jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Itamar Wallwater | Yonatan Harnik | Ari Pakman | Anat Milo
Source: ChemRxiv (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

ChemSIE: From Document Based Records to Machine Actionable Experimental Data Advances in data driven analysis, machine learning, and automated reasoning have intensified the demand for large scale, structured experimental datasets in chemistry. While many existing tools focus on extracting information from narrative scientific text, a substantial fraction of experimentally relevant data is reported in document based formats where molecular depictions and characterization measurements are intentionally co located, reflecting established chemical reporting conventions but limiting direct machine actionability. Here, we introduce ChemSIE, a user-guided, automated pipeline for converting such document embedded records into molecule-resolved, machine actionable experimental data. The workflow integrates both image detection and text detection to exploit these reporting structures, assembling molecule-specific experimental records by associating molecular depictions with nearby characterization text and parsing quantitative observables across multiple modalities. Benchmarking on a diverse corpus of theses and Supplementary Information documents demonstrates high precision and recall for quantitative data extraction, together with robust molecule level segmentation and association. By emphasizing structural heuristics, transparency, and user-guided validation, ChemSIE complements existing text centric approaches and demonstrates how adapting to, rather than abstracting away from, current reporting practices enables the scalable construction of interoperable, FAIR ready experimental datasets from document-based experimental records. Workflow ; Pipeline (software) ; Parsing ; Precision and recall ; Benchmarking ; Focus (optics) ; Experimental data ; Exploit ; Data curation Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Various Chemistry Research Topics
Authors: Tianzhixi Yin | Peiyuan Gao | Gihan Panapitiya | Emily Saldanha
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Out-of-distribution evaluation of active learning pipelines for molecular property prediction Active learning (AL) has been widely applied as a strategy to reduce the data requirements of training machine learning models. Such a strategy can be especially valuable in fields where data collection is costly or time-consuming, as is the case for molecular property data. In this study, we evaluate AL for molecular property prediction, focusing on the performance on out-of-distribution (OOD) data. This OOD evaluation framework mimics the scenario found in real-world applications but is understudied in the prior literature. In our study, we focus on the prediction of solvation energy from molecular structure and develop an AL framework based on prediction uncertainties derived from Evidential Deep Learning (EDL). We started by training our model on an in-distribution training dataset and progressively augmented it with molecules from an OOD dataset sampled from PubChem, selected either randomly or using the AL strategy. We further examined generalization capabilities of AL by beginning with a subset of the in-distribution dataset, intentionally chosen to reduce initial diversity. Our results indicate that EDL demonstrates an advantage over random sampling. To further understand the behavior of the AL algorithm, we performed analysis of how the similarity between the training dataset and the held-out dataset affects the AL performance and of the distributional differences in the types of molecules selected by random sampling and AL. Generalization ; Property (philosophy) ; Similarity (geometry) ; Focus (optics) ; Training set ; Sampling (signal processing) ; Active learning (machine learning) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Supramolecular Chemistry and Complexes
Authors: Eric D. Boittier | Markus Meuwly
Source: Open MIND (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Efficient, Equivariant Predictions of Distributed Charge Models A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or σ-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e). Dipole ; Multipole expansion ; Charge (physics) ; Quadrupole ; Representation (politics) ; Feature (linguistics) ; Point particle ; Equivariant map ; Anisotropy Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Eric D. Boittier | Markus Meuwly
Source: arXiv (Cornell University) (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Efficient, Equivariant Predictions of Distributed Charge Models A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or σ-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e). Dipole ; Multipole expansion ; Charge (physics) ; Quadrupole ; Representation (politics) ; Feature (linguistics) ; Point particle ; Equivariant map ; Anisotropy Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Tasuku Sugiura | Teruyasu Mizoguchi
