jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Angran Xia | YEMAO XIA
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Efficient MACE Force Field Construction Based on Transfer Learning and Study on Short-Range Ordering Strengthening Mechanism in AlCoCrFeNi Alloys Quinary ; Lattice (music) ; Thermal ; Softening ; Thermal expansion ; Molecular dynamics Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; Microstructure and mechanical properties
Authors: Yutao Xue | Lirui Yang | Zhen Zhang | Zhanyong Zhao | Liqing Wang | Kai Ma | Yunlong Li | Peikang Bai
Source: Journal of Electroanalytical Chemistry (article, 2026-04-06)
Field: Materials Science — Biomaterials

Electrochemical noise images and deep transfer learning based on corrosion types identification of AZ31 Mg alloy Electrochemical noise ; Corrosion ; Alloy ; Electrochemistry ; Identification (biology) ; Noise (video) Magnesium Alloys: Properties and Applications ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: J. Oppliger | M. Stifter | A. Rüegg | I. Biało | Lucio Martinelli | P. G. Freeman | D. Prabhakaran | J. W. Zhao | Q. Daniel Wang | J. F. Chang
Source: arXiv (Cornell University) (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

Autonomous Diffractometry Enabled by Visual Reinforcement Learning Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain challenging. For example, crystal alignment strongly relies on humans with the ability to comprehend diffraction patterns. Here we introduce an autonomous system that aligns single crystals without access to crystallography and diffraction theory. Using a model-free reinforcement learning framework, an agent learns to identify and navigate towards high-symmetry orientations directly from Laue diffraction patterns. Despite the absence of human supervision, the agent develops human-like strategies to achieve time-efficient alignment across different crystal symmetry classes. With this, we provide a computational framework for intelligent diffractometers. As such, our approach advances the development of automated experimental workflows in materials science. Workflow ; Automation ; Reinforcement learning ; Diffraction ; Interpretation (philosophy) ; Visualization Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Muhammad Umar Farooq | Fuyi Chen
Source: ACS Applied Nano Materials (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Integrating Artificial Intelligence via Deep Reinforcement Learning for the Autonomous Discovery of High-Entropy Nanoclusters The rational design and prediction of stable configurations for high-entropy alloy (HEA) nanoclusters present a formidable challenge due to the vast combinatorial space defined by size, shape, composition, and chemical ordering. While traditional global optimization algorithms are widely used, they face critical limitations in evaluator accuracy and computational efficiency for realistic systems. To overcome these constraints, we employed a deep reinforcement learning (DRL) framework designed for the rapid and accurate prediction of stable nanostructures. We demonstrate the efficacy of this approach on quaternary and quinary cluster systems. The agent explores the potential energy surface (PES) through stochastic configuration generation and atomic displacements. The local environment is encoded using atom-centered symmetry functions and integrates energy and force data for an accurate state evaluation, guided by a tailored reward function that balances exploration and exploitation. The agent is trained with the Trust Region Policy Optimization (TRPO) algorithm to ensure stable and efficient training, leading to rapid convergence, a strong bias toward low-energy states, and the effective sampling of diverse minima─outperforming conventional search methodologies. Subsequent first-principles validation confirms the stability of the identified nanoclusters, revealing negative formation energies. This thermodynamic stability is further analyzed by molecular dynamics simulations at elevated temperatures (500 and 700 K), which show no structural degradation. Sharp peaks in the radial distribution function (RDF) confirm the preserved crystallinity with minimal atomic displacement. The electronic structure analysis further reveals the emergence of pronounced pseudogaps near the Fermi level, a feature strongly associated with enhanced catalytic potential. Collectively, these results demonstrate that the DRL agent establishes a powerful and generalizable computational framework for the accelerated design of thermally robust functional materials for advanced applications. Reinforcement learning ; Stability (learning theory) ; Nanoclusters ; Function (biology) ; Cluster (spacecraft) ; Potential energy surface ; Molecular dynamics ; Feature (linguistics) ; Global optimization Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Mohammad Atif Faiz Afzal | Coscia,, Benjamin | Andrea Browning | Alexander Goldberg | Mathew Halls | Karl Leswing | Tsuguo Morisato | Hiroyuki Maeshima | Keisuke Hayashi | T. Ando | Nobuyuki Matsuzawa
