jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianchuan Liu | Tao Chen | Sheng Mao | Mohan Chenb
Source: ArXiv.org (preprint, 2025-01-22)
Field: Materials Science — General Materials Science

Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa. This study presents a model trained using density functional theory data for full concentration CuxW100-x compounds. Through this model, we systematically investigate the structural and mechanical properties of W-Cu alloys and have the following findings. First, the bulk modulus (B) and Young's modulus (E) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the CuxW100-x compounds as the Cu concentration rises. Second, a higher Cu content results in higher critical strain and lower critical stress for these compounds. A brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Third, tensile loading tests in the W-Cu gradient structure reveal that Cu-poor region serves as a barrier, hindering shear band propagation while promoting new shear band formation in the Cu-rich region. The above results from the DP model are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems and contribute to the advancement of methodologies for materials simulation. Artificial neural network ; Biological system ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Biology Metallurgical and Alloy Processes ; Advanced materials and composites ; Advancements in Materials Engineering
Authors: Kim, Yong-Woon | Yoo, Paul D. | Yeun, Chan Yeob | Kang, Chulung | Byun, Yung-Cheol
Source: Open MIND (preprint, 2025-11-08)
Field: Materials Science — Materials Chemistry

Hard-constraint physics-residual networks enable robust extrapolation for hydrogen crossover prediction in PEM water electrolyzers Hydrogen crossover in polymer electrolyte membrane water electrolysis poses a critical safety and efficiency bottleneck for scalable green hydrogen production. While machine learning offers real-time monitoring capabilities, conventional data-driven newral networks (Pure NNs) and soft-constraint physics-informed neural networks (Standard PINNs) suffer from inherent optimization conflicts and fail catastrophically when extrapolating beyond sparse training conditions. Here, we present a hard-constraint physics-residual network (PR-Net) that embeds analytical transport equations -- Henry's law, Fick's diffusion, and Faraday's law -- as a deterministic computational backbone, restricting the neural network to learn only systematic physical deviations. Across 184 experimental points spanning six membrane types and operating conditions of 25--85$^{\circ}$C, 1--200~bar, and 0.05--5.0 A cm$^{-2}$, this architecture intrinsically resolves gradient conflicts, yielding $R^{2} = 99.57 \pm 0.16\%$ with a 39-fold reduction in training variance compared to purely data-driven models ($R^{2} = 96.47 \pm 6.20\%$). Crucially, the PR-Net breaks the extrapolation barrier, maintaining $R^{2} &gt; 97\%$ at extreme cathode pressures up to 200~bar -- a 2.5-fold extrapolation beyond the training domain where Standard PINN severely degrades ($R^{2} = 72.2\%$) and Pure NN collapses ($R^{2} = 58.7\%$). Furthermore, the learned residuals autonomously capture temperature-induced membrane swelling (Spearman's $ρ= 0.506$, $p &lt; 0.001$) and identify the non-linear transport regime transition near 0.23 A cm$^{-2}$, without explicit programming. Delivering millisecond-level inference on edge hardware, the PR-Net establishes a highly reliable, generalizable foundation for adaptive safety control and predictive maintenance in high-pressure electrochemical energy systems. Extrapolation ; Bottleneck ; Crossover ; Artificial neural network ; Polymer electrolyte membrane electrolysis ; Inference Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Electrocatalysts for Energy Conversion
Authors: Zhefan Wang | Bin Zhao | Bing Xiao | Guangwen Cheng | Song Yang | Zhongxu Guo | Li Yang | Cheng‐Meng Chen | Jianwei Cheng | Xiaogang Han
Source: Electrochimica Acta (article, 2025-07-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-functional tris(trimethylsilyl) phosphate for simultaneous passivation and moisture scavenging in high-voltage electrochemical double-layer capacitors Passivation ; Tris ; Capacitor ; Electrochemistry ; Layer (electronics) ; Phosphate ; Chemistry ; Scavenging ; Trimethylsilyl ; Materials science ; Moisture ; Chemical engineering ; Inorganic chemistry ; Voltage ; Electrode ; Organic chemistry ; Electrical engineering ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Fanding Xu | Zhiwei Yang | Wu Chung Su | Lizhuo Wang | Deyu Meng | Jiangang Long
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

