jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chinmoy Saha | Justin Whittington | Md Abdus Sabuj | Neeraj Rai
Source: Machine Learning Engineering (article, 2025-06-10)
Field: Materials Science — Polymers and Plastics

Predicting electronic properties of <i>π</i>-conjugated organic polymers using machine learning for optoelectronic applications Conjugated system ; Polymer ; Materials science ; Optoelectronics ; Computer science ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Marko G. Nikolić | Dragutin Sevic | M.S. Rabasović
Source: Photonics (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Improving the High-Pressure Sensing Characteristics of Y2MoO6:Eu3+ Using a Machine Learning Approach In this study, we explore the potential of applying machine learning (ML) to enhance high-pressure luminescence sensing. We investigate the luminescence behavior of Y2MoO6:Eu3+, synthesized via a self-initiated, self-sustained reaction. Emission spectra were collected under varying pressures using a 405 nm laser diode and an AVANTES AvaSpec 2048TEC USB2 spectrometer. An analysis of the pressure-dependent curve, based on the intensities of two key peaks, indicates a possible crystal phase transition or another underlying physical phenomenon. Moreover, the non-unique relationship between pressure and peak intensity limits its effectiveness for precise sensing. To overcome this challenge, we employ an ML-based approach, combining Uniform Manifold Approximation and Projection (UMAP) for data visualization with a deep neural network to estimate pressure directly from the full luminescence spectrum. This strategy significantly extends the usable pressure range of Y2MoO6:Eu3+ up to 12 GPa, representing a marked improvement over conventional methods. Luminescence ; USable ; Artificial neural network ; Projection (relational algebra) ; Visualization ; Manifold (fluid mechanics) ; Light-emitting diode ; Diode Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Atomic and Subatomic Physics Research
Authors: Ehsan Alibagheri | Mohammad Khazaei | Mehdi Estili | Alireza Seyfi | Hiroshi Mizoguchi | Kaoru Ohno | Hideo Hosono | S. Mehdi Vaez Allaei
Source: Institutional Repositories DataBase (IRDB) (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

LAX phases: A family of novel stable layered materials, informatics‐based discovery Ternary MAX phases, characterized by the chemical formula M₂AX, represent a group of layered materials with hexagonal lattices. These MAX phases have been the subject of extensive experimental and theoretical studies. Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals, such as Rh, Ru, Pt, Pd, Co, and Ni, are unlikely to form. Here, we introduce an alternative family of orthorhombic and monoclinic materials, the LAX phases, which exhibit similarities to MAX phases in terms of their layered structure and A and X elements. However, LAX materials incorporate late transition metals in place of the early transition metals. Advanced techniques for predicting the crystal structure of materials, coupled with data-driven materials research and machine learning algorithms, were employed to investigate the stable structures containing transition metals from the last groups of the d-block elements. The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition, with 100 of these systems showing dynamic stability. An in-depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties, outperforminMAX phase counterparts in Young's modulus, stiffness, and hardness. These findings indicate that many LAX phase structures are viable candidates for future synthesis, highlighting the potential of heuristic-based structure searches in material discovery. Stability (learning theory) ; Sequence (biology) ; Representation (politics) ; Class (philosophy) Machine Learning in Materials Science ; 2D Materials and Applications ; Crystal Structures and Properties
Authors: Sitanan Sartyoungkul | S. Balasubramaniyan | Pavel Sidorov | Yuuya Nagata
Source: ChemRxiv (preprint, 2025-09-11)
Field: Chemistry — Spectroscopy

Automated Synthesis and Fragment Descriptor–BasedMachine Learning for Retention Time Predictionin Supercritical Fluid Chromatography The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances. Here, we report a workflow that combines automated synthesis of a structurally diverse amide library with fragment descriptor–based ML for retention time prediction in supercritical fluid chromatography (SFC). Retention data were systematically acquired on the recently developed DCpak® PBT column, providing one of the first structured datasets for this stationary phase. Benchmarking revealed that fragment-count descriptors (ChyLine and CircuS) substantially outperformed conventional molecular fingerprints, delivering higher predictive accuracy and more interpretable relationships between substructures and retention behavior. External validation underscored the role of chemical space coverage, while visualization techniques such as ColorAtom analysis offered mechanistic insight into model decisions. By uniting automated synthesis with chemoinformatics-driven ML, this study demonstrates a scalable approach to generating high-quality training data and predictive models for chromatography. Beyond retention prediction, the framework exemplifies how data-centric strategies can accelerate column characterization, reduce reliance on trial-and-error experimentation, and advance the development of autonomous, high-throughput analytical workflows. Workflow ; Retention time ; Fragment (logic) ; Visualization ; Column (typography) ; Benchmarking ; Supercritical fluid chromatography ; Chemical space ; Resolution (logic) Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Aileen Luo | Tao Zhou | Ming Du | Martin V. Holt | Andrej Singer | Mathew J. Cherukara
Source: npj Computational Materials (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis Abstract Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods. Artificial neural network ; Diffraction ; Convolution (computer science) ; Orientation (vector space) ; Convolutional neural network ; Deep learning ; Unsupervised learning ; Beam (structure) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray Imaging Techniques
Authors: Gautam Ranjan | Ravi Sharma | B. Kiran Naik | Vivek Singh
Source: Heat Transfer Engineering (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Thermal Characterization and Performance Estimation of Pure Single Crystal Tungsten Heat Switch by Using Advance Neural Network Single crystal ; Characterization (materials science) ; Thermal ; Tungsten ; Artificial neural network ; Single stage Advanced Thermoelectric Materials and Devices ; Metallurgical and Alloy Processes ; Welding Techniques and Residual Stresses
Authors: Chengze Yang
Source: ChemRxiv (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Quantum Computing and Artificial Neural Network methods in approximating the Quantum Many-Body Fermion System for Modern Quantum Chemistry Applications Artificial neural network ; Quantum ; Quantum computer ; Quantum Monte Carlo ; Deep learning ; Ab initio ; Electronic structure ; Leverage (statistics) ; Scalability ; Restricted Boltzmann machine Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Yue Shi (128342) | Jiaxin Tian (1470667) | Haoyuan Li (1801717)
Source: Figshare (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

MlCOFSyn: A MachineLearning Framework To Facilitatethe Synthesis of 2D Covalent Organic Frameworks Two-dimensional covalent organic frameworks (2D COFs) have been historically synthesized empirically, often resulting in uncontrolled crystallization and inferior crystal sizes, which limit their performance in various applications. Recently, crystallization models tailored for 2D COFs have been developed that demonstrate great potential in facilitating their rational synthesis. Nevertheless, effective strategies to leverage these models for 2D COF synthesis remain underdeveloped, and the specialized expertise required, combined with the high computational costs of exploring the vast chemical space, poses additional barriers to their practical application. In this work, we present a machine learning framework, named MlCOFSyn, that is designed to assist in the synthesis of 2D COFs. This framework explores the application of 2D COF crystallization models by implementing three pivotal functionalities: predicting crystal sizes based on the input monomer addition sequence, reverse-engineering monomer addition sequences to achieve desired crystal sizes, and optimizing monomer addition sequences to produce larger crystals. These functionalities are critical for the controlled synthesis of 2D COFs but have been largely underexplored due to the lack of accessible theoretical tools. The MlCOFSyn framework leverages efficient machine-learning algorithms and features an intuitive graphical interface, enabling its use on consumer-grade computers by nonexperts. By addressing these gaps, the MlCOFSyn framework represents a substantial advancement in facilitating 2D COF research and synthesis. Leverage (statistics) ; Crystallization ; Monomer ; Covalent bond ; Limit (mathematics) Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis ; Surface Chemistry and Catalysis
Authors: Sadettin Y. Ugurlu
Source: Journal of Solution Chemistry (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Inter-Pol: An Interpretable Machine Learning Framework for Solvent Polarity Prediction Polarity (international relations) ; Transparency (behavior) ; Feature (linguistics) ; Artificial neural network ; Set (abstract data type) ; Quantitative structure–activity relationship ; Usability ; Solvent polarity ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: S. A. Dolenko | Kirill Laptinskiy | A. A. Korepanova | Sergey Burikov | Tatiana A. Dolenko
Source: Optical Memory and Neural Networks (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Intelligent Control of the Synthesis of Luminescent Carbon Dots with the Desired Photoluminescence Quantum Yield Using Machine Learning Photoluminescence ; Quantum yield ; Yield (engineering) ; Luminescence ; Quantum dot ; Carbon fibers ; Computer science ; Carbon quantum dots ; Materials science ; Control (management) ; Artificial intelligence ; Optoelectronics ; Nanotechnology ; Physics ; Optics ; Composite material ; Algorithm Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Heng Liang | Chenghao Ye | Ting Zeng | Jinzhe Zhang | Xiang‐Kui Gu
Source: The Journal of Physical Chemistry C (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Exploring the Evolution of Water Structures on ZnO Surfaces Using Deep Learning The interactions between water molecules and ZnO surfaces play a critical role in thermochemical, photochemical, and electrochemical processes, particularly in proton transfer reactions. However, in practical applications, the presence of water chains or clusters formed by adsorbed water molecules can obscure the actual adsorption configurations of other competing reactants. Therefore, a comprehensive understanding of single and dimer water dissociation, the formation of water chains and clusters, and the influence of surface reconstruction is essential for advancing ZnO-based interfacial catalysis. This study develops accurate neural network potentials (NNPs) for both polar and nonpolar ZnO surfaces and water molecules, based on ab initio molecular dynamics (AIMD) data. These NNPs enable efficient, long-time simulations with reduced computational costs. We identified distinct water dissociation mechanisms on ZnO surfaces and simulated a 50% dissociation rate, in agreement with experimental results. The NNPs also revealed the formation of water chains and clusters on ZnO surfaces. Additionally, we explored the dissociation behavior of the polar ZnO surface, which undergoes structural deformation, adopting a configuration similar to the nonpolar surface, although the dissociation degree remains lower. These findings provide valuable insights into interfacial water behavior on ZnO for catalysis and material applications. Dissociation (chemistry) ; Water dimer ; Adsorption ; Dimer ; Molecule ; Polar ; Molecular dynamics ; Self-ionization of water Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Zisheng Zhang | Frank Abild‐Pedersen
Source: The Journal of Physical Chemistry Letters (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Unlocking Switchable Reactivity of MBene via Asymmetric Surface Adsorption The Sabatier principle and activity volcano have guided and constrained catalyst design. Surpassing these limitations requires going beyond the static view of catalysis. Here, we propose 2D metal boride (MBene) as a promising model system for dynamic catalysis, exemplified by the nitrogen reduction reaction (N<sub>2</sub>RR). The surface reactivity of B-rich MBene can be altered by binding organic ligands to the opposite side of the N<sub>2</sub>RR site. The change in reactivity originates in structural distortions of the metal and boron layers, leading to an energy span of up to 0.8 eV. By cycling the ligands or running a coupled reaction on the opposite side, we could switch between under- and overbinding energetics to surpass the Sabatier limit and access the inverse activity volcano. The reactivity space considering various metals and ligands can be efficiently explored by interpretable machine learning based on geometric descriptors, and promising ligand-bound MBenes for various catalytic scenarios are proposed. Reactivity (psychology) ; Catalysis ; Adsorption ; Metal ; Redox ; Surface (topology) ; Transition metal ; Inverse MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Boron and Carbon Nanomaterials Research
Authors: Heng Liang (813618) | Chenghao Ye (5368865) | Ting Zeng (1521649) | Jinzhe Zhang (128223) | Xiang-Kui Gu (1360686)
Source: Figshare (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Exploring the Evolutionof Water Structures on ZnOSurfaces Using Deep Learning The interactions between water molecules and ZnO surfaces play a critical role in thermochemical, photochemical, and electrochemical processes, particularly in proton transfer reactions. However, in practical applications, the presence of water chains or clusters formed by adsorbed water molecules can obscure the actual adsorption configurations of other competing reactants. Therefore, a comprehensive understanding of single and dimer water dissociation, the formation of water chains and clusters, and the influence of surface reconstruction is essential for advancing ZnO-based interfacial catalysis. This study develops accurate neural network potentials (NNPs) for both polar and nonpolar ZnO surfaces and water molecules, based on ab initio molecular dynamics (AIMD) data. These NNPs enable efficient, long-time simulations with reduced computational costs. We identified distinct water dissociation mechanisms on ZnO surfaces and simulated a 50% dissociation rate, in agreement with experimental results. The NNPs also revealed the formation of water chains and clusters on ZnO surfaces. Additionally, we explored the dissociation behavior of the polar ZnO surface, which undergoes structural deformation, adopting a configuration similar to the nonpolar surface, although the dissociation degree remains lower. These findings provide valuable insights into interfacial water behavior on ZnO for catalysis and material applications. Dissociation (chemistry) ; Water dimer ; Adsorption ; Dimer ; Molecule ; Polar ; Molecular dynamics ; Self-ionization of water Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Chemical and Physical Properties of Materials
