jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengfan Wu | Shenshen Yan | Jie Ren
Source: npj Computational Materials (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity (κL). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow κL, thereby circumventing large-scale brute-force ab initio calculations without clear objectives. As a result, we provide a list of candidates with ultralow κL for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials. Funnel ; Semiconductor ; Lattice (music) ; Thermal conductivity ; Hierarchy ; Boosting (machine learning) ; Condensed matter physics ; Materials science ; Engineering physics ; Physics ; Computer science ; Artificial intelligence ; Engineering ; Mechanical engineering ; Optoelectronics ; Political science ; Composite material Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Thermal properties of materials
Authors: Jinzhe Zeng | Timothy J. Giese | Andreas Goetz | Darrin M. York
Source: Scientific Data (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken from several datasets. We use a query-by-committee active learning strategy to extract data from large datasets to maximize the diversity and avoid redundancy as relevant for neural network training to construct the QDπ dataset. The QDπ dataset requires only 1.6 million structures to express the chemical diversity of 13 elements from the various source datasets at the ωB97M-D3(BJ)/def2-TZVPPD level of theory. The QDπ dataset enables creation of flexible target loss functions for neural network training relevant to drug discovery, including information-dense data sets of relative conformational energies and barriers, intermolecular interactions, tautomers and relative protonation energies of drug-like compounds and biomolecular fragments. It is the hope that the high chemical information density and diversity contained in the QDπ dataset will provide a valuable resource for the development of new universal MLPs for drug discovery. Biopolymer ; Drug ; Computational biology ; Chemistry ; Biology ; Pharmacology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chenghan Li | Or Sharir | Shunyue Yuan | Garnet Kin‐Lic Chan
Source: Nature Communications (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Image super-resolution inspired electron density prediction Predicting ground-state electron densities of chemical systems has recently received growing attention in machine learning quantum chemistry, given their fundamental importance as highlighted by the Hohenberg-Kohn theorem. Drawing inspiration from the domain of image super-resolution, we view the electron density as a 3D grayscale image and use a convolutional residual network to transform a crude and trivially generated guess of the molecular density into an accurate ground-state quantum mechanical density. Here we show that this model produces more accurate predictions than all prior density prediction approaches. Due to its simplicity, the model is directly applicable to unseen molecular conformations and chemical elements. We show that fine-tuning on limited new data provides high accuracy even in challenging cases of exotic elements and charge states. Computer science ; Artificial intelligence ; Resolution (logic) ; Image (mathematics) ; Electron density ; Superresolution ; Pattern recognition (psychology) ; Computer vision ; Electron ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: O. Cherguy | Radosław Chmielowski | Elie Hachem | Imène Lahouij
Source: Tribology Letters (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Deep Learning Prediction of Dry Friction in DLC Coatings Using Literature-Derived Data Abstract Predicting the friction behavior of diamond-like carbon (DLC) coatings remains a key challenge in tribology due to the complex interplay of test conditions, material properties, and experimental variability. Although literature data are abundant, they are often non-standardized and are reported under highly variable conditions, which hinders their systematic reuse for predictive modeling. This study introduces a machine learning (ML) framework that exploits heterogeneous data with a focus on physical relevance and robustness. A dataset of approximately 4100 points (including 410 friction coefficient points) was compiled from an extensive literature review. Two modeling scenarios are defined: the first uses mechanical, structural, and tribological descriptors; the second adds chemical composition features, offering more detail but reducing dataset size. Six machine learning models are evaluated under standardized training conditions to predict friction. Model performance is evaluated using standard metrics. Extra Trees (ET) and Artificial Neural Networks (ANNs) achieve the highest performance. SHAP (SHapley Additive exPlanations) analysis identifies temperature and hertz pressure as dominant predictors, consistent with the tribological observations. Incorporating chemical composition improved prediction accuracy but reduced dataset size, highlighting a key trade-off between data completeness and feature richness. SHAP analysis shows that while temperature and hertz pressure remain key predictors, the importance of humidity increases, reflecting that chemical inputs enhance not only accuracy but also the physical interpretability of the models. The results demonstrate that literature-based data can support robust and physically meaningful friction modeling when feature richness is balanced with careful control of data quality. Materials science ; Nanotechnology ; Composite material ; Artificial intelligence ; Computer science Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Advanced materials and composites
Authors: Nutth Tuchinda | G. B. Olson | Christopher A. Schuh
Source: Applied Physics Letters (article, 2025-04-28)
Field: Materials Science — Metals and Alloys

A grain boundary embrittlement genome for substitutional cubic alloys Grain boundary chemistry plays a critical role for the properties of metals and alloys, yet there is a lack of consistent datasets for alloy design and development. With the advent of artificial intelligence and machine learning in materials science, open materials models and datasets can be used to overcome such challenges. Here, we use a universal interatomic potential to compute a grain boundary segregation and embrittlement genome for the Σ5[001](210) grain boundary for FCC and BCC binary alloys. The grain boundary database calculated here serves as a design tool for the embrittlement of high-angle grain boundaries for alloys across 30 base metals of Ag, Al, Au, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe (both BCC and FCC), Ir, K, Li, Mo, Na, Nb, Ni, Pb, Pd, Pt, Rb, Rh, Sr, Ta, Ti, V, W, Yb, and Zr with 75 solute elements for each. Materials science ; Embrittlement ; Grain boundary ; Metallurgy ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep ; Aluminum Alloy Microstructure Properties
Authors: Akitaka Nakanishi | Shusuke Kasamatsu | Jun Haruyama | Osamu Sugino
Source: The Journal of Physical Chemistry C (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Defect Segregation, Water Layering, and Proton Transfer at Zirconium Oxynitride/Water Interface Examined Using Neural Network Potential Zr oxides and oxynitrides are promising candidates to replace precious metal cathodes in polymer electrolyte fuel cells. Oxygen reduction reaction activity in this class of materials has been correlated with the amount of oxygen vacancies, but a microscopic understanding of this correlation is still lacking. To address this, we simulate a defective Zr7O8N4/H2O interface model and compare it with a pristine ZrO2/H2O interface model. First, ab initio replica exchange Monte Carlo sampling was performed to determine defect segregation at the surface in the oxynitride slab model, then molecular dynamics accelerated by neural network potentials was used to obtain 1000 independent trajectories of 500 ps-dynamics to attain sufficient statistical accuracy of the solid/liquid interface structure. The presence of oxygen vacancies on the surface was found to clearly modify the local adsorption structure: water molecules were found to adsorb preferentially on Zr atoms surrounding oxygen vacancies, but not on the oxygen vacancies themselves. The fact that oxygen vacancy sites are free from poisoning by water molecules may explain the activity enhancement in defective systems. The layering of water molecules was also modified considerably, which should influence the proton and O2 transport near the interfaces which is another parameter that determines the overall activity. Differences in the details of proton transfer on the defective and pristine surfaces are also discussed. Layering ; Zirconium ; Interface (matter) ; Proton ; Materials science ; Artificial neural network ; Computer science ; Composite material ; Physics ; Artificial intelligence ; Metallurgy ; Nuclear physics ; Botany Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Mark DelloStritto | Michael L. Klein
Source: The Journal of Chemical Physics (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Improved loss functions for machine-learned atomic potentials Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality. Leverage (statistics) ; Generality ; Function (biology) ; Artificial neural network ; Mean squared prediction error ; Training set ; Minification ; Simple (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Justin Airas | Bin Zhang
Source: Journal of Chemical Theory and Computation (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Scaling Graph Neural Networks to Large Proteins Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting high accuracy in predicting complex energies and forces based on atomic identities and Cartesian coordinates. To expand the applicability of GNNs, and machine learning force fields more broadly, optimizing their computational efficiency is critical, especially for large biomolecular systems in classical molecular dynamics simulations. In this study, we address key challenges in existing GNN benchmarks by introducing a dataset, DISPEF, which comprises large, biologically relevant proteins. DISPEF includes 207,454 proteins with sizes up to 12,499 atoms and features diverse chemical environments, spanning folded and disordered regions. The implicit solvation free energies, used as training targets, represent a particularly challenging case due to their many-body nature, providing a stringent test for evaluating the expressiveness of machine learning models. We benchmark the performance of seven GNNs on DISPEF, emphasizing the importance of directly accounting for long-range interactions to enhance model transferability. Additionally, we present a novel multiscale architecture, termed Schake, which delivers transferable and computationally efficient energy and force predictions for large proteins. Our findings offer valuable insights and tools for advancing GNNs in protein modeling applications. Scaling ; Computer science ; Graph ; Data science ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tao Du | Zhimin Chen | Stein Tore Johansen | Qiangqiang Zhang | Yuanzheng Yue | Morten M. Smedskjær
Source: Engineering Fracture Mechanics (article, 2025-02-20)
Field: Materials Science — Ceramics and Composites

Predicting stiffness and toughness of aluminosilicate glasses using an interpretable machine learning model • High-throughput MD simulations of calcium aluminosilicate glasses were carried out. • Neural networks models can well capture both the elastic and fracture behaviors. • The prediction models were interpreted by the Shapely additive explanations algorithms. • Oxygen tricluster fraction is the key factor for controlling mechanical properties. The increasing demand for lighter and more durable glass materials relies on the development of stiffer, stronger, and tougher glasses. However, the design of new glasses with targeted properties is largely impeded due to the lack of composition-structure–property models. Here, we combine machine learning with high-throughput molecular dynamics simulations to predict the mechanical properties of 231 calcium aluminosilicate (CAS) glass compositions under varying preparation conditions. We demonstrate that prediction models based on neural networks can well capture both the elastic and fracture behaviors of CAS glasses. By interpretating the prediction model, we demonstrate that the Al 2 O 3 content is the primary factor determining mechanical properties. Specifically, an increase in Al 2 O 3 content leads to higher modulus, tensile strength, and toughness. The roles of preparation pressure and cooling rate are positively correlated with modulus and tensile strength, respectively. Structure analyses reveal that the fraction of oxygen triclusters is the key factor for controlling both the elastic and fracture behavior of the CAS glasses. Based on these findings, our work facilitates the rational design of new oxide glasses with targeted properties. Aluminosilicate ; Stiffness ; Toughness ; Materials science ; Composite material ; Geology ; Chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Pigment Synthesis and Properties
Authors: Szilvia Klébert | Róbert Várdai | Anita Rácz
Source: Composites Science and Technology (article, 2025-02-28)
Field: Materials Science — Polymers and Plastics

Machine learning-driven property predictions of polypropylene composites using IR spectroscopy There is a growing need for environmentally friendly alternatives to the determination of the mechanical properties, thermal stability and other functional characteristics of polymer composites, which led to the use of machine learning modeling combined with fast, non-destructive measurements like Fourier-transform infrared spectroscopy (FTIR). In this study, we have successfully classified almost 200 in-house polypropylene composites according to the applied reinforcements with the above-mentioned combination of methods. The balanced accuracy of test validation was over 0.9 for the extreme gradient boosting (XGBoost)-based model. With the same IR spectra, we have developed consensus machine learning models for predicting the modulus, tensile strength and elongation at break – which are important mechanical properties from the application point of view. The three-step validation protocol has verified that the models were appropriate for the prediction of the mechanical features of the polymer composites and their classification based on the applied reinforcements. • Use of FTIR spectra for the classification and regression modeling of PP composites. • Classification of almost 200 PP composites based on the applied reinforcements. • Application of consensus modeling on the results of PLS and neural networks. • Regression models for predicting modulus, tensile strength and elongation at break. Materials science ; Composite material ; Polypropylene ; Property (philosophy) ; Spectroscopy Natural Fiber Reinforced Composites ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Gutha Yuvaraja | Chiung‐Jung Wen | Munagapati Venkata Subbaiah | Jet‐Chau Wen | Sada Venkateswarlu | Vijaya Yarramuthi | Kun‐Yi Andrew Lin | Dong-Su Kim | K. Keerthi | N.V.V. Jyothi
Source: Coordination Chemistry Reviews (article, 2025-08-09)
Field: Chemistry — Inorganic Chemistry

Advances in next gen aerogel materials for radionuclides cleanup: From functional design to computational insights Chemistry ; Aerogel ; Environmental chemistry ; Astrobiology ; Nanotechnology ; Biochemical engineering Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Covalent Organic Framework Applications
Authors: Zhizhi Fu | Qianru Wan | Qiannan Duan | Jingzheng Lei | Jiacong Yan | Liulu Yao | Fan Song | Mingzhe Wu | Chi Zhou | Weidong Wu | Fei Wang | Jianchao Lee
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

A novel spectroscopy-deep learning approach for aqueous multi-heavy metal detection Addressing heavy metal contamination in water bodies is a critical concern for environmental scientists. Traditional detection methods are often complex and costly. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have shown significant potential in analytical chemistry. However, these AI models require extensive spectral data, which traditional methods struggle to provide quickly. To overcome this challenge, we developed a new digital spectral imaging system and rapidly collected 3000 digital spectra from mixed heavy metal samples. We then created an end-to-end regression model for predicting heavy metal concentrations in mixed water samples using deep convolutional neural networks (ResNet-50, Inception V1, and SqueezeNet V1.1). The results indicated that the trained ResNet-50 model can effectively detect arsenic, chromium, and copper simultaneously, with a linear fitting coefficient exceeding 0.99 between true and predicted values. This study offers an efficient approach for rapid heavy metal detection in complex water environments and serves as a reference for developing intelligent analytical techniques. Convolutional neural network ; Artificial intelligence ; Deep learning ; Computer science ; Artificial neural network ; Spectroscopy ; Machine learning ; Environmental science ; Biological system ; Pattern recognition (psychology) ; Physics Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Remote-Sensing Image Classification
Authors: Fengxiang Sun | Shan Zhu | Zixun Liu | Chang Liu | Kezhu Jiang | Yangyang Zhang | Biao Chen | Shijian Zheng
Source: Journal of Alloys and Compounds (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Data-driven design of metal oxide electrocatalysts with low tafel slope in oxygen evolution reaction Tafel equation ; Oxygen evolution ; Oxide ; Metal ; Oxygen ; Materials science ; Catalysis ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Metallurgy ; Electrochemistry ; Physical chemistry ; Electrode ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Lujun Li | Haibin Yu | Zhuo Wang
Source: Journal of Chemical Theory and Computation (article, 2025-01-22)
Field: Chemistry — Inorganic Chemistry

Attention-Based Interpretable Multiscale Graph Neural Network for MOFs Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns. MOFs' specific features at different scales, such as covalent bonds, functional groups, and global structures, influenced by interatomic interactions, exert varying degrees of impact on gas adsorption or selectivity. Moreover, redundant interatomic interactions hinder training accuracy, leading to overfitting. This research introduces a construction method for multiscale crystal graphs, which considers specific features at different scales by decomposing the crystal graph into multiple subgraphs based on interatomic interactions within varying distance ranges. Additionally, it takes into account the global structure of the crystal by encoding the periodic patterns of the unit cells. We propose MSAIGNN, a multiscale atomic interaction graph neural network with self-attention-based graph pooling mechanism, which incorporates three-body bond angle information, accounts for structural features at different scales, and minimizes interference from redundant interactions. Compared with traditional methods, MSAIGNN demonstrates higher prediction accuracy in assessing single-component adsorption, gas separation, and structural features. Visualization of attention scores confirms effective learning of structural features at different scales, highlighting MSAIGNN's interpretability. Overall, MSAIGNN offers a novel, efficient, multilayered, and interpretable approach for property prediction of complex porous crystal structures like MOFs using deep learning. Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Data science ; Machine learning ; Data mining ; Theoretical computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods