jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yasir Ghunaim | Andrés Villa | Gergő Ignácz | György Székely | Motasem Alfarra | Bernard Ghanem
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Towards Faster and More Compact Foundation Models for Molecular Property Prediction Advancements in machine learning for molecular property prediction have improved accuracy but at the expense of higher computational cost and longer training times. Recently, the Joint Multi-domain Pre-training (JMP) foundation model has demonstrated strong performance across various downstream tasks with reduced training time over previous models. Despite JMP's advantages, fine-tuning it on molecular datasets ranging from small-scale to large-scale requires considerable time and computational resources. In this work, we investigate strategies to enhance efficiency by reducing model size while preserving performance. To better understand the model's efficiency, we analyze the layer contributions of JMP and find that later interaction blocks provide diminishing returns, suggesting an opportunity for model compression. We explore block reduction strategies by pruning the pre-trained model and evaluating its impact on efficiency and accuracy during fine-tuning. Our analysis reveals that removing two interaction blocks results in a minimal performance drop, reducing the model size by 32% while increasing inference throughput by 1.3x. These results suggest that JMP-L is over-parameterized and that a smaller, more efficient variant can achieve comparable performance with lower computational cost. Our study provides insights for developing lighter, faster, and more scalable foundation models for molecular and materials discovery. The code is publicly available at: https://github.com/Yasir-Ghunaim/efficient-jmp. Scalability ; Reduction (mathematics) ; Pruning ; Inference ; Block (permutation group theory) ; Computational complexity theory ; Throughput ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhendong Li | Hongwei Fu | Steven A. Lopez | Jingbai Li
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Machine learning photodynamics reveal intersystem-crossing-driven ladderdiene ring opening Photochemical ring-opening reactions have become an essential tool for chemical syntheses under mild conditions with high atom economy. We propose a near-visible light-induced electrocyclic ring-opening reaction to afford cyclooctatetraene (COT) using carbonyl-functionalized tricyclooctadiene (1) based on our machine learning (ML) accelerated photodynamics simulations. Our CAM-B3LYP/cc-pVDZ calculations show that carbonyl group reduce the S<sub>1</sub>-energy of 1 to 3.65 eV (340 nm) from 6.25 eV, approaching the visible light range. The multiconfigurational CASSCF(12,11)/ANO-RCC-VDZP calculations show small S<sub>1</sub> and T<sub>1</sub> energy gaps near an S<sub>1</sub>-minimum region. Our ML-photodynamics simulations with 1000 FSSH trajectories revealed a stepwise ring-opening mechanism of 1 from the S<sub>1</sub>, dominated by relatively fast S<sub>1</sub>/T<sub>1</sub> intersystem crossings in 20 ps. The trajectories predict that the quantum yield to carbonyl-functionalized COT is 89%, suggesting the light-induced ring-opening reaction of 1 is highly efficient. This work demonstrates a predictive ML-photodynamics application for photochemical reaction design. Intersystem crossing ; Photochemistry ; Quantum yield ; Ring (chemistry) ; Chemistry ; Visible spectrum ; Yield (engineering) ; Excited state ; Materials science ; Fluorescence ; Atomic physics ; Physics ; Optoelectronics ; Singlet state ; Thermodynamics Radical Photochemical Reactions ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Peidong Liang | Bo Liu | Ruiqiang Lu | Jing Li | Yi Tan | Huanxiang Liu | Xiaojun Yao
Source: Chinese Chemical Letters (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Bond-editing driven electron flow mapping for reaction mechanism modeling Interpretability ; Chemical reaction ; Mechanism (biology) ; Bond graph ; Reaction mechanism ; Signal-flow graph ; Inference ; Chemical process ; Electron transfer Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Rebecca
R. Brew (21074024) | Ian A. Nelson (21074027) | Meruyert Binayeva (3073779) | Amlan S. Nayak (21074030) | Wyatt J. Simmons (21074033) | Joseph J. Gair (9904951) | Corin C. Wagen (13283431)
