorgio Conter | Luca Sementa | Alessandro Fortunelli
Source: The Journal of Physical Chemistry C (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Conformal Sampling of Methanol Catalytic Conversion on Bimetallic Systems We present an approach to accelerate the construction of reaction energy diagrams and mechanistic pathways of novel bimetallic catalytic systems by exploiting information on a known case, and we test it on the CuPd system for the catalytic decomposition of methanol. Based on machine-learning and conformal techniques for building training databases, our proposal realizes the multicomponent extension of the conformal sampling of catalytic process (CSCP) approach and maintains its same characteristic features of accuracy, efficiency, and throughput, thus in principle enabling a high-throughput screening of catalytic processes across general alloy compositions. Moreover, the so-derived CSCP reactive MLIP describes equally well the pure (Cu, Pd) and bimetallic (CuPd) catalysts, thus enabling a high-throughput screening for the given catalytic process. Bimetallic strip ; Catalysis ; Methanol ; Conformal map ; Sampling (signal processing) ; Process (computing) ; Alloy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Hao Deng | Mark Messner
Source: ArXiv.org (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress-strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep-fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain-stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke's Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress-strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep-fatigue mechanical behavior, exceeding the standard Chaboche model's accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep-fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form. Ode ; Nonlinear system ; Artificial neural network ; Experimental data ; Identification (biology) ; Test data ; System identification Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Fatigue and fracture mechanics