MolUNet++: Adaptive-grained Explicit Substructure and Interaction Aware Molecular Representation Learning Molecular representation learning is a critical task in AI-driven drug development. While graph neural networks (GNNs) have demonstrated strong performance and gained widespread adoption in this field, efficiently extracting and explicitly analyzing functional groups remains a challenge. To address this issue, we propose MolUNet++, a novel model that employs Molecular Edge Shrinkage Pooling (MESPool) for hierarchical substructure extraction, utilizes a Nested UNet framework for multi-granularity feature integration, and incorporates a substructure masking explainer for quantitative fragment analysis. We evaluated MolUNet++ on tasks including molecular property prediction, drug-drug interaction (DDI) prediction, and drug-target interaction (DTI) prediction. Experimental results demonstrate that MolUNet++ not only outperforms traditional GNN models in predictive performance but also exhibits explicit, intuitive, and chemically logical interpretability. This capability provides valuable insights and tools for researchers in drug design and optimization. Substructure ; Representation (politics) ; Computer science ; Theoretical computer science ; Engineering ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Xinyu Ni | Yoshifumi Amamoto | Jun Kikuchi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Simultaneous Multimodal and Multitask Strategies for Diverse Biodegradable Polymers Powered by Nmr Data Science Polymer ; Multimodal therapy ; Chemistry ; Materials science ; Biochemical engineering ; Nanotechnology ; Engineering ; Organic chemistry ; Psychology Machine Learning in Materials Science
Authors: Kazuhiro Hashiguchi | Akito Maruo | Shinji Iwane | Hideyuki Jippo | Yoshinori Suga
Source: Bulletin of the Chemical Society of Japan (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Material discovery using quantum-inspired technologies: validating a band gap engineering approach for inorganic materials Abstract There is growing interest in applying materials informatics to inorganic materials, such as power semiconductors and high-entropy alloys. Developing material exploration methods that can overcome the combinatorial explosion, particularly in optimizing compositions containing multiple elements, is an urgent issue. This research successfully developed a method for optimizing the element arrangement of diamond-structured C, Si, and Ge to achieve a desired band gap. We combined mathematical optimization techniques utilizing Fujitsu Quantum-inspired Computing Digital Annealer (DA), a quantum-inspired technology, with density functional theory (DFT) calculations using Quantum Espresso (QE). We optimized the element arrangement using FM-DA&amp;GA, a combination of the factorization machines (FM), a machine learning technique, with DA and genetic algorithms (GA). This approach was confirmed to be superior to random search, conventional GA methods, and FM-DA alone in terms of the number of calculations and computation time. Furthermore, in multiobjective optimization incorporating formation energy in addition to band gap, FM-DA&amp;GA successfully identified the real material 3C-SiC in a 2-element (C and Si). FM-DA&amp;GA combines the fast model exploration of FM-DA with the global search capabilities of GA. While the method of combining FM-DA&amp;GA with DFT calculations has previously been applied to organic compounds, this study represents its world's first application to inorganic materials. This achievement is expected to significantly expand the possibilities for inorganic material exploration. Chemistry ; Band gap ; Quantum ; Nanotechnology ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Colin Allen | Albert P. Bartók
Source: arXiv (Cornell University) (preprint, 2025-01-10)
Field: Materials Science — Materials Chemistry

Multi-Phase Dataset for Ti and Ti-6Al-4V Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials (MLIPs) are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the α, \b{eta}, ω and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by Density Functional Theory (DFT) using the PBE exchange-correlation functional. We have also leveraged and extended a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species within bulk crystalline data for Ti-6Al-4V. These configurations may be used to fit MLIP models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models based on the Gaussian Approximation Potential (GAP) and Atomic Cluster Expansion (ACE) frameworks. Materials science ; Phase (matter) ; Business ; Metallurgy ; Chemistry Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Qizhang Huang | Zitong Xu | Xiangchao Gan
Source: The Journal of Physical Chemistry C (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Facile Discovery of Pristine MoS<sub>2</sub> Magnetic Ordering via Machine Learning Prediction and DFT Validation Understanding the magnetic ordering in molybdenum disulfide (MoS2) is crucial for the advancement of novel magnetic devices, given the fascinating physical and chemical properties of MoS2. Our approach combines a preliminary prediction from machine learning (ML) models, trained on a limited data set of only 390 data points, with rigorous validation through density functional theory (DFT) calculations. This hybrid method provides valuable insights into the underlying mechanisms driving the predictions. The analysis facilely reveals a complex relationship between crystal structure and net magnetization in pristine MoS2, demonstrating that an increased cell volume, achieved through plane expansion in a trigonal crystal structure, is associated with the emergence of magnetization in MoS2. Specifically, expanding the cell volume weakens atomic interactions, thereby facilitating the formation of sufficient energy states while simultaneously reducing electron saturation near the Fermi level. This efficient ML-DFT hybrid methodology offers a streamlined path for tailored material design, as exemplified by our exploration of pristine MoS2 for spintronic applications. Materials science ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Jaka Fajar Fatriansyah | Muhammad Ali Yafi Rizky | Rio Sudwitama Persadanta Kaban | Muhammad Rakha Alfarabi | Sharen Ardyana Khintani | Rayhan Hagel Safa
Source:  (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Screening of Pyrimidine-Pyrazole Derivatives as Steel Corrosion Inhibitors in Hydrochloric Solution Industrial corrosion poses significant economic and safety risks. This study evaluates the performance of pyrimidine-pyrazole derivatives as corrosion inhibitors for steel in hydrochloric acid. Various machine learning algorithms, including K-Nearest Neighbours (KNN), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra Trees (ET), and Artificial Neural Network (ANN), were trained on molecular descriptors from AlvaDesc to predict the inhibitor efficiency based on the electronic features. Recursive Feature Elimination (RFE) was used to optimize model performance. Model accuracy was assessed using the R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score, with XGB achieving the highest precision (96.50%). Further analysis using SHAP and Pearson Correlation identified the F02{N-N} descriptor as the most significant feature influencing EHOMO energy. The findings validate that machine learning, particularly the XGB model, provides an accurate and cost-effective approach for screening potential corrosion inhibitors. Support vector machine ; Artificial neural network ; Corrosion ; Hydrochloric acid ; Gradient boosting ; Feature (linguistics) ; Boosting (machine learning) ; Corrosion inhibitor Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Wei Feng | Siyuan Liu | Hongyi Wang | Zhenliang Mu | Zhichen Pu | Xu Han | Tianze Zheng | Zhenze Yang | Zhi Wang | Weihao Gao | Yongji Cao | Kuang Yu | Sheng Gong | Yan Wen
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids The thermal conductivity of organic liquids is a vital parameter influencing various industrial and environmental applications, including energy conversion, electronics cooling, and chemical processing. However, atomistic simulation of thermal conductivity of organic liquids has been hindered by the limited accuracy of classical force fields and the huge computational demand of ab initio methods. In this work, we present a machine learning force field (MLFF)-based molecular dynamics simulation workflow to predict the thermal conductivity of 20 organic liquids. Here, we introduce the concept of differential attention into the MLFF architecture for enhanced learning ability, and we use density of the liquids to align the MLFF with experiments. As a result, this workflow achieves a mean absolute percentage error of 14% for the thermal conductivity of various organic liquids, significantly lower than that of the current off-the-shelf classical force field (78%). Furthermore, the MLFF is rewritten using Triton language to maximize simulation speed, enabling rapid prediction of thermal conductivity. Thermal conductivity ; Workflow ; Force field (fiction) ; Molecular dynamics ; Field (mathematics) ; Ab initio ; Conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Cody A. Nizinski | Aaron M. Chalifoux | Evan J. Warzecha | Alex R. Hagen | Alexa B. Hanson | Kari Sentz | Forrest D. Heller | Richard A. Clark | David E. Meier | Gregg J. Lumetta | Joel M. Tingey
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Effect of precipitation batch size on the morphological properties of various actinide oxalates Oxalate ; Plutonium ; Precipitation ; Actinide ; Coprecipitation ; Uranyl ; Scanning electron microscope ; Particle size ; Process (computing) Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Radioactive contamination and transfer
Authors: Yanjie Li | Jian Xu | Xueqing Chen | Lina Yu | Shiming Xiang | Weijun Li | Cheng-lin Liu
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

QE-Catalytic: A Graph-Language Multimodal Base Model for Relaxed-Energy Prediction in Catalytic Adsorption Adsorption energy is a key descriptor of catalytic reactivity. It is fundamentally defined as the difference between the relaxed total energy of the adsorbate-surface system and that of an appropriate reference state; therefore, the accuracy of relaxed-energy prediction directly determines the reliability of machine-learning-driven catalyst screening. E(3)-equivariant graph neural networks (GNNs) can natively operate on three-dimensional atomic coordinates under periodic boundary conditions and have demonstrated strong performance on such tasks. In contrast, language-model-based approaches, while enabling human-readable textual descriptions and reducing reliance on explicit graph -- thereby broadening applicability -- remain insufficient in both adsorption-configuration energy prediction accuracy and in distinguishing ``the same system with different configurations,'' even with graph-assisted pretraining in the style of GAP-CATBERTa. To this end, we propose QE-Catalytic, a multimodal framework that deeply couples a large language model (\textbf{Q}wen) with an E(3)-equivariant graph Transformer (\textbf{E}quiformer-V2), enabling unified support for adsorption-configuration property prediction and inverse design on complex catalytic surfaces. During prediction, QE-Catalytic jointly leverages three-dimensional structures and structured configuration text, and injects ``3D geometric information'' into the language channel via graph-text alignment, allowing it to function as a high-performance text-based predictor when precise coordinates are unavailable, while also autoregressively generating CIF files for target-energy-driven structure design and information completion. On OC20, QE-Catalytic reduces the MAE of relaxed adsorption energy from 0.713~eV to 0.486~eV, and consistently outperforms baseline models such as CatBERTa and GAP-CATBERTa across multiple evaluation protocols. Inverse ; Graph ; Adsorption ; Artificial neural network ; Reliability (semiconductor) ; Efficient energy use ; Energy (signal processing) ; Function (biology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jingyun Ye (1289649) | Chathura Wijethunga (21841889) | Megan McEwen (14013927)
Source: Figshare (article, 2025-07-31)
Field: Chemistry — Organic Chemistry

The First FrustratedLewis Pairs Database: MachineLearning and Cheminformatics-Aided Prediction of Small Molecule Activation Frustrated Lewis Pairs (FLPs) are a burgeoning field in chemistry, with novel FLPs and surprising new reactions continually being discovered. However, experimentally examining each FLP’s activity toward all possible small molecules (SMs) is impractical due to time constraints and potential dangers from toxic, flammable, or explosive SMs. Here, we developed the first open-access Frustrated Lewis pairs Database (FLPDB), with DFT-optimized atomic structures and computed binding free energies and electronic properties data for each FLP toward SMs, including H<sub>2</sub>, CO<sub>2</sub>, H<sub>2</sub>O, H<sub>2</sub>CO, HCOOH, and CH<sub>3</sub>OH, via high-throughput computational screening. Machine learning was employed to predict the small molecule binding free energies and identify the most important feature governing the binding free energies. Further integrating molecular fingerprints, the H<sub>2</sub> binding free energies can be predicted with a single featurehydride affinityfor FLPs with molecular similarity scores ≥0.55. The FLPDB and the identified structure–activity relationships in this work will enable countless applications for novel FLP design, new reaction discovery, or toxic gas sensing and detection. Frustrated Lewis pair ; Small molecule ; Feature (linguistics) ; Molecule ; Field (mathematics) ; Binding energy ; Similarity (geometry) Organoboron and organosilicon chemistry ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Andrew Ma | Owen Dugan | Marin Soljačić
Source: arXiv (Cornell University) (preprint, 2025-01-06)
Field: Materials Science — Materials Chemistry

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics In solid-state materials science, substantial efforts have been devoted to the calculation and modeling of the electronic band gap. While a wide range of ab initio methods and machine learning algorithms have been created that can predict this quantity, the development of new computational approaches for studying the band gap remains an active area of research. Here we introduce a simple machine learning model for predicting the band gap using only the chemical composition of the crystalline material. To motivate the form of the model, we first analyze the empirical distribution of the band gap, which sheds new light on its atypical statistics. Specifically, our analysis enables us to frame band gap prediction as a task of modeling a mixed random variable, and we design our model accordingly. Our model formulation incorporates thematic ideas from chemical heuristic models for other material properties in a manner that is suited towards the band gap modeling task. The model has exactly one parameter corresponding to each element, which is fit using data. To predict the band gap for a given material, the model computes a weighted average of the parameters associated with its constituent elements and then takes the maximum of this quantity and zero. The model provides heuristic chemical interpretability by intuitively capturing the associations between the band gap and individual chemical elements. Simple (philosophy) ; Property (philosophy) ; Statistics ; Composition (language) ; Econometrics ; Computer science ; Mathematics ; Linguistics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Surface and Thin Film Phenomena