jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Chen | Vijay Ganesh | Weikai Li | Yingyan Lin | Yong Liu | Subhasish Mitra | David Z. Pan | Ruchir Puri | Jason Cong | Yizhou Sun
Source: IEEE Circuits and Systems Magazine (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Report for NSF Workshop on AI for Electronic Design Automation [NSF Workshop Report] This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver along-side NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI—spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.—can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high-level and logic-level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM-assisted verification tools, ML-augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems. The workshop information can be found on the website <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ai4eda-workshop.github.io/</uri> Electronic design automation ; Toolbox ; Automation ; Scalability ; Engineering design process ; Process (computing) ; Physical design Machine Learning in Materials Science ; Embedded Systems Design Techniques ; VLSI and FPGA Design Techniques
Authors: Blake Dallmann | Aryan Saha | Andrew S. Rosen
Source: ChemRxiv (article, 2026-04-06)
Field: Chemistry — Inorganic Chemistry

Predicting the Thermodynamic Limits of Metal–Organic Framework Metastability The vast combinatorial space of metal–organic frameworks (MOFs) has led to their widespread consideration across diverse application areas. That said, much remains unknown about what factors govern their thermodynamic stability. Herein, we use density functional theory to compute the formation energy and construct convex hull phase diagrams for 20,000+ MOFs and coordination polymers. Using the energy above hull as a measure of stability with respect to decomposition and phase transitions, we validate and expand upon previous hypotheses that all MOFs are thermodynamically metastable and that there is an inherent energetic penalty associated with permanent porosity. We also describe how MOF composition and metal/linker identity influence the degree of metastability, in addition to demonstrating how the energy above hull can be used as a synthesizability metric for newly proposed MOFs. To democratize the knowledge gained from our study, we have released the QMOF-Thermo Database, which is the first database of energy above hull values for MOFs and coordination polymers. We conclude by using the QMOF-Thermo Database to benchmark the ability of pre-trained machine learning interatomic potentials to predict the energy above hull of MOFs, and we identify opportunities to correct their performance. Convex hull ; Hull ; Metastability ; Benchmark (surveying) ; Stability (learning theory) ; Metric (unit) ; Energy (signal processing) ; Construct (python library) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Zhuang Cui | Ying Zeng | Manping Liu | Jian Li | Haohao Li
Source: Journal of Magnesium and Alloys (article, 2026-03-01)
Field: Materials Science — Biomaterials

Progress in creep-resistant RE-containing Mg-Al alloys: From micro-mechanisms and composition design to structure-property relationships • Systematically reviews creep behaviors and strengthening mechanisms of Mg-Al-RE alloys. • Critically analyzes the effects of RE elements on microstructure and creep resistance. • Highlights composition design paradigm integrating CALPHAD, first-principles, and machine learning. • Proposes an ideal microstructural architecture for superior creep resistance. Mg-Al based alloys are the dominant commercial magnesium alloys but suffer from inferior creep resistance at elevated temperatures, which strictly limits their application in heat-resistant components. Alloying with rare earth (RE) elements is widely recognized as a critical strategy to enhance their high-temperature performance. This paper systematically reviews the high-temperature creep behavior and strengthening mechanisms of RE-containing Mg-Al alloys. The strengthening mechanism primarily relies on the formation of heat-resistant precipitates and solute segregation. These microstructural features effectively pin dislocations and suppress grain boundary sliding, thereby significantly reducing the creep rate. Following this, current composition design methodologies are highlighted, emphasizing the application of CALPHAD, first-principles calculations, and machine learning. Subsequently, the effects of various RE and alloying elements on microstructural evolution and properties are critically analyzed. Building on the understanding of these strengthening mechanisms, an ideal microstructural architecture tailored for superior creep resistance is proposed, which is distinctly characterized by a robust, thermally stable grain boundary skeleton to suppress grain boundary sliding, coupled with high-density intragranular nano-dispersoids to effectively pin dislocation motion. Furthermore, the influence of processing technologies—casting, heat treatment and thermomechanical processing—on creep performance is summarized. Finally, future development trends are outlined, specifically focusing on innovations in multi-scale integrated computational design, microstructural regulation, and synergistic processing technologies. Such strategies are expected to accelerate the development of next-generation heat-resistant magnesium alloys, satisfying the stringent requirements of aerospace and automotive applications. Composition (language) ; Chemical composition ; Component (thermodynamics) ; Materials processing Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials
Authors: Ningyi Cui | Zhoufa Hu | Xiaopeng Li | Xin Ye | Donghua Liu | Yufan Bao | Jiacheng Ma | Zhengli Zhang | Mingfei Xiao | Tao Zhang | Yecheng Zhou
Source: ACS Applied Energy Materials (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Set of Complementary Descriptors for the Power Conversion Efficiency Predictions of Organic Solar Cells Organic solar cells (OSCs) have undergone rapid development over the past few decades owing to their high efficiency, mechanical flexibility, and potential for low-cost large-scale fabrication. Machine learning (ML) models capable of accurately predicting the power conversion efficiency (PCE) of OSCs can greatly accelerate the discovery of high-performance donor and acceptor materials. In this work, we developed ML models for PCE prediction by introducing a set of complementary molecular descriptors and performing a comprehensive descriptor selection. The resulting models achieved the highest test set R2 value of 0.82 and average R2 value of 0.76 on an updated mixed experimental data set, representing one of the best accuracies reported to date for OSC PCE prediction. Validation using published experimental data confirmed the model’s strong predictive accuracy and generalizability. Our results highlight that the proposed OPT3D descriptors effectively complement the structural information missing from RDKit and Morgan fingerprint descriptors. This study demonstrates that the development of complementary molecular descriptors is critical for improving ML model accuracy in molecular material research and provides a promising approach for the rational design of high-efficiency OSC materials. Complement (music) ; Set (abstract data type) ; Organic solar cell ; Experimental data ; Data set ; Photovoltaic system ; Test set ; Energy conversion efficiency ; Molecular descriptor Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications
Authors: Jeffrey A. Laub | Konstantinos D. Vogiatzis
Source: Chemistry - A European Journal (article, 2026-02-23)
Field: Chemistry — Organic Chemistry