Source: Open MIND (preprint, 2026-02-20)
Field: Materials Science — Materials Chemistry

Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning Machine learning has revolutionized materials property prediction, yet fails catastrophically when extrapolating beyond training distributions-precisely the capability required for discovering unprecedented materials. Graph neural networks (GNNs) exhibit this collapse because end-to-end training fundamentally couples learned representations to target property distributions, preventing genuine extrapolation. We demonstrate that decoupled transfer learning-separating pretrained GNN feature extractors from simple regressors-overcomes this barrier. Pretrained features provide transferable structural knowledge, while simple regressors enable smooth extrapolation by maintaining learned trends beyond training boundaries. Benchmarked on layered intercalation compounds through four rigorous extrapolation scenarios and a temporal Materials Project split, our framework achieves 68% error reduction (RMSE: 0.881 vs. 2.778 eV/atom) versus end-to-end GNNs for extrapolation. Failure analysis reveals extrapolation succeeds for continuous chemical space but fails for discontinuous space, establishing clear design principles. Validated on Fermi energy prediction, this framework is immediately deployable using existing pretrained models, requiring no architectural innovations-transforming ML-driven materials discovery. Extrapolation ; Property (philosophy) ; Feature (linguistics) ; Transfer of learning ; Simple (philosophy) ; Representation (politics) ; Graph ; Training set Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks
Authors: P.V. Chirkov | A.A. Mirzoev
Source: Bulletin of the South Ural State University series Mathematics Mechanics Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MOLECULAR DYNAMICS SIMULATION OF TITANIUM HYDRIDE STABILITY USING MACHINE-LEARNED POTENTIALS Studies into of titanium hydrides are important for understanding hydrogen embrittlement processes in titanium alloys and developing promising metallic hydrogen storage systems. The Ti-H system has a phase diagram characterized by a phase transformation from a FCC to a tetragonal FCT crystal lattice. This transition was studied using machine learning of the interatomic potential based on the Moment Tensor Potential (MTP) model. To construct a potential able to accurately reproduce the energies of the system under tetragonal deformation, we created a training dataset containing both periodic structures and distorted configurations obtained at finite temperatures. To obtain a potential able to reproduce energy characteristics under tetragonal lattice distortion, we used an active learning technique, which controlled the degree of potential extrapolation compared to the training dataset and sequentially retrained the MTP. Molecular dynamics simulations have revealed that the FCT-FCC transformation temperature for TiH2 dihydride is 487 K, which is higher than experimentally observed values. We also found that at 300 K, a tetragonal lattice of TiHx hybrids is formed at a hydrogen content greater than 1,86. Tetragonal crystal system ; Interatomic potential ; Extrapolation ; Titanium ; Molecular dynamics ; Phase diagram ; Crystal structure ; Hydrogen ; Lattice (music) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Megan Robinson | Saikat Ghosh | Feng Qian | Chenyu Du | Mauro Vallati | Parikshit Goswami
Source: Textiles (article, 2026-03-02)
Field: Materials Science — Polymers and Plastics

Recent Development on Sorting of Textiles Waste by Fibre Type for Recycling: A Mini Review With the rapid expansion of the global textile sector and increasing awareness of the environmental pollution caused by textile waste, enhancing the recycling of textile waste has become essential to reduce the volume of materials sent to landfill or incineration. As recycling technologies advance, automated sorting systems that are capable of handling large waste streams and accurately identifying materials for appropriate recycling pathways are increasingly recognised as being critical for efficient textile-waste management. Since 2015, over 20 studies have specifically explored technologies and strategies for automating textile sorting of textile wastes. This mini review introduces various textile fibre identification technologies, including traditional visual and tactile examination; label checking and modern identification technology; and NIR, FT-IR, RFID tags. It summarises the current state of sorting processes, with particular emphasis on the development of AI-assisted, fibre-type-based sorting technologies. Commercial scale automated sorting is not established yet for textile waste recycling, due to the complexity of materials used in textiles, the equipment identification limits and high cost of processing, while machine learning and artificial neural networks provide opportunities for future research advancement and commercialisation. Sorting ; Textile ; Identification (biology) ; Textile industry ; Environmental pollution ; Scale (ratio) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Industrial Vision Systems and Defect Detection
Authors: Anders S. Larsen | Toms Rekis | Philipp Hans | Anders Ø. Madsen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

PhAI: neural network for structure determination For a crystal structure to be solved from diffraction data, it is necessary to obtain the complex structure factors, F H , of the measured reflections.Experimentally, only their squared moduli, |F H | 2 , which are proportional to the measured intensities, can be determined, not the phase angles needed to reconstruct the corresponding complex numbers.Since no exact solution to this phase problem exists, several methods to overcome this problem in crystal structure determination have been developed over time.For example, direct methods or the charge flipping algorithm can be used for most organic, inorganic, and metal-organic structures.Nevertheless, these methods fail if, for example, the available data resolution is not sufficiently high, the completeness of the data is low, or the number of atoms in the asymmetric unit is very high -as is the case for macromolecular crystals and some framework structures.Here we demonstrate how the phase problem can be solved using a neural network, PhAI, which has been trained on millions of fictive structures containing metal atoms and/or molecular fragments.PhAI was subsequently validated on thousands of structures retrieved from the Cambridge Structural Database for which the structure factor amplitudes, |FH|, were generated at several resolution limits and fed into the trained network to output phases.The phases could be retrieved with a striking accuracy, leading to correct structure solutions for over 99% of the validation set entries.The phase retrieval accuracy was also high even if the resolution limit was low, i.e. d min = 2 .Additionally, several dozens of experimentally measured diffraction data sets were used for validation.Our results indicate that deep learning can be used to obtain electron density maps for structures where data could only be obtained at very limited resolution and are more than challenging to solve by state-of-the-art methods.Our published model (Larsen et al., 2024) focused on structures in the centrosymmetric space group P21/c with small unit cells (cell dimensions less than 10 ).In this contribution, we discuss the performance and architecture of an improved neural network that works in the general case (space group P1), i.e. without symmetry restrictions, and on larger unit cells.We also show our progress in implementing the PhAI deep learning for structure solution approach into the Olex2 program. Diffraction ; Artificial neural network ; Phase (matter) ; Limit (mathematics) ; Resolution (logic) ; Set (abstract data type) ; Completeness (order theory) ; Data set Machine Learning in Materials Science ; Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications
Authors: Zihan Liu | Zixuan Wang | Jiateng Sun | Jing Yuan | Yuai Duan | Z Li | Tianyu Han
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

An MNP-based tactile sensor enabling fluorescent texture imaging for human presence recognition Fluorescence ; Convolutional neural network ; Texture (cosmology) ; Tactile sensor ; Pattern recognition (psychology) ; Sensory system Luminescence and Fluorescent Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Zhongyao Wang | Taoyong Cui | Jiawen Zou | Shufei Zhang | Bo Yan | Wanli Ouyang | Weimin Tan | Mao Su
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Equivariant Evidential Deep Learning for Interatomic Potentials Uncertainty quantification (UQ) is critical for assessing the reliability of machine learning interatomic potentials (MLIPs) in molecular dynamics (MD) simulations, identifying extrapolation regimes and enabling uncertainty-aware workflows such as active learning for training dataset construction. Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance. Evidential deep learning (EDL) provides a theoretically grounded single-model alternative that determines both aleatoric and epistemic uncertainty in a single forward pass. However, extending evidential formulations from scalar targets to vector-valued quantities such as atomic forces introduces substantial challenges, particularly in maintaining statistical self-consistency under rotational transformations. To address this, we propose \textit{Equivariant Evidential Deep Learning for Interatomic Potentials} ($\text{e}^2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly by representing uncertainty as a full $3\times3$ symmetric positive definite covariance tensor that transforms equivariantly under rotations. Experiments on diverse molecular benchmarks show that $\text{e}^2$IP provides a stronger accuracy-efficiency-reliability balance than the non-equivariant evidential baseline and the widely used ensemble method. It also achieves better data efficiency through the fully equivariant architecture while retaining single-model inference efficiency. Uncertainty quantification ; Extrapolation ; Deep learning ; Covariance ; Inference ; Workflow ; Tensor (intrinsic definition) ; Reliability (semiconductor) ; Equivariant map Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Riaz K. Israni | Jaydeep Singh Parmar | Mehul Chauhan | Hina Maniya | Brij Pal Singh | Harsh Purohit | Jignesh H. Kamdar
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrating nanotechnology with deep learning for engineering, healthcare, and environmental solutions: A review Combining nanotechnology and deep learning methods is a leading-edge frontier in engineering solutions, with unparalleled prospects for innovation and growth. The synergistic combination blends the scale and precision of nano-materials with the predictive capability and resilience of artificial intelligence. Deep learning methods can process enormous amounts of data collected by nano-scale sensors, designing and optimizing materials at the atomic level. This integration makes possible the creation of self-healing smart nano-materials, highly efficient energy storage devices, and ultra-sensitive healthcare diagnostic tools. Additionally, deep learning methods can expedite the discovery of new nanostructures and their uses with much less time and expense compared to conventional experimental approaches. As this multidisciplinary area continues to grow, it holds the potential to transform numerous industries, such as electronics, healthcare, environmental science, and energy technology, towards more efficient, sustainable, and smart engineering solutions. Deep learning ; Multidisciplinary approach ; Process (computing) ; Scale (ratio) ; Deep integration ; Resilience (materials science) ; Big data ; Frontier Machine Learning in Materials Science ; Nanotechnology research and applications ; Molecular Communication and Nanonetworks
Authors: Nguyen T. T. Duyen | Ngo T. Que | Anh D. Phan
Source: ArXiv.org (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Multiscale Prediction of Polymer Relaxation Dynamics via Computational and Data-Driven Methods We present a multiscale modeling approach that integrates molecular dynamics simulations, machine learning, and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of polymer systems. The glass transition temperatures (Tg) of four representative polymers are estimated using simulations and machine learning model trained on experimental datasets. These predicted Tg values are used as inputs to the ECNLE theory to compute the temperature dependence of structural relaxation times and diffusion coefficients, and the dynamic fragility. The Tg values predicted from simulations show good quantitative agreement with experimental data. While machine learning tends to slightly overestimate Tg, the resulting dynamic fragility values remain close to experimental fragilities. Overall, ECNLE calculations using these inputs agree well with broadband dielectric spectroscopy results. Our integrated approach provides a practical and scalable tool for predicting the dynamic behavior of polymers, particularly in systems where experimental data are limited. Fragility ; Relaxation (psychology) ; Glass transition ; Molecular dynamics ; Dynamics (music) ; Diffusion ; Nonlinear system ; Dielectric Material Dynamics and Properties ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Tasuku Sugiura | Teruyasu Mizoguchi
Source: ArXiv.org (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning Machine learning has revolutionized materials property prediction, yet fails catastrophically when extrapolating beyond training distributions-precisely the capability required for discovering unprecedented materials. Graph neural networks (GNNs) exhibit this collapse because end-to-end training fundamentally couples learned representations to target property distributions, preventing genuine extrapolation. We demonstrate that decoupled transfer learning-separating pretrained GNN feature extractors from simple regressors-overcomes this barrier. Pretrained features provide transferable structural knowledge, while simple regressors enable smooth extrapolation by maintaining learned trends beyond training boundaries. Benchmarked on layered intercalation compounds through four rigorous extrapolation scenarios and a temporal Materials Project split, our framework achieves 68% error reduction (RMSE: 0.881 vs. 2.778 eV/atom) versus end-to-end GNNs for extrapolation. Failure analysis reveals extrapolation succeeds for continuous chemical space but fails for discontinuous space, establishing clear design principles. Validated on Fermi energy prediction, this framework is immediately deployable using existing pretrained models, requiring no architectural innovations-transforming ML-driven materials discovery. Extrapolation ; Property (philosophy) ; Feature (linguistics) ; Transfer of learning ; Simple (philosophy) ; Representation (politics) ; Graph ; Training set Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks