jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zayan Ahsan Ali | Preeti Tewatia | Oliver Waldmann
Source: Magnetochemistry (article, 2026-02-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspectives of Machine Learning for Ligand-Field Analyses in Lanthanide-Based Single Molecule Magnets Lanthanide-based single-molecule magnets are promising candidates for potential applications. Their magnetism is governed by ligand-field splittings, which may require up to 27 ligand-field parameters for accurate modeling. Determining these parameters reliably from measured data is a major challenge, for which machine learning approaches offer promising solutions. We provide an overview of these approaches and present our perspective on addressing the inverse problem relating experimental data to ligand-field parameters. Previously, a machine learning architecture combining a variational autoencoder (VAE) and an invertible neural network (INN) showed promise for analyzing temperature-dependent magnetic susceptibility data. In this work, the VAE-INN model is extended through data augmentation to enhance its tolerance to common experimental inaccuracies. Focusing on second-order ligand-field parameters, diamagnetic and molar-mass errors are incorporated by augmenting the training dataset with experimentally motivated error distributions. Tests on simulated experimental susceptibility curves demonstrate substantially improved prediction accuracy and robustness when the distributions correspond to realistic error ranges. When applied to the experimental susceptibility curve of the complex Al2IIIEr2III, the augmented VAE–INN recovers ligand-field solutions consistent with least-squares benchmarks. The proposed data augmentation thus overcomes a key limitation, bringing the ML approach closer to practical use for higher-order ligand-field parameters. Robustness (evolution) ; Experimental data ; Artificial neural network ; Autoencoder ; Magnet ; Magnetism ; Diamagnetism ; Inverse problem Magnetism in coordination complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Leila Kerdja | Yazid Fizi | Benoît Vieille | Boualem Keskes
Source: International Journal of Impact Engineering (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Inverse identification of material properties using artificial neural network - particle swarm optimization (ANN-PSO) in Taylor impact testing Particle swarm optimization ; Artificial neural network ; Inverse ; Identification (biology) ; Material properties ; Inverse problem High-Velocity Impact and Material Behavior ; Machine Learning in Materials Science ; Composite Material Mechanics
Authors: Ramindu P. de Silva | Wei Ge | Carolin Bapp | A. Zainelabdeen A. Mustafa | Robert Chapman | Yanan Fan | Scott A. Sisson | Martina H. Stenzel
Source: ACS Polymers Au (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model Understanding the interaction between polymers and proteins is of interest for researchers in medicine, biology, food science, and water treatment, among other fields. The goal may be to create strong interactions with enzymes to improve their catalytic stability, while in nanomedicine and biomedical engineering, the focus is often on reducing protein adsorption on polymer surfaces. Researchers have developed libraries of polymers with various monomer combinations and tested their binding to different proteins to better understand these interactions. In this work, we aimed to identify the polymer with the highest or lowest binding affinity to all proteins, respectively, using Gaussian Process Regression (GPR). However, incorporating categorical features such as the type of monomer has not been widely applied in GPR. Here we compare a range of process models, which were coined Multiplicative kernel, Additive kernel, Easy to interpret Gaussian Process model (EzGP), Latent Variable Gaussian Processes (LVGP), and the Latent Map Gaussian Processes (LMGP) by their developers. The LVGP model was found to perform best on the polymer-protein data set, where the output for binding strength was given by Förster resonance energy transfer (FRET), which can be used to help generate large data sets for machine learning (ML). The polymer that had the highest affinity to glucose oxidase (GOx), uricase (Uri), casein (Cas), trypsin (Trp), carbonic anhydrase (CAn) and bovine serum albumin (BSA) carried positive charges as well as hydrophobic benzyl groups. Negatively charged monomers dominated the polymer that rejected the most proteins intermixed with some cationic units, reminiscent of zwitterionic polymers. Categorical variable ; Polymer ; Monomer ; Polymer adsorption ; Affinities ; Feature selection ; Latent variable ; Protein–protein interaction Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Chunlin Luo | Yujuan Chen | Austin Braniff | Md Abdullah Al Masud | Yuhe Tian | Belachew Desta | Yuxin Wang | Jianli Hu
Source: Chemical Engineering Journal (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

Microwave catalysis for platform chemicals synthesis and waste upcycling Catalysis ; Microwave chemistry ; Microwave ; Scalability ; Green chemistry ; Bridging (networking) ; Waste heat ; Heterogeneous catalysis Microwave-Assisted Synthesis and Applications ; Ammonia Synthesis and Nitrogen Reduction ; Catalysts for Methane Reforming
Authors: Chuntao Guo | Jing Lin | Shunxing Bao | Xin Liu | Yaru Wang | Yunlin Chen
Source: Sensors (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