Calix[4]arene Design for Enhanced Carbon Capture via Topological Learning Solid direct air capture (S-DAC) technologies have the potential to remove pre-existing CO<sub>2</sub> from the atmosphere by taking advantage of hydrophobic materials that will retain CO<sub>2</sub> through physisorption and separating it from the abundant water molecules. Calix[n]arenes are highly tunable and versatile complexes with inherent hydrophobic cavities, making them promising candidates for S-DAC technologies. In this study, we investigated four classes of calix[4]arenes, which were functionalized symmetrically at various sites, using machine learning workflows based on molecular topology. The Topology-Guided Carbon Capture with Calix[4]arenes (TopoC<sup>3</sup>) model presented here leverages DFT data from an initial molecular subset together with persistent homology-based topological descriptors to enable targeted molecular discovery. Recognizing the strong dependence of CO<sub>2</sub> host-guest interactions on molecular structure, this study identified key structural and topological features that govern adsorption performance. These insights establish clear design criteria for calix[4]arenes as molecular candidates for CO<sub>2</sub> capture and provide a principled framework for further molecular engineering. Physisorption ; Adsorption ; Topology (electrical circuits) ; Carbon fibers ; Workflow ; Key (lock) ; Molecular recognition Supramolecular Chemistry and Complexes ; Surface Chemistry and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Debendra Meher | Nikhil V. S. Avula | Sundaram Balasubramanian
Source: The Journal of Chemical Physics (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

On the boroxol ring fraction in melt-quenched B2O3 glass: Insights from machine learning potentials An atomistic structural model for melt-quenched B2O3 glass has eluded the simulation community so far. The difficulty lies in the abundance of six-membered boroxol rings-an intermediate-range order motif suggested by Raman and NMR spectroscopy-which is challenging to capture in atomistic molecular dynamics simulations. Here, we report the development of a density functional theory-accurate machine-learned potential and employ quench rates as low as 109 K/s to obtain B2O3 glasses with more than 30% of boron atoms in boroxol rings. Additionally, we show that the pressure, and consequently the boroxol fraction, in the deep potential molecular dynamics simulations critically depends on the range of the geometry descriptor used in the embedding neural network, and it converges beyond a value of 7 Å. The boroxol ring fraction increases with decreasing quench rate. Finally, amorphous B2O3 configurations display a minimum in energy at a boroxol fraction of 75%, remarkably close to the experimental estimate in B2O3 glass. Molecular dynamics ; Raman spectroscopy ; Amorphous solid ; Boron ; Ring (chemistry) ; Fraction (chemistry) ; Potential energy Material Dynamics and Properties ; Glass properties and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Shiyu Hou | Tingchang Shi | Zhen Huang | Jintao Zhao | Xinyi Li | Hongquan Wang | Yuting Gao
Source: Chemical Engineering Journal (article, 2026-02-23)
Field: Chemistry — Organic Chemistry

Machine learning–guided screening of highly efficient multi-cationic photosensitizers for capture–contact–photodynamic antibacterial fabrics Singlet oxygen ; Photodynamic therapy ; Reactive oxygen species ; Quantum yield ; Fluorescence ; Membrane ; Antimicrobial ; Photosensitizer Antimicrobial agents and applications ; Nanoplatforms for cancer theranostics ; Photodynamic Therapy Research Studies
Authors: Pranav Kakhandiki | Sathya R. Chitturi | Daniel Ratner | Sean Gasiorowski
Source: Research Square (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution Scalability ; Artificial neural network ; Metastability ; Energy (signal processing) ; Bayesian probability ; Reduction (mathematics) ; Deep neural networks ; Work (physics) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Kamil Reza Khondakar | Piyush Kalra | Hirak Mazumdar | Suparna Das | Ajeet Kaushik
Source: Microchemical Journal (article, 2026-03-28)
Field: Chemistry — Analytical Chemistry

Advanced sensing and machine learning technologies for economically motivated adulterations (EMA) management: Trends toward sustainability Sustainability ; Big data ; Sustainability science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Pharmaceutical Quality and Counterfeiting
Authors: Izaz Ur Rehman | Muhammad Ishtiaq | Adil Mansoor | Tae-hyun Nam | N. Sivasankara Reddy
Source: Physica Scripta (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Modeling the correlation between composition and Néel transition temperature in Fe-Mn-Al-Cr-Si alloys Abstract The Néel transition temperature (T N ) is a critical parameter governing the magnetic and elastic behavior of Fe–Mn–C–Al–Cr–Si alloys. However, previously proposed empirical equations often fail to capture the complex interactions among alloying elements, limiting their predictive accuracy. In this study, an artificial neural network (ANN) model is developed to quantitatively relate the chemical composition (Mn, C, Al, Cr, Si) to T N . The optimized ANN—featuring a single layer with 8 neurons, a momentum value of 0.3, a learning rate of 0.45, and 24,000 iterations—predicted T N with the highest accuracy. The model achieved an exceptional fit with an adjusted R 2 of 0.98 for the training data and a strong generalization capability with an adjusted R 2 of 0.93 for the test data. ANN predictions were further validated through comparison with established empirical equations reported in the literature, confirming the predictive reliability of the model. Sensitivity analyses were performed to evaluate the individual and combined effects of alloying elements, and the relative importance index was used to quantify their contributions to T N variation. ANN predictions for unseen samples showed excellent agreement with experimental values. The results are further interpreted from a metallurgical perspective, offering deeper insight into the compositional dependence of the Néel transition temperature. Generalization ; Reliability (semiconductor) ; Artificial neural network ; Limiting ; Range (aeronautics) ; Momentum (technical analysis) ; Predictive value ; Value (mathematics) ; Correlation Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Kartik Jhawar | Xiao‐Liu Chu | Joseph B. DeGrandchamp | Yueming Yin | Lalitha Anjali Peddibhotla | Shanaz Banu | Farah Najwa Nabila Mohd Hatta | Yue Wang | Péter Török | Lipo Wang
Source: Scientific Reports (article, 2026-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Morphology-guided deep learning for nanoparticle agglomeration diagnostic assays Accurate and rapid diagnostic tests that are simultaneously affordable remain elusive, typically due to trade-offs between performance and cost. Conventional nucleic acid tests offer high sensitivity but require complex, expensive steps such as amplification and purification, whereas lateral flow assays are simple and low-cost but lack the necessary sensitivity for many applications. New analytical approaches provide an opportunity to bridge this gap, raising sensitivity but not material expense. We present a miniaturized and simplified chip-based platform that combines three components into a single diagnostic pipeline: we use (i) spectrally distinct silver and gold nanoparticles (NPs) that form analyte-dependent clusters with unique spectral fingerprints, (ii) a one-pot, enzyme- and purification-free assay on a chip integrated with a high-throughput automated low-cost microscope, and (iii) a morphology-guided convolutional Graph Neural Network that embeds morphology information into convolutional kernels and performs graph-based relational learning across particle-level features. This integration captures spectral, spatial, and morphological quantification at the particle level, rather than relying on bulk spectral shifts, thereby addressing key limitations observed in representative nanoparticle (NP) assays and image-level deep learning baselines under particle-parallel analysis. Mc-GNN is able to process up to 5,000 particle crops per image in a single computational step, using less than 2 GB of GPU memory. This allows us to achieve femtomolar sensitivity with 98.2% recall for synthetic DNA and 94.8% for SARS-CoV-2 RNA from whole viruses, within in-domain experimental variability arising from different NP combinations and assay conditions. By embedding morphological information into the biosensing pipeline, our diagnostic platform is computationally efficient with millisecond-scale neural compute per raw image on a single consumer-grade GPU and is readily extensible to new analytes and multiplexing, offering a scalable solution for a fieldable diagnostic tool. Convolutional neural network ; Deep learning ; Sensitivity (control systems) ; Artificial neural network ; Hyperspectral imaging ; Nanoparticle ; Pattern recognition (psychology) Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Cell Image Analysis Techniques
Authors: Yang Li | Zhihui Wang | Wei Zhou | Rui Wang | Haiyan Zhang | Shu Zhan | Jiajia Xu
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

FBformer: A four-body feature enhanced periodic graph transformer for crystal property prediction Dihedral angle ; Crystal (programming language) ; Crystal structure prediction ; Property (philosophy) ; Graph ; Bond graph ; Representation (politics) ; Diagrammatic reasoning ; Feature (linguistics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Xin Li | Liang Huang | Sze Jack Tan | Junheng Ling | Xinman Li | Qinqin Liang | Bingyue Zhang | Juanlin Chen | Gang Bai | Erwei Hao | Jiagang Deng | Xiaotao Hou
Source: Microchemical Journal (article, 2026-01-10)
Field: Chemistry — Analytical Chemistry

Rapid discrimination and quantitative analysis of amino acids, calcium carbonate of three source species of Ostreae concha based on NIR spectroscopy and chemometrics tools Chemometrics ; Ostrea edulis ; Calcium carbonate ; Oyster ; Feature selection ; Linear discriminant analysis ; Pattern recognition (psychology) ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kai Sellschopp
Source:  (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Computational design of metal hydrides for hydrogen storage In a sustainable economy built on renewable energy, hydrogen plays a key role for storing energy and replacing fossil fuels. An efficient way to store hydrogen is to keep it in the solid state by binding it chemically in a metal hydride, which is particularly useful for seasonal energy storage or for applications where safety is a concern. Since experimental studies with hydrogen can be challenging and time-consuming, computational tools are extremely valuable for gaining a deeper understanding of the processes underlying the (de-)hydrogenation reaction. In this seminar, I will highlight our recent work on predicting the thermodynamics, interface energies, and volume expansion of metal-hydrogen systems using different computational methods. We leverage available experimental and computational data to build machine learning models as well as more sophisticated thermodynamic models for the calculation of phase diagrams (CALPHAD) to find novel metal hydride compositions and fine-tune existing alloys. Additionally, atomic-scale calculations based on density functional theory (DFT) are employed to model interfaces between the metal and hydride phases to inform meso-scale models and gain insights into nucleation barriers. As hydrogen is the lightest element of the periodic table, we also perform simulations that go beyond the classical approximation and test what happens if we treat the whole hydrogen atom as a quantum object instead. Hydrogen storage ; Hydride ; Hydrogen ; Density functional theory ; Hydrogen economy ; Leverage (statistics) ; Scalability ; Work (physics) Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Bangchen Yin | Jian Ouyang | Zhen Fan | Kailai Lin | Hanshi Hu | Dingshun Lv | Weiluo Ren | Hai Xiao | Ji Chen | Changsu Cao
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

Hessian-informed machine learning interatomic potential towards bridging theory and experiments Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling accurate analysis of associated thermodynamic and kinetic phenomena. To make Hessian supervision practically viable, we develop a highly efficient training protocol, termed Hessian INformed Training (HINT), achieving two to four orders of magnitude reduction for the requirement of expensive Hessian labels. HINT integrates critical techniques, including Hessian pre-training, configuration sampling, curriculum learning and stochastic projection Hessian loss. Enabled by HINT, Hi-MLIP significantly improves transition-state search and brings Gibbs free-energy predictions close to chemical accuracy especially in data-scarce regimes. Our framework also enables accurate treatment of strongly anharmonic hydrides, reproducing phonon renormalization and superconducting critical temperatures in close agreement with experiment while bypassing the computational bottleneck of anharmonic calculations. These results establish a practical route to enhancing curvature awareness of machine learning interatomic potentials, bridging simulation and experimental observables across a wide range of systems. Hessian matrix ; Observable ; Curvature ; Anharmonicity ; Bridging (networking) ; Bottleneck Machine Learning in Materials Science ; Quantum many-body systems ; Inorganic Chemistry and Materials
Authors: Xiao Chen | Dehao Kong | Xuan He | Taihong Huang | Peng Song
Source: Materials Letters (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

A novel quantum mixed kernel regression model for prediction of oxidation mass gain in Ni-based superalloys Benchmark (surveying) ; Kernel (algebra) ; Nonlinear system ; Superalloy ; Basis function ; Generalization ; Piecewise linear function ; Nonlinear regression ; Similarity (geometry) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High-Temperature Coating Behaviors