jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huidi Zhang | Xiaonan Zhang | Jun Tang | Yaohan Li | Zhirong Zhang | Sheng Zhou
Source: Analytical Chemistry (article, 2025-10-03)
Field: Chemistry — Spectroscopy

Deep Learning-Enhanced Dual-Component Gas Sensor Based on Wavelength Modulation Spectroscopy Considering the challenge of qualitative and quantitative detection for gas mixtures caused by spectral overlap, a deep learning-enhanced dual-component gas sensor based on wavelength modulation spectroscopy (WMS) with the 2<i>f</i>/1<i>f</i> signals is proposed, achieving simultaneous detection of exhaled carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) concentrations using a single laser. A convolutional neural network (CNN)-based concentration prediction model (CPM) is introduced to address the cross-interference caused by the spectral overlap between gas molecules and to predict the concentration of each gas component accurately. Unlike traditional methods that collect a large number of labeled data from time-consuming experiments, a generative adversarial network (GAN) is used for the data augmentation of 2<i>f</i>/1<i>f</i> spectral signals, effectively addressing the issue of scarce experimental data for model training. The predicted concentrations are linearly fitted against the standard concentrations with high determination coefficients, demonstrating the strong feasibility and reliability of the proposed gas sensor. Allan deviation analysis indicates minimum detection limits of 17.34 ppm for CO<sub>2</sub> and 3.52 ppb for CH<sub>4</sub> at integration times of 112 and 159 s, respectively. Critically, the successful measurement of exhaled CO<sub>2</sub> and CH<sub>4</sub> concentrations using this sensor demonstrates its excellent performance in practical applications. This is a successful attempt to apply deep learning-enhanced WMS to dual-component gas detection in human breath, which provides guidance for simultaneous measurement of multicomponent gases and further paves the way for breath diagnosis. Methane ; Spectroscopy ; Wavelength ; Reliability (semiconductor) ; Analytical Chemistry (journal) ; Allan variance ; Modulation (music) ; Sensor fusion ; Standard deviation Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Hyeongmin Moon | Md Morshedur Rahman | Sang‐Duk Choi | Sarang Oh | Chang Kyu Park | Joonseok Koh
Source: Fibers and Polymers (article, 2025-08-11)
Field: Materials Science — Polymers and Plastics

Deep Learning Approach for Predicting the Physical Properties of Air-Jet Textured Yarn with PET/PTT Bicomponent Fiber Materials science ; Yarn ; Fiber ; Composite material ; Jet (fluid) ; Mechanics Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials
Authors: Ivan Dudakov | С. В. Савельев | Iurii M. Nevolin | Artem Mitrofanov | Vadim Korolev | Yulia G. Gorbunova
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Examining proton conductivity of metal–organic frameworks by means of machine learning The tunable structure of metal-organic frameworks (MOFs) is an ideal platform to meet contradictory requirements for proton exchange membranes: a key component of fuel cells. Nonetheless, rational design of proton-conducting MOFs remains a challenge owing to the intricate structure-property relationships that govern the target performance. In the present study, the modeling of quantities available for hundreds of MOFs was scaled up to many thousands of entities using supervised machine learning. The experimental dataset was curated to train multimodal transformer-based networks, which integrated crystal-graph, energy grid, and global-state embeddings. Uncertainty-aware models revealed superprotonic conductors among synthesized MOFs that have not been previously investigated for the application in question, thus highlighting magnesium-containing frameworks with aliphatic linkers as high-confidence candidates for experimental validation. Furthermore, classifiers trained on the activation energy threshold effectively discriminated between well-known proton conduction mechanisms, thereby providing physical insights beyond the black-box routine. Thus, our findings prove high potential of data-driven materials design, which is becoming a valuable addition to experimental studies on proton-conducting MOFs. Conductivity ; Thermal conduction ; Proton ; Transformer ; Metal ; Metal-organic framework ; Materials science ; Computer science ; Artificial intelligence ; Chemical physics ; Chemistry ; Physics ; Physical chemistry ; Nuclear physics ; Quantum mechanics ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Daniel Marchand
Source: MRS Bulletin (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Foundation models for metallurgy? Abstract Foundation models appear to promise precision atomic modeling across the periodic table, requiring little more than “fine-tuning” with a few density functional theory calculations. However, it is not clear whether they are sufficiently accurate, even with fine-tuning, for materials modeling to justify their high compute cost. Here, we compare state-of-the art foundation models against a collection of “bespoke” neural network potentials for the Al–Cu–Mg–Zn system. While we find many foundation models to give poor or very poor results, some such as GRACE2L-OAM offer extremely good accuracy in most cases. We find that models trained on the defect-containing and higher k-point density “Alexandria” data set had much better performance than those trained on Materials Project data alone. Our results also indicate that thermal conductivity scores are a much better indicator of metallurgical performance than energy errors on the convex hull. Impact statement GPT4 established to the broad community that foundation models can achieve results that are impossible with hand-crafted or “bespoke” language models. There has now been a rush to see if this is also true for atomic modeling. Indeed even large corporations such as Microsoft, Meta, and Google have begun to develop large training sets and publishing results for universal atomic potentials. The promise is that these models should be accurate enough out of the box for most applications or at most, require a small amount of “fine-tuning” on targeted data. But how accurate are these models and can they be used in a production context for metallurgy? Here, we carefully validate a variety of top-performing foundation models on a comprehensive set of benchmarks applicable to aluminum metallurgy. While we find many are completely unsuitable, some, including the very recently released GRACE2L-OAM model, do indeed have sufficient accuracy for most applications. Our results show that the GRACE2L-OAM model offers similar and sometimes somewhat better accuracy than our previously developed neural network potentials. While foundation models do indeed seem to have found their place in the atomic modeling toolkit, they remain much more computationally expensive. Thus proper model selection is a tradeoff between desired accuracy, compute time, and user setup time. Graphical abstract Bespoke ; Foundation (evidence) ; Statement (logic) ; Computer science ; Set (abstract data type) ; Point (geometry) ; Industrial engineering ; Programming language ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: P. Cheng | Ling‐Feng Mao | Wenhao Shen | Yuling Yan
Source: Electronics (review, 2025-08-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromigration Failures in Integrated Circuits: A Review of Physics-Based Models and Analytical Methods Electromigration (EM), current-driven atomic diffusion in interconnect metals, critically threatens integrated circuit (IC) reliability via void-induced open circuits and hillock-induced short circuits. This review examines EM’s physical mechanisms, influencing factors, and advanced models, synthesizing seven primary determinants: current density, temperature, material properties, microstructure, geometry, pulsed current, and mechanical stress. It dissects the coupled contributions of electron wind force (dominant EM driver), thermomigration (TM), and stress migration (SM). The review assesses four foundational modeling frameworks: Black’s model, Blech’s criterion, atomic flux divergence (AFD), and Korhonen’s theory. Despite advances in multi-physics simulation and statistical EM analysis, achieving predictive full-chip assessment remains computationally challenging. Emerging research prioritizes the following: (i) model order reduction methods and machine-learning solvers for verification of EM in billion-scale interconnect networks; and (ii) physics-informed routing optimization to inherently eliminate EM violations during physical design. Both are crucial for addressing reliability barriers in IC technologies and 3D heterogeneous integration. Electromigration ; Integrated circuit ; Interconnection ; Electronic circuit ; Reliability (semiconductor) ; Electronic engineering ; Physics of failure ; Hillock ; Physical design ; Chip ; Computer science ; Engineering physics ; Engineering ; Nanotechnology ; Mechanical engineering ; Materials science ; Electrical engineering ; Physics ; Telecommunications Copper Interconnects and Reliability ; Corrosion Behavior and Inhibition ; Electronic Packaging and Soldering Technologies
Authors: Tian-Xing Yang | Peng Dou
Source: Journal of Iron and Steel Research International (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Studying corrosion resistance of ODS steels in supercritical water by machine learning Supercritical fluid ; Corrosion ; Metallurgy ; Materials science ; Resistance (ecology) ; Chemistry ; Agronomy ; Biology Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Utkarsh Bhardwaj | Vinayak Mishra | Suman Mondal | M. Warrier
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

A robust machine learned interatomic potential for Nb: collision cascade simulations with accurate non-equilibrium properties Abstract Niobium (Nb) and its alloys are extensively used in various technological applications owing to their favorable mechanical, thermal and irradiation properties. Accurately modeling Nb under irradiation is essential for predicting microstructural changes, defect evolution, and overall material performance. Many classical interatomic potentials for Nb have found difficulty in predicting the correct self-interstitial atom (SIA) configuration, a critical factor in radiation damage simulations. We develop a machine learning interatomic potential (MLIP) within the spectral neighbor analysis potential (SNAP) framework. The potential was trained on a high-fidelity dataset generated from ab initio density functional theory (DFT) calculations. This dataset was refined using diversity-based selection algorithms, and the MLIP was developed through cross-validation combined with multivariate hyperparameter optimization. The developed MLIP accurately captures a wide range of material properties, particularly the non-equilibrium properties crucial for radiation damage simulations, such as threshold displacement energies, relative stabilities of various SIA configurations, edge dislocation loop stability, and close pair-potential interactions. The resulting MLIP reproduces DFT-level accuracy while maintaining computational efficiency for large-scale molecular dynamics (MD) simulations. Through a series of validation tests involving elastic, thermal, and defect properties—including high energy collision cascade simulations—we show that our SNAP potential performs very well for radiation damage studies, and resolves persistent limitations present in earlier embedded atom method and Finnis–Sinclair potentials. It shows competitive advantage in accuracy and efficiency aspects compared to other MLIP and modern semi-empirical potential. Using detailed statistical results of dumbbell orientations formed in collision cascades carried out using the developed MLIP and three other interatomic potentials, we show the differences in formation energies have drastic effect on the defect configurations at primary damage produced in a collision cascade. Our developed potential accurately captures the relative stability of all defect configurations of Nb, its threshold displacement energy and other equilibrium properties offering a robust tool for predictive irradiation studies. Interatomic potential ; Cascade ; Molecular dynamics ; Dumbbell ; Collision ; Radiation damage ; Dislocation ; Atom (system on chip) ; Range (aeronautics) Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Yuanyuan Jiang | Yuanyuan Wang | Xuejiao Li | Yu Gong
Source: The Journal of Physical Chemistry B (article, 2025-07-15)
Field: Chemistry — Inorganic Chemistry

Temperature Extensible Deep Potential Model for Molten NaF–BeF<sub>2</sub>–ZrF<sub>4</sub>: Predicting Transport Properties and Local Structure Molten NaF-BeF<sub>2</sub>-ZrF<sub>4</sub> (FNaBeZr) has garnered significant interest as one of the potential nuclear fuel carrier salts for molten salt reactors. Here, deep potential molecular dynamic (DPMD) simulations by integrating first-principles calculation, machine learning, and molecular dynamic methods are employed to systematically investigate the transport properties and local structures of molten FNaBeZr across a broad temperature range. Such computational approaches will bypass resource-intensive trial-and-error experimentation and massive high-fidelity density functional theory (DFT) calculations, which represents that the DPMD framework achieves dual optimization by substantially reducing computational costs while enabling enhanced system scalability─a critical advancement for modeling complex coordination chemistry environments and deciphering coupled ionic transport phenomena at extended spatiotemporal scales. The trained deep potential model reproduces the densities, ionic self-diffusion coefficients, and pair/cluster structures from 773 to 973 K and successfully predicts these properties as well as the ionic conductivity and shear viscosity at extended temperatures (beyond the training range). Furthermore, the quantifiable correlations between structural features (e.g., first-peak height/position and coordination number) and temperature are established, and their latent connections to transport properties are also analyzed. The robust DPMD results demonstrate the temperature extensibility of the deep potential for molten FNaBeZr and provide critical technical parameters and engineering data for future simulations of molten fluorides and for the design optimization of pump systems and reprocessing infrastructure in high-temperature environments. Multiscale modeling ; Ionic liquid ; Molecular dynamics ; Molten salt ; Ionic bonding ; Chemistry ; Scalability ; Thermodynamics ; Materials science ; Chemical physics ; Computer science ; Ion ; Computational chemistry ; Physics Inorganic Chemistry and Materials ; Inorganic Fluorides and Related Compounds ; Machine Learning in Materials Science
Authors: Arash Vaghef‐Koodehi
Source: Ceramics International (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Processing–structure–property relationships in nanostructured ZnO varistors: Effects of advanced sintering and additive design Spark plasma sintering ; Sintering ; Varistor ; Grain boundary ; Taguchi methods ; Nanoscopic scale ; Nanostructure ; Oxide ZnO doping and properties ; Lightning and Electromagnetic Phenomena ; High-Voltage Power Transmission Systems
Authors: Yuhang Sun | Qiong Wang | Zhitong Yao | Zhiyuan Fu | Xuewen Han | Rongrong Si | Wei Qi | Junwen Pu
Source: International Journal of Biological Macromolecules (article, 2025-03-08)
Field: Materials Science — Biomaterials

Targeted conversion of cellulose and hemicellulose macromolecules in the phosphoric acid/acetone/water system: An exploration of machine learning evaluation and product prediction Hemicellulose ; Cellulose ; Macromolecule ; Phosphoric acid ; Acetone ; Chemistry ; Product (mathematics) ; Organic chemistry ; Chemical engineering ; Biochemistry ; Engineering Advanced Cellulose Research Studies ; Biofuel production and bioconversion
Authors: Oluwayomi J. Oyejide | Faiz Ahmad | Shahrul Kamaruddin | Modestus O. Okwu | Francis Amadhe | Abdul Basıt | Rilwan Anjorin | Mohammed El Faghi Abdulmalek
Source: Knowledge-Based Systems (article, 2025-06-23)
Field: Chemistry — Analytical Chemistry

Novel stack-model configuration for Merox-treated gasoline yield prediction synergized with EMMS-CFD hydrodynamic analysis Computational fluid dynamics ; Stack (abstract data type) ; Yield (engineering) ; Gasoline ; Engineering ; Computer science ; Materials science ; Aerospace engineering ; Metallurgy ; Waste management ; Operating system Petroleum Processing and Analysis ; Heat transfer and supercritical fluids ; Fluid Dynamics and Mixing
Authors: Mohsen Dehbashi | Anna Kaźmierczak-Bałata | Jerzy Bodzenta
Source: International Journal of Heat and Mass Transfer (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

An approach for thermal conductivity measurements in thin films: Combining localized surface topography, thermal analysis, and machine learning techniques Thermal conductivity ; Materials science ; Thermal ; Surface (topology) ; Thin film ; Thermal conductivity measurement ; Composite material ; Thermodynamics ; Nanotechnology ; Physics ; Geometry ; Mathematics Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Adhesion, Friction, and Surface Interactions
Authors: Ignacio Pickering | Jiangeng Xue | Kate Huddleston | Nicholas Terrel | Adrián E. Roitberg
Source: Journal of Chemical Information and Modeling (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

TorchANI 2.0: An Extensible, High-Performance Library for the Design, Training, and Use of NN-IPs In this work, we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. TorchANI 2.0 builds upon the foundation of its predecessor, while addressing its limitations and introducing new features. These changes greatly enhance its extensibility, performance, and suitability as a framework for developing models ready for molecular dynamics applications. These improvements include the introduction of a modular system to add arbitrary pairwise potentials to models, CUDA-accelerated optimization for faster and more memory-efficient calculation of local atomic features, and a batched system for better performance of network ensembles, among others. Our benchmarks demonstrate that TorchANI 2.0 achieves significant speedup over previous versions in both training and inference, and the library enhancements allow users to train physically constrained models that better represent important qualities of chemical systems. We demonstrate this by introducing three new ANI models that incorporate these features and evaluating their capabilities. Pairwise comparison ; Modular design ; Speedup ; Software ; Deep learning ; Modularity (biology) ; Software package Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Andrew C. Powers | P. Douglas Renfrew | Parisa Hosseinzadeh | Vikram Khipple Mulligan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-27)
Field: Chemistry — Physical and Theoretical Chemistry

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Backbone Sampling Macrocycles are a promising therapeutic class. The incorporation of heterochiral and non-natural chemical building-blocks presents challenges for rational design, however. With no existing machine learning methods tailored for heterochiral macrocycle design, we developed a novel convolutional autoencoder model to rapidly generate energetically favorable macrocycle backbones for heterochiral design and structure prediction. Our approach surpasses the current state-of-the-art method, Generalized Kinematic loop closure (GenKIC) in the Rosetta software suite. Given the absence of large, available macrocycle datasets, we created a custom dataset in-house and <i>in silico</i>. Our model, CyclicCAE, produces energetically stable backbones and designable structures more rapidly than GenKIC. It enables users to perform energy minimization, generate structurally similar or diverse inputs via MCMC, and conduct inpainting with fixed anchors or motifs. We propose that this novel method will accelerate the development of stable macrocycles, speeding up macrocycle drug design pipelines. Autoencoder ; Chemistry ; Stereochemistry ; Sampling (signal processing) ; Computer science ; Artificial intelligence ; Deep learning ; Computer vision Crystallography and molecular interactions ; Chemical Reactions and Isotopes ; Chemical Synthesis and Analysis
Authors: M. Sepliarsky | F. Aquistapace | F. Di Rino | R. Machado | M. G. Stachiotti
Source: Physical review. B./Physical review. B (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Local-phase framework for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>BaTi</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> <mml:msub> <mml:mi>Zr</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:msub> <mml:mi mathvariant="normal">O</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> phase diagram: From ferroelectricity to dipolar glass We apply a first-principles-based atomistic model to investigate the ${\mathrm{BaTi}}_{1\ensuremath{-}x}Z{r}_{x}{O}_{3}$ phase diagram, focusing on both macroscopic and local structural changes. Our approach, which combines molecular dynamics with machine learning techniques, accurately captures the influence of Ti and Zr cations on their local environment and its evolution with composition and temperature. The computed phase diagram shows excellent agreement with existing experimental and theoretical data. Beyond reproducing known results, our analysis reveals that the behavior of the solid solution across different compositions and temperatures can be understood in terms of coexisting Ti cells with different symmetries, whose stability depends on the local B-site configuration. This local-phase-based approach provides a unified description of the distinct regions of the solid solution---including ferroelectric, relaxor, and dipolar glass phases---and captures the continuous evolution from one regime to another. Our findings demonstrate how atomic-level distortions drive the complex macroscopic behavior of the material. Phase diagram ; Ferroelectricity ; Stability (learning theory) ; Phase (matter) ; Dipole ; Solid solution ; Molecular dynamics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Chemical and Physical Properties of Materials