Source: Open MIND (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Multi-Objective Design of Electrolyte Solvents Via Physics-Based Modeling and Reinforcement Learning BP_Model_Training_Data.csv: This file contains the molecular structures used to train the boiling point (BP) prediction models. The dataset includes canonical SMILES strings for each molecule, along with the experimentally measured boiling point values. In addition, the file includes the full set of molecular descriptors computed and used in the development of both regression and classification models. These descriptors were generated using the DeepAutoQSAR (DAQ) platform and encompass RDKit-based descriptors. REINVENT_Generated_Molecules.zip files (eight files): This file includes the complete set of molecules generated across all eight REINVENT experiments: BC+VC, BC+VR, BR+VC, BR+VR, BC+VC+HR, BC+VR+HR, BR+VC+HR, and BR+VR+HR. Each entry includes the generated SMILES string and model-predicted values for boiling point, viscosity, and HOMO energy. This dataset provides a comprehensive view of the chemical space explored through reinforcement learning. Top_200_Validated_Candidates.csv: This file contains the 200 top-ranking molecules selected from the REINVENT-generated set, based on multi-objective criteria. For each molecule, the file provides the SMILES structure along with calculated properties from physics-based simulations. These include boiling point and viscosity values obtained from molecular dynamics (MD) simulations and HOMO energy values computed using density functional theory (DFT). These validated candidates represent the most promising molecules for further experimental consideration. Boiling point ; Set (abstract data type) ; Point (geometry) ; Molecule ; Boiling ; Molecular dynamics ; Space (punctuation) ; Viscosity ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Mohammad Atif Faiz Afzal | Coscia,, Benjamin | Andrea Browning | Alexander Goldberg | Mathew Halls | Karl Leswing | Tsuguo Morisato | Hiroyuki Maeshima | Keisuke Hayashi | T. Ando | Nobuyuki Matsuzawa
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Multi-Objective Design of Electrolyte Solvents Via Physics-Based Modeling and Reinforcement Learning BP_Model_Training_Data.csv: This file contains the molecular structures used to train the boiling point (BP) prediction models. The dataset includes canonical SMILES strings for each molecule, along with the experimentally measured boiling point values. In addition, the file includes the full set of molecular descriptors computed and used in the development of both regression and classification models. These descriptors were generated using the DeepAutoQSAR (DAQ) platform and encompass RDKit-based descriptors. REINVENT_Generated_Molecules.zip files (eight files): This file includes the complete set of molecules generated across all eight REINVENT experiments: BC+VC, BC+VR, BR+VC, BR+VR, BC+VC+HR, BC+VR+HR, BR+VC+HR, and BR+VR+HR. Each entry includes the generated SMILES string and model-predicted values for boiling point, viscosity, and HOMO energy. This dataset provides a comprehensive view of the chemical space explored through reinforcement learning. Top_200_Validated_Candidates.csv: This file contains the 200 top-ranking molecules selected from the REINVENT-generated set, based on multi-objective criteria. For each molecule, the file provides the SMILES structure along with calculated properties from physics-based simulations. These include boiling point and viscosity values obtained from molecular dynamics (MD) simulations and HOMO energy values computed using density functional theory (DFT). These validated candidates represent the most promising molecules for further experimental consideration. Boiling point ; Set (abstract data type) ; Point (geometry) ; Molecule ; Boiling ; Molecular dynamics ; Space (punctuation) ; Viscosity ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Muhammad Bilal | Faisal Latif | Muhammad Hasnain | Muhammad Ali | Raziya Nadeem