Source: Figshare (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Wiggle150: BenchmarkingDensity Functionals and NeuralNetwork Potentials on Highly Strained Conformers Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving nonequilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To address this gap, we introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz. These geometriesgenerated via metadynamics and scored using DLPNO–CCSD(T)/CBS reference energiesexhibit substantially larger deviations in bond lengths, angles, dihedrals, and relative energies than other conformer benchmarks. We evaluate a diverse array of computational methods, including density-functional theory, composite quantum chemical methods, semiempirical models, neural network potentials, and force fields, on predicting relative energies for this challenging benchmark set. The results highlight multiple methods along the speed–accuracy Pareto frontier and identify AIMNet2 as particularly robust among the NNPs surveyed. We anticipate that Wiggle150 will be used to validate computational protocols involving nonequilibrium systems and guide the development of new density functionals and neural network potentials. Metadynamics ; Benchmark (surveying) ; Non-equilibrium thermodynamics ; Artificial neural network ; Robustness (evolution) ; Ab initio ; Benchmarking ; Quantum Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Chandra Has | Sanjay Kumar Kureel
Source: Journal of Liposome Research (article, 2025-12-25)
Field: Materials Science — Biomaterials

Advances in microfluidic and artificial intelligence-assisted design of liposomes for drug delivery applications Liposomes are used as a vehicle in targeted drug delivery due to inherent biocompatibility and encapsulative potential for diverse bioactive agents. However, consistent production remains challenging. With the help of microfluidic systems, liposome synthesis can be precisely controlled, conferring consistency and scalability. This review focuses on the integration of innovative microfluidic-based liposome synthesis and artificial intelligence (AI), specifically machine learning (ML), for the design of optimal critical quality attributes (CQAs), such as vesicle size, size distribution and encapsulation efficiency. ML models such as artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) can predict liposome quality with limited input data. Together with design-of-experiment (DoE), these paradigms provide data-driven optimization. However, the interpretability of the model is enhanced by explainable AI (XAI) tools such as SHAP, which identify determinants for every prediction. The integration of these methods makes an advance toward robust and scalable liposome design. We have also summarized the challenges and future perspectives of scaling AI-guided microfluidic platforms into mainstream pharmaceutical development. Liposome ; Microfluidics ; Drug delivery ; Interpretability ; Quality by Design ; Scalability ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Pickering emulsions and particle stabilization
Authors: Qinze Yu | L Zhan | Xinjian Cao | Tingting Wang | Haolong Fan | Zhenru Zhou | Huixia Yang | Teng Zhang | Quanzhen Zhang | Yeliang Wang
Source: Physchem (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Predicting the Bandgap of Graphene Based on Machine Learning Over the past decade, two-dimensional materials have become a research hotspot in chemistry, physics, materials science, and electrical and optical engineering due to their excellent properties. Graphene is one of the earliest discovered 2D materials. The absence of a bandgap in pure graphene limits its application in digital electronics where switching behavior is essential. In the present study, researchers have proposed a variety of methods for tuning the graphene bandgap. Machine learning methodologies have demonstrated the capability to enhance the efficiency of materials research by automating the recording of characteristic parameters from the discovery and preparation of 2D materials, property calculations, and simulations, as well as by facilitating the extraction and summarization of governing principles. In this work, we use first principle calculations to build a dataset of graphene band gaps under various conditions, including the application of a perpendicular external electric field, nitrogen doping, and hydrogen atom adsorption. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) Regression were utilized to successfully predict the graphene bandgap, and the accuracy of the models was verified using first principles. Finally, the advantages and limitations of the three models were compared. Graphene ; Band gap ; Automatic summarization ; Support vector machine ; Random forest ; Multilayer perceptron Graphene research and applications ; Machine Learning in Materials Science ; Graphene and Nanomaterials Applications
Authors: M. Adeel Ajaib | Fariha Nasir | Abdul Rehman
Source: ArXiv.org (preprint, 2025-08-09)
Field: Materials Science — Materials Chemistry

Explainable AI for Curie Temperature Prediction in Magnetic Materials We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several machine learning models. We find that the Extra Trees Regressor delivers the best performance reaching an R^2 score of up to 0.85 $\pm$ 0.01 (cross-validated) for a balanced dataset. We employ the k-means clustering algorithm to gain insights into the performance of chemically distinct material groups. Furthermore, we perform the SHAP analysis to identify key physicochemical drivers of Curie behavior, such as average atomic number and magnetic moment. By employing explainable AI techniques, this analysis offers insights into the model's predictive behavior, thereby advancing scientific interpretability. Curie temperature ; Curie ; Cluster analysis ; Key (lock) ; Marie curie Machine Learning in Materials Science
Authors: Haruto Seki (21837361) | Kenji Nawa (19362571) | Chiharu Mitsumata (14132940) | Toyo Kazu Yamada (2036215)
Source: Figshare (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Tuning EpitaxialGrowth of Atomically Thin MnTe Filmson Fe(001) for Creating van der Waals 2D Magnets Layered two-dimensional (2D) van der Waals magnetic crystals have predominantly been synthesized using 3<i>d</i> transition metals and chalcogenides via chemical vapor transport. However, for device applications, it remains critical to establish growth conditions that enable epitaxial, layer-by-layer film formation under vacuum, particularly on atomically flat solid substrates. In this study, we investigate manganese (Mn) and tellurium (Te) as a model system, due to the layered Mn–Te compound’s intriguing topological and magnetic properties. A clean body-centered-cubic Fe(001) substrate was used because it supports the epitaxial, layer-by-layer growth of atomically flat body-centered tetragonal Mn(001) films. Our findings using scanning tunneling microscopy and spectroscopy (STM/STS) combined with density functional theory calculation reveal that the direct growth of layered Mn/Te films on the ferromagnetic Fe(001) substrate is challenging, even at 300 K. The resulting surface comprises a mixture of Te, Te/Mn, Mn/Te, and MnTe alloy regions. However, post-annealing up to ∼650 K induces a transformation from these metastable states into a thermodynamically stable MnTe alloy phase, forming an atomically flat 2D surface. This study further highlights how combining machine learning with STM/STS enables precise chemical classification and accurate identification of multiple local density of states types, even within structurally complex or mixed-phase systems. van der Waals force ; Scanning tunneling microscope ; Ferromagnetism ; Substrate (aquarium) ; Epitaxy ; Density functional theory ; Tetragonal crystal system ; Metastability ; Topological insulator 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Muhammad Fadhlan Anshor
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Supplementary files for CO Coadsorption Effects on Water-Gas Shift Reaction over Cu Clusters on Cu(111): Insights from Machine Learning Force Field and Microkinetic Modeling Supplementary files for the paper submitted in PCCP (Physical Chemistry Chemical Physics). Field (mathematics) ; Force field (fiction) ; Potential field ; Reaction mechanism Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Weiyi Xia (711340) | Wei-Shen Tee (21303270) | Paul Canfield (14062124) | Rebecca Flint (21303273) | Cai-Zhuang Wang (267930)
Source: Figshare (article, 2025-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Search forStable and Low-Energy Ce–Co–CuTernary Compounds Using Machine Learning Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce–Co–Cu ternary compounds using a machine learning (ML)-guided framework integrated with first-principles calculations. We employ a crystal graph convolutional neural network (CGCNN), which enables efficient screening for promising candidates, significantly accelerating the material discovery process. With this approach, we predict five stable compounds, Ce<sub>3</sub>Co<sub>3</sub>Cu, CeCoCu<sub>2</sub>, Ce<sub>12</sub>Co<sub>7</sub>Cu, Ce<sub>11</sub>Co<sub>9</sub>Cu, and Ce<sub>10</sub>Co<sub>11</sub>Cu<sub>4</sub>, with formation energies below the convex hull, along with hundreds of low-energy (possibly metastable) Ce–Co–Cu ternary compounds. First-principles calculations reveal that several structures are both energetically and dynamically stable. Notably, two Co-rich low-energy compounds, Ce<sub>4</sub>Co<sub>33</sub>Cu and Ce<sub>4</sub>Co<sub>31</sub>Cu<sub>3</sub>, are predicted to have high magnetizations. Ternary operation ; Convolutional neural network ; Regular polygon ; Artificial neural network ; Intermetallic ; Graph ; Convex combination Magnetic Properties of Alloys ; Rare-earth and actinide compounds ; Machine Learning in Materials Science
Authors: Ruoyan Jin | A. Hamedani | Andrea E. Sand
Source: Machine Learning Science and Technology (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Data-efficient machine-learning interatomic potential for studying radiation effects in germanium Abstract Exposure to harsh radiation environments leads to displacement damage in semiconductor materials, ultimately degrading the device performance. Molecular dynamics (MD) is a powerful method for simulating the dynamic processes of radiation-induced defect generation, clustering, and evolution, which are often beyond the reach of experimental observation. In this work, we introduce a machine-learning Gaussian approximation potential for germanium, specifically designed to describe properties related to radiation-induced collision cascades and the resulting damage. A repulsive potential is incorporated to accurately capture short-range interactions during cascade simulations. In addition, we reproduce elastic, thermal, and vibrational properties, and accurately capture the energetics of vacancies and self-interstitials. These high-fidelity predictions are comparable to ab initio calculations while requiring only a compact training database. This potential is expected to facilitate accurate MD simulations of radiation damage in germanium with greatly improved accuracy compared to empirical potentials. Germanium ; Interatomic potential ; Cascade ; Radiation ; Molecular dynamics ; Radiation damage ; Displacement (psychology) ; Gaussian Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Advancements in Semiconductor Devices and Circuit Design
Authors: Patrick Weber | Werner Wagner | Steffen Freitag
Source: Computational Mechanics (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

ANN material modeling for SMA fibers enhanced with a physical constraint and its application to FE computations Abstract So-called shape memory alloys (SMAs) show intriguing multi-physical and history-dependent behavior. This includes most prominently the recovery of their initial shape after inelastic deformation, if the temperature is increased afterwards. This is known as the shape memory effect. The precise and reliable description of this and other SMA phenomena is crucial for industrial applications. Therefore, in addition to the wide range of analytical material models for SMA, we want to apply the material modeling strategy with artificial neural networks (ANN) to SMAs. We define an ANN material model in order to represent the SMA behavior with a feedforward ANN. Therefore, the correct setup of input and output vectors for rate-independent material behavior is investigated. The training is done based on synthetic data. The resulting SMA ANN material model is able to represent the SMA strain–stress behavior generally, for arbitrary strain and temperature fields. The resulting one-dimensional ANN material model is used within finite element computations. This increases the accuracy requirements due to the need for a material tangent. Therefore, we improve the performance of the ANN material model in terms of numerical stability by enforcing a material tangent related constraint during the ANN training process. In order to evaluate the performance of ANN material models during training reliably for these accuracy requirements, in depth studies on different target variables during the training process are done. SMA* ; Shape-memory alloy ; Artificial neural network ; Computation ; Tangent ; Computer science ; Finite element method ; Process (computing) ; Deformation (meteorology) ; Constraint (computer-aided design) ; Feed forward ; Material properties ; Mechanical engineering ; Structural engineering ; Control engineering ; Artificial intelligence ; Algorithm ; Materials science ; Engineering ; Mathematics ; Geometry ; Composite material Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Composite Structure Analysis and Optimization
Authors: Tiantian Gao | Yufeng Wu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Ionic Conductivity in Halide-Based Solid-State Electrolytes Using Relational Network Model Bottleneck ; Conductivity ; Ionic conductivity ; Electrolyte ; Network model ; Credibility ; Mean squared error ; Experimental data Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Shuhao Zhang (3804106) | Roman Zubatyuk (5485025) | Yinuo Yang (4975805) | Adrian Roitberg (1371537) | Olexandr Isayev (1352004)
Source: Figshare (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

ANI-1xBB: An ANI-BasedReactive Potential for SmallOrganic Molecules Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing those of conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive data sets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena. Pericyclic reaction ; Reactive intermediate ; Dissociation (chemistry) ; Work (physics) ; Chemical reaction ; Reaction mechanism ; Molecule ; Organic molecules Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Ehsan Ghane | Martin Fagerström | Mohsen Mirkhalaf
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Multi-fidelity Data Fusion for In-elastic Woven Composites: Combining Recurrent Neural Networks with Transfer Learning Transfer of learning ; Materials science ; Composite material ; Fidelity ; Artificial neural network ; Sensor fusion ; Fusion ; Transfer (computing) ; Computer science ; Artificial intelligence ; Telecommunications Textile materials and evaluations ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Zhicheng Zhong | Xuejian Qin | Taoyuze Lv
Source: Research Square (preprint, 2025-09-29)
Field: Materials Science — Materials Chemistry

EAC-Net: Predicting real-space charge density via equivariant atomic contributions Generalization ; Charge (physics) ; Equivariant map ; Density functional theory ; Scalability ; Charge density ; Electronic structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques