jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Josiah Roberts | Eva Zurek | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas A. Wolfe | Donald W. Brenner | Stefano Curtarolo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Software Package for Generating Robust and Accurate Potentials Using the Moment Tensor Potential Framework Moment tensor ; Moment (physics) ; Tensor (intrinsic definition) ; Software package ; Computer science ; Software ; R package ; Computational science ; Algorithm ; Applied mathematics ; Mathematics ; Physics ; Programming language ; Geometry ; Classical mechanics ; Magnitude (astronomy) Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fatigue and fracture mechanics
Authors: Josiah Roberts | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas A. Wolfe | Donald W. Brenner | Stefano Curtarolo | Eva Zurek
Source: ArXiv.org (preprint, 2025-12-13)
Field: Materials Science — Materials Chemistry

A Software Package for Generating Robust and Accurate Potentials using the Moment Tensor Potential Framework We present the Plan for Robust and Accurate Potentials (PRAPs), a software package for training and using moment tensor potentials (MTPs) in concert with the Machine Learned Interatomic Potentials (MLIP) software package. PRAPs provides an automated workflow to train MTPs using active learning procedures, and a variety of utilities to ease and improve workflows when utilizing the MLIP software. PRAPs was originally developed in the context of crystal structure prediction, in which one calculates convex hulls and predicts low energy metastable and thermodynamically stable structures, but the potentials PRAPs develops are not limited to such applications. PRAPs produces two potentials, one capable of rough estimates of the energies, forces and stresses of almost any chemical structure in the specified compositional space -- the Robust Potential -- and a second potential intended to provide more accurate descriptions of ground state and metastable structures -- the Accurate Potential. We also present a Python library, mliputils, designed to assist users in working with the chemical structural files used by the MLIP package. Python (programming language) ; Software ; Workflow ; Tensor (intrinsic definition) ; Moment (physics) ; Context (archaeology) ; Metastability ; Software package Machine Learning in Materials Science ; Crystallography and molecular interactions ; Quantum Mechanics and Non-Hermitian Physics
Authors: Josiah Roberts | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas A. Wolfe | Donald W. Brenner | Stefano Curtarolo | Eva Zurek
Source: Computer Physics Communications (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

A software package for generating robust and accurate potentials using the moment tensor potential framework Python (programming language) ; Software ; Workflow ; Tensor (intrinsic definition) ; Moment (physics) ; Metastability ; Software package ; Context (archaeology) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Quantum Mechanics and Non-Hermitian Physics
Authors: Akira Matsui | Tsz Wai Ko | Ji Qi | Shyue Ping Ong
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Constructing Robust Machine Learning Potential through Enhanced Sampling in UMLIP Latent Feature Space Recent advances in machine learned interatomic potentials (MLIPs) have enabled accurate, large-scale atomic simulations for a wide range of materials and molecules. The robustness and accuracy of MLIPs critically depend on the coverage of the configuration space in the training dataset. A common strategy involves sampling diverse configurations generated by finite-temperature molecular dynamics (MD) for the initial dataset, followed by iterative expansion of the training set through active learning. However, certain configurations, such as transition states, are inherently rare events that may remain undersampled, even in long-timescale MD simulations. Here, we propose a method that leverages recent advances in universal MLIPs with biased MD simulations to enhance the sampling of such configurations. We demonstrate that the resulting MLIPs are significantly improved in robustness and predictive accuracy using several application domains, including lithium diffusion in battery materials. Robustness (evolution) ; Training set ; Feature vector ; Sampling (signal processing) ; Importance sampling ; Adaptive sampling ; Feature (linguistics) ; Set (abstract data type) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum many-body systems
Authors: Matthew Adle | Joshua B. Bostwick | Kristin Graves | Synjin Hipolito | Yong Gan
Source:  (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

High Impact Learning Practice through Group Research on Thermoelectric Energy Conversion Nanomaterials formed and performed research in the Senior Capstone Design course.The work deals with manufacturing multicomponent organic/inorganic composite materials containing nanotubes, nanofibers and nanoparticles in electric field.One of the fundamental problems associated with the manufacturing process is how fast the thermoelectric nanoparticles move into the titanium dioxide nanotubes under the electric force.The results were used to examine the effect of the manufacturing parameters on the production rate and the uniformity of the composite materials made in the casting process.The specialty of the project lies in the hands-on learning strategy for training upper level undergraduate students in the emerging field of energy conversion.Some special learning methods such as active learning via student interactions, instructorstudent face to face learning were adopted in the classroom activities.It is concluded that the active learning should be the future trend in educating upper level students.The curriculum we should strive for is an integration of knowledge learning and hands-on practicing.Through such a particular project, students should be better trained for energy manufacturing industry. Nanomaterials ; Thermoelectric effect ; Group (periodic table) ; Materials science ; Energy (signal processing) ; Energy transformation ; Thermoelectric materials ; Computer science ; Engineering physics ; Nanotechnology ; Engineering ; Physics Advanced Thermoelectric Materials and Devices
Authors: Victor Oluwatosin Ajayi | Samuel Terver Agamber | Rachael Folake Ameh | Musa James Adejoh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Laboratory Practical Interest as a Correlate of Students' Perceived Competence and Academic Performance in Practical Chemistry Competence (human resources) ; Mathematics education ; Psychology ; Chemistry ; Social psychology Various Chemistry Research Topics ; Science Education and Pedagogy ; Educational Research and Pedagogy
Authors: Yongjie Zhang | Shuang Liang | Xiaocheng Li | Chunyu Bai
Source: Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Analysis of bullet-resistant performance and weight optimization for civil aircraft cockpit doors This paper examines the shortcomings of current civil aircraft cockpit doors in resisting penetration from light firearms. We analyze and optimize the bullet-resistant capabilities of the cockpit door to ensure compliance with airworthiness standards while minimizing structural weight. Using the ABAQUS finite element analysis software, we constructed a dynamic bullet impact model for the cockpit door. Our comprehensive analysis of the door’s bullet-stopping performance under various impact scenarios includes examining the effects of impact angle, velocity, and door material on its bullet-resistant properties. We introduced lightweight, energy-absorbing materials and employed surrogate modeling alongside Latin Hypercube Sampling methods to determine optimal material combinations and thicknesses. These efforts aim to reduce the door’s weight while fulfilling airworthiness bullet-resistance requirements. This paper serves as a valuable reference for subsequent practical engineering applications in bullet-resistant design for cockpit doors. Doors ; Cockpit ; Aeronautics ; Engineering ; Aerospace engineering ; Computer science ; Mechanical engineering High-Velocity Impact and Material Behavior ; Automotive and Human Injury Biomechanics ; Transportation Safety and Impact Analysis
Authors: Caroline St‐Antoine | Gabrielle Foran | Amandine Bon | Marie-Chloé Michaud Paradis | Félix Therrien | Arnaud Prébé | Mickaël Dollé
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Polymers and Plastics

Flammability of Gel Polymer Electrolytes Gel polymer electrolytes are promising technology for next-generation lithium batteries, offering a balance between the high ionic conductivity of liquids and the safety of polymers. Although it is often suggested that gel polymer electrolytes are safer than traditional organic electrolytes, there is a lack of systematic research available on the flammability of this type of electrolyte. Flammability testing for liquids, including flash point measurement, is well-established. The flash point temperature represents the minimum temperature at which a vapor-air mixture ignites in the presence of an ignition source. This measurement is standardized and allows flammability to be quantified. The use of a closed-cup flash point apparatus typically requires 2 mL of liquid. However, a single gel polymer electrolyte sample normally contains only 15 to 300 µL of liquid. This limited volume complicates flash point testing, as the resulting flame is often too small to be visible to the naked eye. This challenge has been addressed via the development of a novel methodology that combines thermal imaging and computer vision to automatically detect the flash point in gel polymer electrolytes [1]. The adapted flash point detection apparatus will be presented along with insights into how intermolecular interactions between the solvent, polymer, and salt affect the flash point of gel polymer electrolytes. Balancing low flammability with high ionic conductivity poses significant challenges. While increasing solvent content can enhance the ionic conductivity of the electrolyte, it often compromises safety by raising flammability. Multi-objective Bayesian optimization enables the fine-tuning of electrolyte composition to balance ionic conductivity and flammability, even with a limited number of data points. St-Antoine, C., et al., Novel method to automatize flash point detection in small volumes of liquid by computer vision using thermal images. Submitted. Flammability ; Polymer electrolytes ; Electrolyte ; Materials science ; Polymer ; Composite material ; Chemistry ; Ionic conductivity Flame retardant materials and properties
Authors: Victor Oluwatosin Ajayi | Samuel Terver Agamber | Rachael Folake Ameh | Musa James Adejoh
Source: American Journal of Education and Learning (article, 2025-06-26)
Field: Chemistry — Physical and Theoretical Chemistry