Source: Preprints.org (preprint, 2026-04-14)
Field: Chemistry — Inorganic Chemistry

From Screening to Generative Design: Advances in ML-Assisted MOFs for Carbon Capture Carbon Capture and Storage (CCS) and Direct Air Capture (DAC) technologies must improve quickly due to the escalating climate problem, which is caused by annual CO₂ emissions reaching 37 billion metric tons. Because of their remarkable surface areas and customizable pore topologies, Metal–Organic Frameworks (MOFs) have become very intriguing sorbents; yet, exploring their large chemical design space is still computationally prohibitive. The growing significance of machine learning (ML) in advancing CO₂ capture research within MOFs is methodically examined in this review. We evaluate state-of-the-art models in four important areas using a structured evaluation protocol: process-level application, mechanistic interpretability, descriptor physical relevance, and predictive performance. Recent developments in Machine Learning Interatomic Potentials (MLPs) challenge traditional rigid-lattice assumptions by showing that framework flexibility greatly affects diffusivity and adsorption thermodynamics. While physics-informed descriptor engineering achieves R² values ranging from 0.81 to 0.97 depending on gas species and pressure regime, generative techniques, such as Deep Reinforcement Learning and transformer-based topologies, enable the inverse construction of high-affinity frameworks. Crucially, the field is moving away from isolated property prediction and toward multiscale, process-integrated optimization, where machine learning models combine material characteristics with industrial performance indicators like recovery and CO₂ purity in pressure swing adsorption systems. All of these advancements point to the need for physics-informed, comprehensible designs that can connect molecular-scale discovery with experimentally reliable, water-stable materials appropriate for commercial use. Flexibility (engineering) ; Generative grammar ; Metric (unit) ; Field (mathematics) ; Generative model ; Point (geometry) ; Uncertainty quantification ; Chemical space Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Partha Sarathi Dutta | Aditya Koneru | Adil Muhammed | Henry Chan | Karthik Balasubramanian | Sukriti Manna | Troy D. Loeffler | Kiran Sasikumar | Pierre Darancet | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry C (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Machine Learning an Ab-Initio Based Bond-Order Potential for Bismuthene Bismuthene is a heavy 2D material whose strong spin–orbit coupling and recently observed single-element ferroelectricity have intensified interest in its structural, vibrational, and transport properties. Accurate modeling of these behaviors requires a short-range interatomic potential that can reproduce the underlying bonding physics at a fraction of the computational cost of first-principles methods. However, such a potential is currently unavailable. In this work, we construct a Tersoff bond-order potential for β-bismuthene using a reinforcement-learning framework that integrates a continuous Monte Carlo Tree Search with a simplex-based local optimizer. The optimized parameter sets reproduce first-principles lattice constants, cohesive energy, the equation of state, elastic constants, and phonon dispersion. We validate the models by performing thermal-conductivity calculations and uniaxial fracture simulations─ our findings confirm the reliability of the resulting models across multiple thermomechanical regimes. Comparison of the three best solutions reveals how differences in pairwise interactions, angular terms, and bond-order behavior govern phonon features and mechanical responses. We demonstrate an interpretable and computationally efficient potential for bismuthene and demonstrate a general reinforcement-learning strategy for developing bond-order models in emerging 2D materials. Monte Carlo method ; Pairwise comparison ; Interatomic potential ; Lattice (music) ; Reliability (semiconductor) ; Phonon ; Coupling (piping) ; Construct (python library) 2D Materials and Applications ; Topological Materials and Phenomena ; Thermal properties of materials
Authors: Thanchanok Archapitakkol | Santi Bardeeniz | Thanayuth Kaweetirawatt | Chanin Panjapornpon
Source: Chemical Engineering Journal Advances (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

A deep reinforcement learning framework for regime-specific decision in the operation of industrial ammonium sulfate crystallization Real-time prediction of crystal size in industrial ammonium sulfate (AS) crystallization remains a challenge due to opaque slurry conditions, delayed laboratory measurements, variability across nucleation and crystal growth, and a mismatched sampling interval between inputs and output. Therefore, this study develops a reinforcement-learning router for regime-specific decision-making to guide a multi-model prediction framework in estimating the ratio of large AS crystals. The operating domain is segmented into distinct regimes, and a specialized long short-term memory is trained for each segmented regime to learn the characteristics and local dynamics. A Deep Q-Network (DQN) router evaluates sequential inputs to make a decision and select the best submodel by balancing predictive accuracy and operational consistency. The model is verified on the industrial-scale crystallization system. Based on the results, the DQN-router agent demonstrates effective performance across the distinct operatiing regimes using ten actions. The proposed model achieves a coefficient of determination of 0.961, a root mean square error of 1.810, and a mean absolute percentage error of 1.392 % and outperforms a single-prediction model without decision-making and benchmarked selectors. Analysis of router decisions confirms that the learned policy adapts effectively with shifting operating conditions and resolves overlaps between regime boundaries. A constrained optimization analysis of the model predictions identifies an optimal 19-hour operating pattern, consisting of an 11-hour hold-up period and an 8-hour discharge period, which produces an average large-crystal ratio of 88.38 % ± 2.09 %. Router ; Crystallization ; Mean squared error ; Slurry ; Nucleation ; Control theory (sociology) ; Reinforcement learning ; Approximation error Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Freezing and Crystallization Processes
Authors: Umadevi Venkatappa | Savithri Bhat | Mukta Dixit | H. S. Aishwarya | Anjan Kumar | K. Devaki Devi | Sreedevi Esuralla
Source: Scientific Reports (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Few-shot learning for classification of SEM images from green-synthesized nanoparticles of Momordica cymbalaria Nanotechnology offers wide range of applications owing to the unique physicochemical and biological properties of nanoparticles. Synthesis of nanoparticles from plant extracts provides a sustainable and safe alternative to the conventional chemical methods. In the current research phyto-synthesis and characterization of silver nanoparticles (AgNPs) and calcium carbonate nanoparticles (CaCO3 NPs) using fruit and root extracts of Momordica (M.) cymbalaria, is reported. The characterization was carried out using Ultraviolet (UV)–Visible spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray (EDAX) spectroscopy. EDAX confirmed CaCO3 NPs to be rich in oxygen (42–45%) and calcium (30%), while AgNPs exhibited distinct silver peaks (15–20%) along with significant carbon and oxygen content. UV–Visible spectroscopy and FTIR spectra confirmed functional groups and surface plasmon resonance peaks characteristic of the synthesized nanoparticles. The SEM analysis revealed spherical CaCO₃ nanoparticles (200–600 nm), whereas AgNPs displayed irregular aggregates (3–25 μm). To address the limited dataset of SEM images, a few-shot learning (FSL) framework was adopted for nanoparticle classification across four classes (AgNO3-root, AgNO3-fruit, CaCO3-root, CaCO3-fruit). MobileNetV2 and ResNet50 were trained for classification of SEM images. MobileNetV2 with episodic training, Mahalanobis distance, and KMeans clustering (Configuration 3) achieved 95% accuracy with a compact 8.98 MB as model size, outperforming baseline transfer learning approaches. The findings highlight the potential of automated classification of nanoparticles’ SEM images using FSL under low-data regimes. Silver nanoparticle ; Nanoparticle ; Scanning electron microscope ; Fourier transform infrared spectroscopy ; Characterization (materials science) ; Surface plasmon resonance Nanoparticles: synthesis and applications ; Biosensors and Analytical Detection ; Spectroscopy and Chemometric Analyses
Authors: Ivan Kulagin | Ming Li
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Towards high precision control of strain wave gearing magnetic shape memory actuators utilizing reinforcement learning Actuator ; Control theory (sociology) ; Position (finance) ; Reinforcement learning ; PID controller ; Cascade ; Controller (irrigation) ; Range (aeronautics) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Chien-Chih Wang | Chun-Kai Wei
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

A two-stage transfer learning framework for quality prediction under data scarcity: an industrial case study in copper foil manufacturing Scrap ; Quality (philosophy) ; Key (lock) ; Transfer of learning ; Scarcity ; Process (computing) ; Predictive maintenance ; Domain (mathematical analysis) Machine Learning in Materials Science ; Digital Transformation in Industry ; Machine Fault Diagnosis Techniques
Authors: Aditya Koneru | Henry Chan | Valeria Paula Molinero | Subramanian K. R. S. Sankaranarayanan
Source: Journal of Chemical Theory and Computation (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our previous RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potential that provides an analytic embedding of local coordination through a three-body term. A hierarchical RL workflow combining continuous-action Monte Carlo Tree Search and property-based rewards efficiently explores the 26-dimensional parameter space, sequentially optimizing lattice parameters, densities, angles, and cohesive energies of 21 silica polymorphs. The resulting models, Q-Tersoff and ML-Tersoff, reproduce the energetic ordering of low-energy phases and capture the angular correlations and amorphous structure factors of silica with improved fidelity over pairwise force fields, while remaining orders of magnitude faster than high-dimensional machine-learned potentials. Both models underperform for elastic constants and high-energy frameworks, delineating the limits of the current analytic form. The approach establishes a general and interpretable route to angle-aware, short-range potentials that bridge physics-based and machine-learned descriptions of silicate materials. Pairwise comparison ; Embedding ; Reinforcement learning ; Workflow ; Monte Carlo method ; Fidelity ; Lattice (music) ; Amorphous silica Machine Learning in Materials Science ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Daniele Pessina | Tony Tian | Oliver Watson | Jerry Y. Heng | Maria M. Papathanasiou
Source: Digital Chemical Engineering (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Transfer learning of data-driven crystallisation processes via constrained Neural Ordinary Differential Equations Modelling complex crystallisation processes remains challenging due to limited experimental datasets, high measurement noise, and the need for generalisability across varying operating conditions. Neural Ordinary Differential Equations (NODEs) and transfer learning (TL) offer promising tools to overcome these limitations by providing data-efficient, flexible, and transferable modelling frameworks. This work investigates the use of NODEs to model protein crystallisation dynamics under data-scarce conditions. A NODE trained on a data-rich source system successfully captures solute consumption and particle size dynamics, but when applied to data-sparse target systems, scratch-trained NODEs exhibit limited generalisation and unphysical behaviours. To address this, several TL strategies are evaluated, including layer freezing, parameter deviation penalisation, and system-embedding within the neural architecture. Results show that layer freezing and deviation penalty consistently improve knowledge transfer, while system-embedding offers robustness in noisy or undersampled datasets. In addition, physics-informed NODEs, constrained to enforce monotonic concentration decay and crystal growth, demonstrate greater stability under high noise and sparse measurement regimes, ensuring physically consistent predictions. Overall, the combination of constrained NODEs with appropriate TL strategies provides a robust framework for accurate, transferable modelling of crystallisation systems in low-data regimes. • Transfer learning enables Neural ODEs to model crystallisation with limited data. • Layer freezing and parameter penalties reliably transfer knowledge across systems. • System embeddings improve predictions when measurements are sparse or noisy. • Physics constraints ensure physically consistent predictions under high noise. • Framework demonstrated on template-induced protein crystallisation systems. Ordinary differential equation ; Robustness (evolution) ; Artificial neural network ; Ode ; Stability (learning theory) ; Noise (video) ; Work (physics) ; Monotonic function Enzyme Structure and Function ; Crystallization and Solubility Studies ; Protein Structure and Dynamics