jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jijie Zou | Zhanghao Zhouyin | Qiangqiang Gu | Shishir Kumar Pandey
Source:  (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While density functional theory (DFT) combined with the non-equilibrium Green's function (NEGF) formalism provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonians derived learned directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. To validate the method, we apply it to three prototypical 2D materials: graphene, hexagonal boron nitride (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$h$</tex>-BN), and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{MoS}_{2}$</tex>. The resulting band structures and transmission spectra show excellent agreement with conventional DFT-NEGF results, while achieving orders-of-magnitude improvement in efficiency. These results highlight the capability of DeePTB-NEGF to enable accurate and efficient quantum transport simulations, thereby opening avenues for large-scale exploration and device design in 2D materials. Quantum ; Formalism (music) ; Density functional theory ; Transport theory ; Boron nitride ; Quantum simulator ; Quantum computer Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Zhen Jiang | Hua Tian
Source: ACS Omega (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

SFTGNN: An Efficient Spherical Fourier Transform-Enhanced Graph Neural Network for High-Accuracy Crystal Property Prediction Crystalline materials, characterized by periodic atomic arrangements, play a fundamental role in materials science due to the strong correlation between their structure and physical properties. While graph neural networks (GNNs) have shown promise in predicting crystal properties, many existing models either neglect crucial angular features such as bond angles or suffer from high computational costs, as seen in methods like ALIGNN with <i>O</i>(<i>k</i> <sup>2</sup>) complexity for local angle calculations. In this work, we introduce SFTGNN, a novel architecture that integrates spherical Fourier transforms (SFT) with GNNs to efficiently incorporate three-dimensional geometric information into crystal property prediction. By projecting atomic local environments into the spherical harmonic domain, SFTGNN captures angular dependencies without the need to explicitly enumerate all bond angles, while reducing the computational complexity of bond angle encoding from <i>O</i>(<i>k</i> <sup>2</sup>) to <i>O</i>(<i>k</i>) and obviating the requirement for additional graph construction. We have validated the effectiveness of this method and evaluated the predictive performance of the model on the Materials Project and Jarvis data sets. SFTGNN achieves state-of-the-art results in multiple crystal property prediction tasks, while training 5.3 times faster than ALIGNN. These results highlight the potential of combining spatial geometric representations with graph-based learning for efficient and accurate modeling of crystal structures. Property (philosophy) ; Fourier transform ; Graph ; Computational complexity theory ; Artificial neural network ; Crystal (programming language) ; Spherical harmonics ; Encoding (memory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Inorganic Chemistry and Materials
Authors: Li Xu | Haitao Zhang | C. Wang | Jiaojian Wang | Donghui Yang | Cheng Guo | Zibin Wu | Hiromi Nagaumi | Guoyi Zhang
Source: Journal of Alloys and Compounds (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Accelerated design of 5xxx-Series Al-Mg alloys with enhanced mechanical properties via interpretable machine learning and microstructural validation Interpretability ; Ultimate tensile strength ; Bayesian optimization ; Alloy ; Bayesian probability ; Tensile testing ; Aluminium ; Boosting (machine learning) ; Hardening (computing) Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Jia-kuan Ren | Yinshuai Ji | Lijuan Zhu | Peng Wang | Zhihong Li | Xuewei Xing | Chun Feng
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Predicting Mechanical Properties of High-strength Titanium Alloys through Feature Engineering-guided Machine Learning Algorithms High strength titanium alloys (HSTAs) have unique advantages in the drilling and production fields of ultra deep wells due to its lightweight and high flexibility characteristics. Accurately predicting the strength and toughness of HSTAs plays a significant role in their alloy design and performance evaluation. In this work, a modified greedy algorithm (MGA) guided machine learning algorithm was put forward to predict the strength and toughness of HSTAs, and the MGA adopting a multiple out of order addition strategy exhibit superior feature recognition capabilities in high dimensional data, compared to the other three feature engineering. The MGA also had the higher feature selection stability compared to the native greedy algorithm. With the reasonable selection of machine learning algorithm, the MGA guided Gradient Boosting (GDB) algorithms achieved the highest prediction accuracy for yield strength and impact toughness of HSTAs. The process and composition window of strength and toughness balance obtained by the model can provide reference and guidance for the further design of HSTAs with comprehensive service performance. Feature selection ; Titanium alloy ; Toughness ; Boosting (machine learning) ; Gradient boosting ; Stability (learning theory) ; Flexibility (engineering) Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yan‐He Lv | Zhi Ning Chen
Source:  (article, 2025-08-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prior Knowledge-Guided Generative Deep Learning for Multi-Objective Synthesis of Miniaturized Transmissive Metasurface Elements A generative deep learning-enabled method has been proposed to efficiently synthesize quasi-freeform miniaturized metacells for dual-polarized wide-angle transmission. A four-layer pixelated metacell with metal vias is developed under a constrained small period, wherein the enriched degrees of freedom (DoF) enhance the potential for high transmission amplitude and wide-range transmission phase shifts under dualpolarized wide-angle incidence. However, the high DoF of these quasi-freeform metacells also poses the time-consuming challenges of searching in a vast solution space for multi-objective synthesis. To address this, multiple types of prior knowledge (PK) are extracted from the design targets to guide the data construction for efficient training and effective synthesizing. A condition generative neural network is proposed to learn the PK-guided dataset and realize a controllable generation for miniaturized metacells using the trained network. Finally, the generated metacells achieve a transmission phase shift range exceeding 297° and a transmission amplitude above 0.75 within a compact period of 0.24 wavelengths under the dual-polarized incoming waves at the incident angles from 0° to 45°. Transmission (telecommunications) ; Deep learning ; Phase (matter) ; Artificial neural network ; Generative grammar ; Amplitude ; Range (aeronautics) ; NASA Deep Space Network Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Peikun Zheng | Olexandr Isayev
Source: ChemRxiv (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

High-Throughput Electronic Property Prediction of Cyclic Molecules with 3D-Enhanced Machine Learning Complex organic molecules play a pivotal role in bioactive compounds and organic functional materials, yet existing molecular datasets lack structural diversity for such systems, limiting the generalizability of machine learning (ML) models. This study introduces a high-quality dataset, Ring Vault, comprising 201,546 cyclic molecules, including monocyclic, bicyclic, and tricyclic systems, spanning 11 non-metallic elements. This dataset covers a wide chemical space and provides a robust foundation for molecular property prediction. Leveraging quantum mechanical (QM) calculations on a subset (36,000 molecules), we trained three ML models (Graph Attention Network, Chemprop, and AIMNet2) to predict five key electronic properties: HOMO-LUMO gap, ionization potential (IP), electron affinity (EA), and redox potentials (Eox, Ered). The fine-tuned AIMNet2 model, incorporating 3D conformational information, outperformed 2D-based models, achieving R² values exceeding 0.95 and reducing mean absolute errors (MAEs) by over 30%. Principal component analysis (PCA) of AIMNet2 embeddings revealed intrinsic correlations between electronic properties and structural features, such as conjugation extent and functional group effects. This work establishes a robust framework for high-throughput screening and rational design of cyclic molecules, with applications spanning drug discovery, organic electronics, and energy materials. The dataset and methodology provide a foundation for exploring complex structure-property relationships and accelerating functional molecule discovery. Throughput ; Property (philosophy) ; Molecule ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Telecommunications ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic and Organometallic Chemistry
Authors: Rongrong Li | Qi Ou | Zhigang Shuai
Source: Chemical Society Reviews (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Combining quantum chemistry, machine learning and rate theory for organic luminescent materials The theoretical design of highly efficient, low roll-off and full-color emission organic materials is of great interest, although there are great challenges due to the limitations of the present-day methodology. In this review, we present progress achieved in our group on the theoretical and computational investigation for the structure-property relationships and screening strategy for organic fluorescent molecules, selection of thermally activated delayed fluorescence (TADF) and multi-resonance TADF (MR-TADF) molecules for optically and electrically pumped lasing application, and high-throughput virtual screening of phosphorescent organometallic complexes. We combined a quantum chemistry method with the molecular representation learning model Uni-Mol and rate theory-based molecular material property prediction package (MOMAP) developed in our group. Finally, we outline the limitation of current computational protocols and the future directions for organic luminescent materials. Lasing threshold ; Phosphorescence ; Fluorescence ; Luminescence ; Organic molecules ; Representation (politics) ; Quantum Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Organic Electronics and Photovoltaics
Authors: Bing Zheng | Liying Cui | Qing Li | Yanchang Zhang | Jianyu Zhang | Zhe Wang | Jiankang Chen
Source: Research Square (preprint, 2025-08-08)
Field: Materials Science — Materials Chemistry

WITHDRAWN: Machine Learning-Assisted High-Throughput Screening of Transparent Organic Light-Emitting Diodes Anode Materials Anode ; Throughput ; OLED ; Optoelectronics ; Materials science ; Diode ; Computer science ; Nanotechnology ; Telecommunications ; Chemistry ; Electrode ; Wireless Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Light-Emitting Diodes Research
Authors: Riku NAKAJIMA | Tomoki SHIONOYA | Iori Shimada
Source: Journal of Computer Chemistry Japan (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Introduction of Clustering into Chemical Reaction Neural Networks for Improvement of Prediction Accuracy Cluster analysis ; Artificial neural network ; Pattern recognition (psychology) ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Clustering Algorithms Research
Authors: Alireza Shafizadeh | Ehsan Kargaran | Meysam Madadi | Nilofar Najafi | Mahdy Elsayed
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven design of high-performance biochar from lignocellulose for sustainable battery electrodes Biochar ; Renewable energy ; Response surface methodology ; Anode ; Boosting (machine learning) ; Sustainable design ; Sorting ; Pyrolysis ; Battery (electricity) ; Multi-objective optimization Supercapacitor Materials and Fabrication ; Thermochemical Biomass Conversion Processes ; Advancements in Battery Materials
Authors: Sander van Haagen (20694455) | Salahuddin Nur (17494956) | Ryoichi Ishihara (4109431)
Source: Figshare (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Deep Learning-Optimized,Fabrication Error-TolerantPhotonic Crystal Nanobeam Cavities for Scalable On-Chip Diamond QuantumSystems Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond’s unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfectionssuch as surface roughness, sidewall slant, and nonoptimal emitter positioningcan improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting <i>Q</i>-factors at least one-million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in <i>Q</i>-factor degradation, achieving quality factors of 5 × 10<sup>4</sup> under real-world conditions and a 2-fold expansion in field distribution, enabling efficient coupling of nonoptimally positioned emitters. Compared to previous deep-learning optimization methods, this approach achieves twice the <i>Q</i>-factor performance in the presence of fabrication errors, significantly enhancing device robustness. Hence, this methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study. Nanophotonics ; Fabrication ; Photonic crystal ; Diamond ; Photonics ; Qubit ; Scalability ; Quantum computer Diamond and Carbon-based Materials Research ; Graphene research and applications ; Mechanical and Optical Resonators
Authors: Alex Kötter (7470032) | Kanishka Singh (12823214) | Hans Matter (1943974) | Gerhard Hessler (1830418) | Christoph Grebner (1478161)
Source: Figshare (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Upgrading Reliabilityin Molecular Property Predictionby Robust Quantification of Uncertainty from Machine Learning Models Reliable methods to quantify the predictive uncertainty of machine learning (ML) models can significantly increase the impact of molecular property prediction and are routinely used in applications like active learning and ML-guided property optimization. Poor predictive accuracy of ML models is often related to (i) regions of the chemical space, which are characterized by large property differences for structurally similar molecules, and (ii) a lack of representation of test molecules in the training data. Here, we analyze the relationship between these error sources and the predictive uncertainty of popular uncertainty quantification (UQ) methods on molecular activity data sets. We find that several UQ methods struggle to identify poorly predicted compounds in regions of steep structure–activity relationships (SAR). We also demonstrate that the evaluation scenario, as defined by data splitting into training and test sets, significantly impacts observed UQ performance. Based on our findings we introduce a simple but strong and very robust method for UQ that offers significant improvements over previous approaches in several evaluation scenarios and demonstrate its usefulness in an exploratory active learning setting. Property (philosophy) ; Uncertainty quantification ; Reliability (semiconductor) ; Training set ; Representation (politics) ; Predictive modelling ; Test data ; Simple (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yingying Wang | Rui Xu | Hui Sun | Shanshan Song | Xinke Qi | Li Wang
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

From alloy composition to battery performance: a machine learning strategy for the design of Mg–air battery anodes A novel ML-driven strategy is proposed to design Mg–air battery anodes with new elements beyond the existing dataset. Battery (electricity) ; Anode ; Alloy ; Composition (language) Machine Learning in Materials Science ; Advancements in Battery Materials ; Magnesium Alloys: Properties and Applications
Authors: F. X. Liu | Jimin Zhu | Jıng Zhang | Peiqiang Mu | Jikai Wen | Xu Wang | Yinbao Wu | Yan Wang | Xiliang Yan | Bing Yan
Source: ACS Applied Materials & Interfaces (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Predicting Nanotoxicity across Materials Using Interpretable Machine Learning and Descriptors Based on the Periodic Table The biological effects of engineered nanomaterials are largely determined by their intrinsic physicochemical properties; however, predicting these effects across materials with different core compositions remains a significant challenge. In this study, we developed an interpretable machine learning framework to enable cross-material nanotoxicity prediction using descriptors based on the periodic table. A curated data set comprising 1206 entries of metal oxide nanoparticles with cytotoxicity endpoints was constructed from high-quality literature sources. We evaluated multiple machine learning models to systematically analyze their performance using both within-material and cross-material validation strategies. Initial models based solely on experimental conditions and global nanomaterial properties showed high accuracy in within-material prediction but failed to generalize to unseen materials. To overcome this limitation, we introduced elemental-level descriptors, including electronegativity, ionization energy, atomic radius, and oxidation state. Incorporating these fundamental features significantly improved cross-material prediction performance, with coefficient of determination (<i>R</i><sup>2</sup>) values from approximately 0.35 to 0.65 for unseen nanomaterials such as CuO, ZnO, MgO, and Cu<sub>2</sub>O. Further experimental validation using NiO and Cr<sub>2</sub>O<sub>3</sub> nanoparticles on A549 and HUVEC cell lines confirmed the reliability of model predictions and highlighted material- and cell-type-specific toxic responses. Model interpretation and oxidative stress assay provided mechanistic insights into the cytotoxicity induced by the metal oxide nanoparticles. This study demonstrates the feasibility of cross-material toxicity prediction and establishes a scalable and interpretable computational framework for safer nanomaterial design and risk assessment. Nanotoxicology ; Nanomaterials ; Predictive modelling ; Set (abstract data type) ; Nanoparticle ; Uncertainty quantification ; Biomimetics ; Computational model Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Graphene and Nanomaterials Applications
Authors: Yuki Sakai | Munekazu Ohno
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction for Solid–Liquid Interfacial Properties From Micrographs in Al–Cu and Fe–Mn Alloys Micrograph ; Microstructure ; Anisotropy ; Alloy ; Electron micrographs ; Convolutional neural network Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Isaac W. Beaglehole | Miles J. Pemberton | Elliot H. E. Farrar | Matthew N. Grayson
Source: ChemRxiv (preprint, 2025-03-23)
Field: Materials Science — Materials Chemistry

Machine learning transition state geometries and applications in reaction property prediction The calculation of transition state (TS) geometries is essential for understanding reaction mechanisms and rational synthetic methodology design. However, traditional methods like density functional theory (DFT) are often too computationally expensive for large-scale TS identification and are significantly slower than high-throughput experimental screening methods. Recent advancements in machine learning (ML) offer promising alternatives, enabling the direct prediction of TS geometries, reducing the reliance on expensive quantum mechanical (QM) calculations and affording predictions ahead of experiment. The works explored here include the broader application of ML in reaction property prediction, emphasising how accurate TS geometries can serve as vital input data to improve model accuracy. A comprehensive review of ML methods developed to explicitly predict TS geometries are then presented, with attention to their application in downstream tasks, such as energy barrier calculations, and their use as initial structures for further optimisation via QM methods. Finally, a critical evaluation of the accuracy and limitations of existing TS prediction methods are discussed, highlighting challenges that impede wider adoption and areas where further research is needed. Property (philosophy) ; Transition (genetics) ; State (computer science) ; Computer science ; Statistical physics ; Artificial intelligence ; Algorithm ; Physics ; Chemistry ; Philosophy ; Epistemology Machine Learning in Materials Science