Laboratory practical interest as a correlate of students’ perceived competence and academic performance in practical chemistry The study examined the correlation between students’ laboratory practical interest and thier perceived competence and academic performance in the context of practical chemistry. The study adopted a correlational research design. Laboratory Practical Interest Questionnaire (LPIQ), Perceived Competence Scale (PCS) and Chemistry Practical Performance Test (CPPT) were the instruments used for data collection. A sample of 179 students drawn from 4 schools in Anyigba, Kogi State, Nigeria was selected using multi-stage sampling techniques. Pearson correlation was used to answer the research questions and null hypotheses that guided the study. The study revealed that there was a significant correlation between students’ laboratory practical interest and perceived competence, particularly in their ability to solve practical chemistry related questions (r =.881, P&lt;0.005). Students who are genuinely interested in laboratory practical are more engaged in active learning because students’ interest fuel their perceived competence, making students more persisted through challenges and put in the necessary effort to learn and achieve higher academic performance in practical chemistry. Thus, science teachers should use innovative learning strategies to foster students’ interest and school authorities should provide infrastructure and 21st century laboratory facilities needed by science teachers to maintain students’ interest in laboratory practical. Competence (human resources) ; Psychology ; Mathematics education ; Medical education ; Chemistry ; Medicine ; Social psychology Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Bing Zheng | Yanchang Zhang | Liying Cui | Jun Duan | Bowen Sun | Danning Sun | Miao Du | Qian Wang
Source: Research Square (preprint, 2025-08-07)
Field: Materials Science — Materials Chemistry

BiLayerStacker: A Universal and Global Program for Determining Ground-State Stacking Configurations of Bilayer 2D Nanomaterials Stacking ; Monolayer ; Materials science ; Ground state ; Bilayer ; Nanomaterials ; Condensed matter physics ; Nanotechnology ; Physics ; Chemistry ; Quantum mechanics ; Membrane Machine Learning in Materials Science ; Graphene research and applications ; Molecular Junctions and Nanostructures
Authors: Javier
E. Alfonso-Ramos (20651656) | Carlo Adamo (1268928) | Éric Brémond (1325355) | Thijs Stuyver (1966315)
Source: Figshare (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

CYCLO70: A NewChallenging Pericyclic BenchmarkingSet for Kinetics and Thermochemistry Evaluation Here, a new challenging benchmarking data set for cycloaddition reactions, CYCLO70, is presented and analyzed. CYCLO70 has been generated with the specific aim of being representative of the most challenging regions of the chemical reaction space surrounding Diels–Alder, dipolar cycloadditions, and (sigmatropic) rearrangement reactions with the help of an active learning approach. Testing 93 different functionals, spanning from spin-local density approximation to the most recent double-hybrid functionals, we observe that the errors on CYCLO70 are significantly bigger than those on the cycloaddition subset of BH9, the most popular benchmarking data set for this reaction class. Furthermore, we observe that the range-separated hybrid ωB97M-V is the best performing functional to model barrier heights and reaction energies, with a deviation closest to the desirable “chemical accuracy”; among the double hybrids, PBE-QIDH performs best, and among the fixed-range hybrids, M06–2X and r<sup>2</sup>SCAN0 emerge as the most balanced in terms of simultaneously reproducing both properties. Next, we perform a principal component analysis on the errors across the data set and demonstrate not only that the errors across different functional approximations correlate to a significant extent (the first two components explain 98% of the variance), but we also observe that functionals belonging to the same rung of Jacob’s ladder cluster together in the constructed two-dimensional plot. These results were further validated on a set of Diels–Alder reactions relevant to self-healing polymer design, reinforcing the practical relevance of CYCLO70. Benchmarking ; Set (abstract data type) ; Thermochemistry ; Principal component analysis ; Relevance (law) ; Data set ; Pericyclic reaction ; Kernelization ; Cycloaddition Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Synthesis and Properties of Aromatic Compounds
Authors: Krzysztof Szajek | Wojciech Sumelka
Source: Scientific Reports (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Surrogate model for statics of fractional thin bar element and its equivalence with mass-spring metamaterial The paper presents a two-level metametrial homogenization procedure, in which the mechanical behavior of a bar with non-uniform stiffness profile is considered. The connection between the microstructure (spring-mass model) and the surrogate model (simple spring model) is done by an intermediate space-fractional bar element (FBE) modeled utilizing fractional calculus (FC). The impact of the FC parameters, namely a length scale and a material order, on the global stiffness of the bar was analyzed based on 6000 numerical analyses, including various stiffness profiles - distributions of Young's modulus and the cross-sectional area along the element axis. The study reveals that changes in FC parameters significantly influence the stiffness of the resulting bar (up to 20%), and the impact of the change on the stiffness is very difficult to predict, highlighting the need for a detailed analysis. The presented approach with the model that mimics the microstructure efficiently opens a way to analyze large lattice or chain structures expressing scale effect and then to look for their optimal design. Stiffness ; Statics ; Homogenization (climate) ; Finite element method ; Bar (unit) ; Microstructure ; Equivalence (formal languages) ; Metamaterial ; Modulus Nonlocal and gradient elasticity in micro/nano structures ; Composite Material Mechanics ; Composite Structure Analysis and Optimization
Authors: Yi Cao | Paulette Clancy
Source: arXiv (Cornell University) (preprint, 2025-08-27)
Field: Materials Science — Materials Chemistry

Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields Machine-learned force fields (MLFFs), especially pre-trained foundation models, are transforming computational materials science by enabling ab initio-level accuracy at molecular dynamics scales. Yet their rapid rise raises a key question: should researchers train specialist models from scratch, fine-tune generalist foundation models, or use hybrid approaches? The trade-offs in data efficiency, accuracy, cost, and robustness to out-of-distribution failure remain unclear. We introduce a benchmarking framework using defect migration pathways, evaluated through nudged elastic band trajectories, as diagnostic probes that test both interpolation and extrapolation. Using Cr-doped Sb2Te3 as a representative two-dimensional material, we benchmark multiple training paradigms within the MACE architecture across equilibrium, kinetic (atomic migration), and mechanical (interlayer sliding) tasks. Fine-tuned models substantially outperform from-scratch and zero-shot approaches for kinetic properties but show partial loss of long-range physics. Representational analysis reveals distinct, non-overlapping latent encodings, indicating that different training strategies learn different aspects of system physics. This framework provides practical guidelines for MLFF development and establishes migration-based probes as efficient diagnostics linking performance to learned representations, guiding future uncertainty-aware active learning. Benchmarking ; Robustness (evolution) ; Benchmark (surveying) ; Generalist and specialist species ; Foundation (evidence) ; Training set Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Nuo Wang | Qiming Qian | Jingzheng Ren | Chang He
Source: Journal of environmental chemical engineering (article, 2025-08-25)
Field: Chemistry — Inorganic Chemistry

Integrated carbon capture and methanation for valorisation of sludge: Process development, optimization and performance evaluation Valorisation ; Methanation ; Process (computing) ; Process development ; Carbon fibers ; Environmental science ; Waste management ; Process engineering ; Pulp and paper industry ; Chemistry ; Engineering ; Materials science ; Computer science Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Wenbo Sun (531308) | Tammo van der Heide (16430408) | Van-Quan Vuong (11896003) | Thomas Frauenheim (1289043) | Michael A. Sentef (14365215) | Bálint Aradi (1524193) | Carlos R. Lien-Medrano (15212407)
Source: Figshare (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Hybrid FunctionalDFTB Parametrizations for ModelingOrganic Photovoltaic Systems Density functional tight binding (DFTB) is a quantum chemical simulation method based on an approximate density functional theory (DFT), known for its low computational cost and comparable accuracy to DFT. For several years, the application of DFTB in organic photovoltaics (OPV) has been limited by the absence of an appropriate set of parameters that adequately account for the relevant elements and necessary corrections. Here we have developed new parametrizations using hybrid functionals, including B3LYP and CAM-B3LYP, for OPV applications within the DFTB method in order to overcome the self-interaction error present in DFT functionals lacking long-range correction. These parametrizations encompass electronic and repulsive parameters for the elements H, C, N, O, F, S, and Cl. A Bayesian optimization approach was employed to optimize the free atom eigenenergies of unoccupied shells. The effectiveness of these new parametrizations was evaluated by a data set of 12 OPV donor and acceptor molecules, showing consistent performance when compared with their corresponding DFT references. Frontier molecular orbitals and optimized geometries were examined to evaluate the performance of the new parametrizations in predicting ground-state properties. Furthermore, the excited-state properties of monomers and dimers were investigated by means of real-time time-dependent DFTB (real-time TD-DFTB). The appearance of charge-transfer (CT) excitations in the dimers was observed, and the influence of alkyl side-chains on the photoinduced CT process was explored. This work paves the way for studying ground- and excited-state properties, including band alignments and CT mechanisms at donor–acceptor interfaces, in realistic OPV systems. Density functional theory ; Tight binding ; Hybrid functional ; Organic solar cell ; Bayesian optimization ; Acceptor ; Basis set ; Quantum ; Work (physics) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Chemical Physics Studies