Authors: Adil Kabylda (20542497) | J. Thorben Frank (22150562) | Sergio Suárez-Dou (22150565) | Almaz Khabibrakhmanov (16470600) | Leonardo Medrano Sandonas (2940957) | Oliver T. Unke (3719704) | Stefan Chmiela (14003045) | Klaus-Robert Müller (1360080) | Alexandre Tkatchenko (1283865)
Source: Figshare (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Molecular Simulationswith a Pretrained Neural Networkand Universal Pairwise Force Fields Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Unke et al., Sci. Adv. <b>2024</b>, <i>10</i>, eadn4397]. This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semilocal interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions. SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the PBE0+MBD level of quantum mechanics, ensuring broad coverage of covalent and noncovalent interactions. Our approach is characterized by computational and data efficiency, scalability to 200 thousand atoms on a single GPU, and reasonable to high accuracy across the chemical space of organic (bio)molecules. SO3LR is applied to study units of four major biomolecule types, polypeptide folding, and nanosecond dynamics of larger systems such as a protein, a glycoprotein, and a lipid bilayer, all in explicit solvent. Finally, we discuss future challenges toward truly general molecular simulations by combining MLFFs with traditional atomistic models. Scalability ; Pairwise comparison ; Artificial neural network ; Molecular dynamics ; Set (abstract data type) ; Force field (fiction) ; Field (mathematics) ; Key (lock) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Mehmet Aziz Yirik | Jakob L. Andersen | Rolf Fagerberg | Daniel Merkle
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Electrochemistry

ChemReservoir—An Open-Source Framework for Chemically-Inspired Reservoir Computing Reservoir computing is a type of a recurrent neural network, mapping the inputs into higher dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir and Nguyen developed a DNA chemistry-inspired tool based on Gillespie algorithm. However, these software tools were designed mainly with the focus on DNAchemistry and their maintenance status has limited their current usability. Due to these limitations, there was a need for a proper open-source tool. This study introduces ChemReservoir, an open-source framework for chemically-inspired reservoir computing. In contrast to the former studies focused on DNA-chemistry, ChemReservoir is a general framework for the construction and analysis of chemically-inspired reservoirs, which also addresses the limitations in these previous studies by ensuring enhanced testing, evaluation, and reproducibility. The tool was evaluated using various cycle-based reservoir topologies and demonstrated stable performance across a range of configurations in memory capacity tasks. Computer science ; Open source ; Reservoir computing ; Operating system ; Artificial intelligence ; Software ; Artificial neural network Electrochemical Analysis and Applications ; Neural Networks and Applications ; Neural Networks and Reservoir Computing
Authors: Zisheng Zhang (3740830) | Frank Abild-Pedersen (1342020)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Unlocking Switchable Reactivity of MBene via AsymmetricSurface Adsorption The Sabatier principle and activity volcano have guided and constrained catalyst design. Surpassing these limitations requires going beyond the static view of catalysis. Here, we propose 2D metal boride (MBene) as a promising model system for dynamic catalysis, exemplified by the nitrogen reduction reaction (N<sub>2</sub>RR). The surface reactivity of B-rich MBene can be altered by binding organic ligands to the opposite side of the N<sub>2</sub>RR site. The change in reactivity originates in structural distortions of the metal and boron layers, leading to an energy span of up to 0.8 eV. By cycling the ligands or running a coupled reaction on the opposite side, we could switch between under- and overbinding energetics to surpass the Sabatier limit and access the inverse activity volcano. The reactivity space considering various metals and ligands can be efficiently explored by interpretable machine learning based on geometric descriptors, and promising ligand-bound MBenes for various catalytic scenarios are proposed. Reactivity (psychology) ; Catalysis ; Adsorption ; Metal ; Redox ; Surface (topology) ; Transition metal ; Inverse MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Ammonia Synthesis and Nitrogen Reduction