jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shreshth A. Malik | Tiarnan A. S. Doherty | Benjamin Colmey | Stephen Roberts | Yarin Gal | Paul A. Midgley
Source: Nature Communications (article, 2026-04-11)
Field: Materials Science — Surfaces, Coatings and Films

Hybrid physics-machine learning models for quantitative electron diffraction refinements High accuracy electron microscopy simulations required for quantitative crystal structure refinements face a fundamental challenge: while physical interactions are well-described theoretically, real-world experimental effects are challenging to model analytically. To address this gap, we present a hybrid physics-machine learning framework that integrates differentiable physical simulations with neural networks. By leveraging automatic differentiation throughout the simulation pipeline, our method enables gradient-based joint optimization of physical parameters and neural network components representing experimental variables, offering superior scalability compared to traditional second-order methods. We demonstrate this framework through application to three-dimensional electron diffraction (3D-ED) structure refinement, where our approach learns complex thickness distributions directly from diffraction data rather than relying on simplified geometric models. This method achieves state-of-the-art refinement performance across synthetic and experimental datasets, recovering atomic positions, thermal displacements, and thickness profiles with high fidelity. The modular architecture proposed can naturally be extended to accommodate additional physical phenomena and extended to other electron microscopy techniques. This establishes differentiable hybrid modeling as a powerful paradigm for quantitative electron microscopy, where experimental complexities have historically limited analysis. Modular design ; Scalability ; Artificial neural network ; Diffraction ; Differentiable function ; Experimental data ; Face (sociological concept) ; Thermal Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: V.G. Petruk | S.M. Kvaterniuk | D.R. Latusha | M.P. Maksymenko | S.V. Gavadza
Source: Visnyk of Vinnytsia Politechnical Institute (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Selective Graphene-Based Biosensors in Environmental Monitoring The paper provides a comprehensive review of the physicochemical properties of various graphene forms (CVD graphene, GO, rGO, LIG), transduction mechanisms, and surface functionalization strategies, specifically focusing on DNA aptamers. Mathematical modeling methods were employed to analyze the performance of a graphene field-effect transistor (GFET) biosensor. The calculations are based on the Dirac equation for massless fermions, the drift-diffusion model of conductivity, and the Hill-Langmuir isotherm, accounting for the effects of graphene’s quantum capacitance. Analytical characteristics of graphene-based sensors for the detection of heavy metals, pesticides, and pathogens are systematized. The developed GFET mathematical model enabled a quantitative assessment of the Debye screening effect on device sensitivity in liquid media. It was established that to prevent signal loss, the receptor layer (aptamer) length should be 2...3 nm, while the solution ionic strength must not exceed 10 mM. An optimal stability window (pH 6.0...6.2) was identified to prevent aptamer denaturation and metal ion hydrolysis. Modeling results confirmed a potential limit of detection (LOD) within the range of 1.5...2.0 nM, and the calculated selectivity coefficient demonstrates high sensor specificity for lead ions even in the presence of background electrolytes. The findings substantiate the prospects of graphene biosensors as an alternative to conventional analytical methods. It is shown that integrating GFET arrays with Internet of Things (IoT) technologies and artificial intelligence algorithms (neural networks, deep learning) facilitates the development of high-performance systems for continuous, real-time environmental monitoring of water quality and ambient air. Graphene ; Aptamer ; Biosensor ; Sensitivity (control systems) ; Ionic strength ; Detection limit ; Surface modification ; SIGNAL (programming language) Graphene research and applications ; Advanced biosensing and bioanalysis techniques ; Nanopore and Nanochannel Transport Studies
Authors: Shan He | Jiajia Yu | Ningyuan Yang | Gaoshen Cai
Source: Journal of Food Measurement & Characterization (article, 2026-02-26)
Field: Chemistry — Analytical Chemistry

Non-destructive prediction of strawberry soluble solids content using hyperspectral imaging and deeplearning model Hyperspectral imaging ; Deep learning ; Test set ; Pattern recognition (psychology) ; Weighting ; Visualization ; Key (lock) ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Junxiao Li | Wei Liu | Sizhe Lin | Changlong Li | Jiacheng Song | Zhiwei Liu | Yue Hu | Shuchen Zhang
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Co-designing growth factors for the synthesis of (6, 5)-enriched single-walled carbon nanotube horizontal arrays Precise control over the diameter and chirality of single-walled carbon nanotubes (SWNTs) is critical for scalable electronic applications, yet it remains a challenge for horizontally aligned arrays. Although low-temperature powder synthesis routinely yields sub-1 nm SWNTs with relatively high chirality selectivity, directly transferring these growth conditions to substrate-guided horizontal arrays has been hindered by poorly understood catalyst evolution and coupled growth factors (gas, temperature, pressure <i>etc</i>). Here, we employ a machine-learning-assisted framework that integrates systematic experiments with interpretable models to optimize the growth parameters, enabling sub-1 nm SWNT growth in horizontal arrays at low temperatures. Our ML model reveals that SWNT formation is governed by kinetic competition between amorphous-carbon deposition and graphitized-carbon incorporation, with catalytic cracking acting as the dominant carbon source under low-temperature conditions. Further, we successfully achieve horizontally aligned SWNT arrays enriched in (6, 5) nanotubes with a chiral selectivity of approximately 70% at 650 °C under an optimized environment predicted by the ML model. This is the first demonstration of sub-1 nm chiral-selective growth in horizontal arrays at such a low temperature. This work bridges the long-standing gap between powder and horizontal-array growth and establishes a rational, data-driven strategy for the deterministic synthesis of SWNTs. Carbon nanotube ; Chirality (physics) ; Carbon fibers ; Nanotube ; Carbon nanotube actuators ; Scalability Carbon Nanotubes in Composites ; Graphene research and applications ; Synthesis and Properties of Aromatic Compounds
Authors: Seiki Saito | Keisuke TAKEUCHI | Hiroaki Nakamura | Yasuhiro Oda | Kazuo Hoshino | Yuki Homma | S. Yamoto | Yuki Uchida
Source: arXiv (Cornell University) (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Development of a 3D-CNN-based Prediction Model for Migration Barriers in Plasma-Wall Interactions Understanding the long-term transport of hydrogen isotopes in plasma-facing materials, such as tungsten, is critical for the steady-state operation of magnetic confinement fusion reactors. However, dynamically updating the transition parameters for kinetic Monte Carlo (kMC) simulations as the atomic structure evolves under continuous plasma irradiation remains a severe computational bottleneck. Conventionally, calculating these migration barriers requires the iterative and computationally expensive Nudged Elastic Band (NEB) method. To overcome this limitation, this article presents a highly efficient surrogate model for predicting migration barriers using a three-dimensional Convolutional Neural Network (3D-CNN), establishing the final component necessary to realize on-the-fly molecular dynamics (MD) and kMC hybrid simulations. The proposed deep learning model takes a two-channel volumetric input, the local three-dimensional potential energy distribution and the voxelized spatial coordinates of the initial and final trapping sites, to directly output the migration barrier as a scalar value. Trained on a comprehensive dataset of tungsten-hydrogen configurations evaluated using the Embedded Atom Method (EAM) potential, the model demonstrated robust predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.124 eV and a high coefficient of determination of 0.890. Furthermore, utilizing GPU acceleration, the inference time is reduced to approximately 2.7 milliseconds per barrier, achieving a speed-up ratio of over 23,000 compared to conventional NEB calculations. This extraordinary acceleration effectively resolves the computational barrier of transition rate evaluations, paving the way for large-scale, dynamic modeling of plasma-wall interactions. Convolutional neural network ; Scalar (mathematics) ; Inference ; Monte Carlo method ; Acceleration ; Molecular dynamics ; Artificial neural network ; Fusion Fusion materials and technologies ; Magnetic confinement fusion research ; Laser-Plasma Interactions and Diagnostics
Authors: Nanxi Chen | Airong Chen | Rujin Ma
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Physics-informed neural operator for predictive parametric phase-field modelling Predicting the microstructural and morphological evolution of materials through phase-field modelling is computationally intensive, particularly for high-throughput parametric studies. While neural operators such as the Fourier neural operator (FNO) show promise in accelerating the solution of parametric partial differential equations (PDEs), the lack of explicit physical constraints, may limit generalisation and long-term accuracy for complex phase-field dynamics. Here, we develop a physics-informed neural operator framework to learn parametric phase-field PDEs, namely PF-PINO. By embedding the residuals of phase-field governing equations into the data-fidelity loss function, our framework effectively enforces physical constraints during training. We validate PF-PINO against benchmark phase-field problems, including electrochemical corrosion, dendritic crystal solidification, and spinodal decomposition. Our results demonstrate that PF-PINO significantly outperforms conventional FNO in accuracy, generalisation capability, and long-term stability. This work provides a robust and efficient computational tool for phase-field modelling and highlights the potential of physics-informed neural operators to advance scientific machine learning for complex interfacial evolution problems. Operator (biology) ; Parametric statistics ; Artificial neural network ; Embedding ; Benchmark (surveying) ; Limit (mathematics) ; Partial differential equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Z. Najeeb | L. Spillane | M. E. Schuster | A. Varambhia | D. Ozkaya
Source: Johnson Matthey Technology Review (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Autonomous Data Acquisition Pipeline for High Throughput Statistical Analysis of Catalyst Nanoparticles at Elevated Temperature Nanoparticles underpin a significant portion of the chemicals industry, with nanoparticle catalysts serving as some of the most extensively deployed technologies at scale. Most notably their performance can be directly linked to structural and compositional properties of the catalyst. Scanning transmission electron microscopy provides a comprehensive insight into a catalyst’s microstructure, however the technique is prone to manual and laborious data acquisition. As a result, it is difficult to perform this process over a statistically significant number of particles. This challenge grows when data must be acquired under industrially realistic operating conditions, such as at elevated temperatures and/or in oxidizing or reducing conditions. With recent developments in automation in the field, we present a framework for autonomous particle sizing and spectrum imaging acquisition which incorporates machine learning, programmable mask-based scanning, in-situ stimulus control, particle size distribution, and compositional analysis. This pipeline paves the way toward studying nanoparticle catalyst structure-property relationships both at scale and under operating conditions. Automation ; Pipeline (software) ; Sizing ; Data acquisition ; Nanoparticle ; Throughput ; Oxidizing agent Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Catalytic Processes in Materials Science
Authors: Yonatan Harnik | Nir Cohen | Shai Kendler | Nitzan Tzanani | Anat Milo
Source: ChemRxiv (article, 2026-02-16)
Field: Chemistry — Spectroscopy

Aligning Mass Spectrometry with Molecular Representations for Guiding de novo Structure Prediction Interpreting mass spectrometry (MS) data is challenging because reconstructing molecular structures from fragment patterns is an inherently ambiguous task, even for experienced analysts. Recent advances in MS instrumentation and acquisition protocols have created new opportunities for data-driven structural elucidation by leveraging large amounts of experimental spectra. This work presents DeniMS, a deep learning framework for a de novo generation of molecular structures from tandem mass spectrometry (MS/MS) data. We use a contrastive learning strategy to train a model to produce a joint representation that aligns spectra with their corresponding molecular graphs, resulting in embeddings that organize MS data in a chemically meaningful manner. These MS embeddings then guide a discrete graph diffusion model to predict molecular structures directly from spectra. Rather than returning a single answer, the model produces a ranked list of plausible structures, which better reflects practical MS/MS analysis, where isomers and closely related compounds can yield similar spectra. The framework can also integrate multiple spectra per molecule, improving robustness by pooling complementary evidence across measurements. We train and evaluate the method on the open FragHub dataset and release the code and pretrained models at https://github.com/Milo-group/DeniMS. Pooling ; Robustness (evolution) ; Tandem mass spectrometry ; Graph ; Mass spectrometry ; Representation (politics) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: P. V. Stishenko | Qian Chen | Julia Westermayr | Reinhard Maurer | Andrew J. Logsdail
Source: ChemRxiv (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to be studied. Here, we demonstrate how contributions from system-specific electronic structure machine learning (ESML) models may be combined (“stitched”) to deliver density matrices of entire systems of interest, improving the initial guess for the self-consistent field cycle and delivering gains in computational efficiency. The “stitching” of density matrices is demonstrated for sequential calculations, such as geometry optimization and molecular dynamics, and we show that the synergistic use of ESML models and density matrix extrapolation algorithms can accelerate standard computational calculations. The algorithms are demonstrated for test cases relating to water clusters and a methane clathrate cage, with the benefits discussed. The future opportunities for hybrid quantum mechanical and ML (QM/ML), and also ML/ML paradigms, are broad-ranging with significant computational speed-up attainable. Extrapolation ; Ab initio ; Field (mathematics) ; Density matrix ; Density functional theory ; Matrix (chemical analysis) ; Transferability ; Electronic structure Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: M.R. Alipoor | M. Eshghi
Source: Annals of Nuclear Energy (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Artificial neural network approach for optimizing X-ray shielding by leveraging the photoelectric absorption edge Electromagnetic shielding ; Attenuation ; Artificial neural network ; Absorption (acoustics) ; Attenuation coefficient ; Photon ; Photoelectric effect ; Radiation shielding Radiation Shielding Materials Analysis ; Radiation Therapy and Dosimetry ; Graphite, nuclear technology, radiation studies
Authors: Sahar Yasami | Hamid Khoeini | Majid Abdouss
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-Driven Analysis of Factors Influencing Capacitance and Lifetime in Graphene Supercapacitors: A Review and modelling Workflow ; Categorical variable ; Capacitance ; Exploit ; Artificial neural network ; Dimensionality reduction ; Realization (probability) ; Feature (linguistics) ; Supercapacitor Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Xü Liang | Suqin Wu | Pengcheng Yan | Hongping Li | Lei Xu | Henan Li
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

Ensemble machine learning enhanced Raman spectroscopy for high accuracy and noise robust classification of carbamate pesticides Support vector machine ; Raman spectroscopy ; Random forest ; Leverage (statistics) ; Noise (video) ; Ensemble learning ; Pattern recognition (psychology) ; Extreme learning machine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Zahra Khatibi | Mariano, Lorenzo A. | Lion Frangoulis | Alessandro Lunghi
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Efficient training of generative models from multireference simulations and its application to the design of Dy complexes with large magnetic anisotropy Generative machine learning models can potentially provide direct access to novel and relevant portions of the full chemical space, overcoming the cost of systematic sampling. However, the training of these models generally requires a large amount of data, often precluding the use of expensive high-level ab initio simulations for this task. The generation of coordination compounds of Dy with large magnetic anisotropy represents a topical example, where multireference simulations of large molecules are necessary to perform reliable predictions. Here, we show that a semi-supervised chemically-inspired training-by-proxy of generative variational autoencoders can reduce the cost associated with building a training set from multireference simulations by two orders of magnitude. We illustrate the power of this approach by generating 100s of new organic ligands for Dy(III) pentagonal bipyramidal complexes exhibiting record values of magnetic anisotropy, while starting from datasets as small as 1k multireference calculations. This work thus paves the way to the computational generation of molecules as complex coordination compounds with target electronic and magnetic properties. Ab initio ; Generative grammar ; Set (abstract data type) ; Anisotropy ; Ab initio quantum chemistry methods ; Work (physics) Machine Learning in Materials Science ; Magnetism in coordination complexes ; Computational Drug Discovery Methods
Authors: Guang-Nan Xing | Teng-Ao Yang | Jun-Chen Li | Suna Fan | Qixuan Fu | Zhiguang Song | Guodong Feng
Source: Analytical Chemistry (article, 2026-03-18)
Field: Chemistry — Organic Chemistry

A Fluorescent Palladium–Ligand Platform for In Situ Monitoring and Bayesian Optimization of Sonogashira Coupling Reactions Palladium-catalyzed coupling reactions are indispensable tools for carbon-carbon bond formation, yet their optimization and real-time monitoring remain challenging in homogeneous catalytic systems. Herein, we report a multifunctional fluorescent ligand, (<i>E</i>)-<i>N</i>'-((1<i>H</i>-benzo[<i>d</i>][1,2,3]triazol-6-yl)methylene)-4-(7-(diethylamino)-2-oxo-2<i>H</i>-chromen-3-yl)benzohydrazide (BTACB), which integrates palladium coordination, catalytic activity, and intrinsic fluorescence reporting within a single molecular framework. BTACB exhibits strong fluorescence emission at 518 nm and undergoes efficient and selective quenching upon coordination with Pd (II). The BTACB-Pd (II) complex is an effective catalyst for Sonogashira coupling. By combining the in situ fluorescence monitoring system with the Bayesian optimization-random forest model, the optimal reaction conditions can be quickly determined using the time-resolved fluorescence data derived from experiments. Under the optimized conditions, the coupling reaction continued with a yield as high as 99%. It is worth noting that the fluorescence of BTACB is strongly suppressed during the catalytic process and recovers after the reaction is completed, thus achieving continuous, noninvasive, and real-time monitoring of the catalytic process. This work presents a universal strategy in which the catalytically active fluorescent ligand serves as the reaction medium simultaneously. The in situ fluorescence monitoring and machine-learning-assisted optimization data interface provide a new paradigm for the intelligent regulation of palladium catalytic reactions. Sonogashira coupling ; Fluorescence ; Catalysis ; In situ ; Palladium ; Coupling reaction ; Fluorophore Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Click Chemistry and Applications