A Vision-Based Deep Learning Framework for Monitoring and Recognition of Chemical Laboratory Operations Standardized operating procedures are essential for ensuring safety and reproducibility in chemical laboratory experiments. However, real-time monitoring of manual laboratory operations, such as pipetting, remains challenging due to complex human-tool interactions, temporal dependencies between procedural steps, and operator variability. In this study, we propose a vision-based deep learning framework that leverages spatiotemporal features for automated monitoring of pipetting operations using non-contact visual sensing. Briefly, human poses and pipette interactions are extracted from video recordings using a YOLO-based perception model, while temporal execution patterns are captured through bidirectional long short-term memory networks. Experimental results demonstrate that the proposed approach can reliably distinguish between standard and non-standard pipetting behaviors across multiple predefined error categories and shows improved robustness compared with static or frame-level analysis. Overall, this work demonstrates the feasibility of vision-based AI systems for objective and scalable monitoring of laboratory pipetting operations, with potential applicability to other manual laboratory procedures. Robustness (evolution) ; Deep learning ; Scalability ; Pipette ; Visual inspection ; Pattern recognition (psychology) Machine Learning in Materials Science ; Chemical Safety and Risk Management ; Advanced Neural Network Applications
Authors: Rakesh Kumar Sharma | Vikram Singh | Kamlesh Jangid | Kapil Jangir
Source: Theoretical and Applied Fracture Mechanics (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Strain gradient elasticity analysis of mode-I crack in bi-directional functionally graded material using machine learning Elasticity (physics) ; Material properties ; Strain (injury) ; Functionally graded material Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Material Mechanics
Authors: Ziheng Liu | Xinxin Qiu | Yangkun He | Qicheng Yang | Shizhong An | Tianli Zhang | Jinghua Liu | Chengbao Jiang
Source: Scripta Materialia (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting time-dependent demagnetization behavior of permanent magnetic materials by a physics-informed neural network-based strategy Magnet ; Demagnetizing field ; Work (physics) ; Stability (learning theory) ; Artificial neural network Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: M. Lebeda | Jan Drahokoupil | Petr Veřtát | Petr Vlčák
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science High-throughput powder X-ray diffraction (XRD) simulations are a key prerequisite for generating large datasets used in the development of machine-learning models for XRD-based materials analysis. However, the widely used pymatgen powder XRD calculator, implemented entirely in Python, can be computationally inefficient for large-scale workloads, limiting throughput. We present XRD-Rust, a Rust-accelerated implementation of the pymatgen powder XRD calculator that maintains full compatibility with existing Python-based workflows. The method retains pymatgen for crystal structure handling and symmetry analysis while reimplementing the computationally intensive parts of the XRD calculation in Rust. Performance benchmarks were carried out on large crystallographic datasets from the Materials Cloud Three-Dimensional Structure Database (MC3D, 33 142 structures) and the Crystallography Open Database (COD 515 181). For the MC3D dataset, XRD-Rust achieves an average speedup of 4.7 +- 1.6 and a maximum speedup of 25, reducing computation from 34.9 s to 1.4 s. For the COD dataset, the average speedup is 6.1 +- 4.6 with a maximum speedup of 719 (1437 min to 2 min). These benchmarks demonstrate that XRD-Rust significantly accelerates powder XRD simulations, enabling efficient high-throughput dataset generation and improved performance in interactive diffraction analysis applications. Speedup ; Computation ; Diffraction ; Powder diffraction ; Limiting ; X-ray crystallography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Yuliana Nur Azizah | Fitri Aulia Permatasari | Ferry Iskandar
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Toward Robust and Stable Model Selection in Machine Learning for Materials Science via Systematic Hyperparameter Optimization ABSTRACT In materials science, Machine learning (ML) has emerged as a powerful and efficient method for predicting and optimizing material properties. However, the widespread use of default hyperparameter approaches presents challenges in ensuring model reliability. This study systematically analyses the hyperparameter optimization (HPO) framework, using carbon dots (CDs) as a representative material system. Common HPO strategies, including Grid Search, Random Search, and Bayesian Optimization, are comparatively evaluated using cross‐validation. These methods were evaluated to assess their effects on prediction accuracy and computational efficiency. The results reveal that HPO significantly improves model generalization and stabilization under limited data conditions, especially over tree‐based algorithms that are sensitive to parameter adjustment. Furthermore, initial screening with default configurations has also proven necessary for the purposeful implementation of HPO. This study demonstrates the critical role of structured HPO in ensuring reproducible, reliable ML models for materials and provides generalizable insights broadly applicable to theoretical materials research. Hyperparameter ; Hyperparameter optimization ; Bayesian optimization ; Generalization ; Model selection ; Selection (genetic algorithm) ; Bayesian probability ; Bayesian inference Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Graph Neural Networks
Authors: Muhammad Ishtiaq Jan | Abda Khatoon | Wei Sun | Naeem Khan | Sajid Ali | Ibrahim A. Shaaban | Mansoor Khan | Amir Badshah | Imran Mujaddid Rabbani | Umar Nishan
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Medicinal chemistry meets nanotechnology: machine learning assisted colorimetric sensing platform for oxalic acid based on drug mediated copper oxide nanoparticles Increased levels of oxalic acid are associated with an increased risk of kidney stone formation, which can lead to renal failure. In addition, its high concentration in the blood can lead to cardiovascular diseases. Therefore, it is vital to detect and quantify oxalic acid economically and rapidly. Copper oxide nanoparticles (CuONPs) are gaining importance as colorimetric nanosensors due to their intrinsic color change, cost-effectiveness, and easy synthesis. Paracetamol-mediated CuO NPs were synthesized through a new approach and characterized through various spectroscopic and morphological techniques. UV-visible spectroscopy confirmed the synthesis of CuO NPs through surface plasmon resonance at 225 nm. The peak at 850 cm<sup>-1</sup> corresponds to the stretching vibration of CuO NPs. The XRD and SEM characterization techniques confirmed the particle size of 27.51 nm with a spherical morphology. A machine learning-assisted strategy was developed with four prediction models: Random Forest, Linear Regression, XGBoost, and Decision Tree Regression. The intrinsic colorimetric features of CuO NPs were observed through the naked eye and quantified through spectroscopy with the addition of oxalate. The developed platform selectively detected oxalate levels in concentrations ranging from 1 to 120 µM, with a limit of detection (LOD) of 0.23 µM and a limit of quantification (LOQ) of 0.78 µM. The developed biosensor successfully quantified oxalate, crucial for diagnosing hyperoxaluria and preventing calcium oxalate stone formation in the kidneys. The machine learning complementary tools further bolster the accuracy of colorimetric concentration prediction. Oxalic acid ; Detection limit ; Calcium oxalate ; Oxalate ; Nanoparticle ; Nanosensor ; Spectroscopy ; Colorimetry ; Surface plasmon resonance Copper-based nanomaterials and applications ; Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection
Authors: Pingzhi Li | Hongxuan Li | Zirui Liu | Xingcheng Lin | Tianlong Chen
Source: arXiv (Cornell University) (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain slower than classical force fields due to fragmented kernels and memory-bound pipelines that underutilize GPUs. We show that a missing principle is making GNN-MD IO-aware, carefully accounting for reads and writes between GPU high-bandwidth memory (HBM) and on-chip SRAM. We present FlashSchNet, an efficient and accurate IO-aware SchNet-style GNN-MD framework built on four techniques: (1) flash radial basis, which fuses pairwise distance computation, Gaussian basis expansion, and cosine envelope into a single tiled pass, computing each distance once and reusing it across all basis functions; (2) flash message passing, which fuses cutoff, neighbor gather, filter multiplication, and reduction to avoid materializing edge tensors in HBM; (3) flash aggregation, which reformulates scatter-add via CSR segment reduce, reducing atomic writes by a factor of feature dimension and enabling contention-free accumulation in both forward and backward passes; (4) channel-wise 16-bit quantization that exploits the low per-channel dynamic range in SchNet MLP weights to further improve throughput with negligible accuracy loss. On a single NVIDIA RTX PRO 6000, FlashSchNet achieves 1000 ns/day aggregate simulation throughput over 64 parallel replicas on coarse-grained (CG) protein containing 269 beads (6.5x faster than CGSchNet baseline with 80% reduction of peak memory), surpassing classical force fields (e.g. MARTINI) while retaining SchNet-level accuracy and transferability. Artificial neural network ; Quantization (signal processing) ; Gaussian ; Basis (linear algebra) ; Granularity ; Filter (signal processing) ; Reduction (mathematics) ; Dimension (graph theory) ; Encoding (memory) ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Seyed Mahdi Mastoor | Ayda Baghervand | Amirhossein Ahmadkhan Kordbacheh
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Scalable machine learning models for predicting quantum transport in disordered 2D hexagonal materials Extrapolation ; Scalability ; Random forest ; Quantum ; Feature (linguistics) ; Granularity ; Hamiltonian (control theory) ; Regression Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Maximilian Bechtel | Julien Steffen
Source: ChemRxiv (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Vibrational Power Spectra as a Tool to Benchmark Universal Machine-Learning Interatomic Potentials for Molecular Systems: The OMOL-1k-MD Data Set We present OMOL-1k-MD, a new data set for the benchmark and training of universal machine-learning interatomic potentials (uMLIPs). It contains three independent ab initio molecular dynamics (AIMD) trajectories of 10 ps each for 1000 arbitrarily chosen neutral closed shell molecular systems from the OMOL25 dataset, calculated at the PBE level of theory at 300 K. In contrast to most other MLIP training or benchmark data sets, which are predominantly focused on local minimum structures or geometry optimization trajectories, OMOL-1k-MD contains a total of 30 million structures sampled from all parts of the potential energy surface that are accessible at room temperature. In this paper, we use the new data set to benchmark several popular uMLIPs towards their ability to reasonably reproduce the DFT dynamics. To this end, we compare vibrational power spectra of the OMOL-1k-MD trajectories, obtained from the velocity autocorrelation function, with uMLIP trajectories for the same set 1 of 1000 molecular structures. The comparison can be done in detail by looking at the relative positions of different vibrations, or cumulatively by calculating vibrational free energies, obtained by integrating the power spectra. This procedure enables a thorough benchmark and ranking of uMLIP performances apart from local minima. We found that the MACE MP-0 uMLIP performed best, followed by SevenNet and Orb OMat. The physics-based GFN2-xTB semiempirical method, however, performed better than all tested uMLIPs. Set (abstract data type) ; Benchmark (surveying) ; Spectral line ; Data set ; Power (physics) ; Interatomic potential Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Xijin Xing | Huanzhi Feng | Yuan Kuang | Junying Zhang | Yang Yu | Changfeng Chen
Source: International Journal of Electrochemical Science (article, 2026-04-01)
Field: Materials Science — Metals and Alloys

Interpretable Machine-learning Modeling of Corrosion Rates of Casing and Tubing Materials under High-Temperature and High-Pressure Downhole Conditions This research presents an interpretable machine-learning model for predicting the corrosion rates of casing and tubing materials exposed to aggressive downhole environments across an ultra-wide range of conditions. The model is trained and evaluated on a consolidated experimental dataset of 744 High-Temperature and High-Pressure (HTHP) autoclave tests spanning extreme operating windows (e.g., temperature 25-220°C, CO 2 partial pressure 0-28.39 MPa, H 2 S partial pressure 0-2000 kPa, chloride concentration 0-160,000 ppm) and multiple material classes, including carbon steels and corrosion-resistant alloys (Cr 0-17.0%) that are representative of casing and tubing materials used in downhole conditions, and fluid types. Fourteen candidate learning algorithms were hyperparameter-tuned using particle swarm optimization (PSO). Among them, the TabPFN algorithm achieved the best generalization on an independent test set. To ensure transparency, SHAP (SHapley Additive exPlanations) was applied to quantify feature contributions and interactions. The combined TabPFN+SHAP analysis reproduces and explains complex corrosion behaviors—including the beneficial effect of chromium, the roles of temperature, CO 2 and H 2 S partial pressures, bicarbonate concentration, oil fraction and fluid type—consistent with corrosion electrochemical corrosion theory and microstructural observations. The framework bridges predictive accuracy and mechanistic interpretability and offers a practical decision-support tool for integrity management of casing and tubing materials in aggressive downhole environments. Casing ; Corrosion ; Wellbore Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis ; Material Properties and Failure Mechanisms
Authors: Pingzhi Li | Hongxuan Li | Zirui Liu | Xingcheng Lin | Tianlong Chen
Source: Open MIND (preprint, 2026-02-13)
Field: Materials Science — Materials Chemistry

FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain slower than classical force fields due to fragmented kernels and memory-bound pipelines that underutilize GPUs. We show that a missing principle is making GNN-MD IO-aware, carefully accounting for reads and writes between GPU high-bandwidth memory (HBM) and on-chip SRAM. We present FlashSchNet, an efficient and accurate IO-aware SchNet-style GNN-MD framework built on four techniques: (1) flash radial basis, which fuses pairwise distance computation, Gaussian basis expansion, and cosine envelope into a single tiled pass, computing each distance once and reusing it across all basis functions; (2) flash message passing, which fuses cutoff, neighbor gather, filter multiplication, and reduction to avoid materializing edge tensors in HBM; (3) flash aggregation, which reformulates scatter-add via CSR segment reduce, reducing atomic writes by a factor of feature dimension and enabling contention-free accumulation in both forward and backward passes; (4) channel-wise 16-bit quantization that exploits the low per-channel dynamic range in SchNet MLP weights to further improve throughput with negligible accuracy loss. On a single NVIDIA RTX PRO 6000, FlashSchNet achieves 1000 ns/day aggregate simulation throughput over 64 parallel replicas on coarse-grained (CG) protein containing 269 beads (6.5x faster than CGSchNet baseline with 80% reduction of peak memory), surpassing classical force fields (e.g. MARTINI) while retaining SchNet-level accuracy and transferability. Artificial neural network ; Quantization (signal processing) ; Gaussian ; Basis (linear algebra) ; Granularity ; Filter (signal processing) ; Reduction (mathematics) ; Dimension (graph theory) ; Encoding (memory) ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms