jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Maevskiy | Alexandra Carvalho | Emil Sataev | Volha Turchyna | Keian Noori | Aleksandr Rodin | Antonio Castro Neto | A. Ustyuzhanin
Source: Physical Review Research (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Predicting ionic conductivity in solids from the machine-learned potential energy landscape Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and it is at least 3000 times faster compared to first-principles molecular dynamics. Conductivity ; Energy (signal processing) ; Energy landscape ; Ionic conductivity ; Materials science ; Computer science ; Environmental science ; Physics ; Chemistry ; Mathematics ; Statistics ; Thermodynamics ; Physical chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Electronic and Structural Properties of Oxides
Authors: Raheel Hammad | Sownyak Mondal
Source: Journal of Cheminformatics (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach The Gibbs free energy of an inorganic material represents its maximum reversible work potential under constant temperature and pressure. Its calculation is crucial for understanding material stability, phase transitions, and chemical reactions, thus guiding optimization for diverse applications like catalysis and energy storage. In this study, we have developed a Physics-Informed Neural Network model that leverages the Gibbs free energy equation. The overall loss function is adjusted to allow the model to simultaneously predict all three thermodynamic quantities, including Gibbs free energy, total energy, and entropy, thus transforming it into a multi-output model. In recent literature, there is a growing emphasis on evaluating machine learning models under challenging conditions, such as small datasets and out-of-distribution predictions. Reflecting this trend, we have rigorously benchmarked our model across these scenarios, demonstrating its robustness and adaptability. It turns out that our model demonstrates a 43% improvement for normal scenario and even more in out-of-distribution regime compared to the next-best model. Scientific Contribution This study introduces the application of a Physics-Informed Neural Network to simultaneously compute multiple thermodynamic properties, including Gibbs free energy, total energy, and entropy. By integrating the Gibbs free energy equation into the loss function, the model achieves superior accuracy in low data regimes and enhances robustness in the out-of-distribution scenarios. Gibbs free energy ; Laws of thermodynamics ; Robustness (evolution) ; Artificial neural network ; Computer science ; Thermodynamic free energy ; Entropy (arrow of time) ; Statistical physics ; Thermodynamics ; Artificial intelligence ; Physics ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Nuclear Materials and Properties
Authors: Jordan J. Winetrout | Zilu Li | Qi Zhao | Landon Gaber | Vinu Unnikrishnan | Vikas Varshney | Yanxun Xu | Yusu Wang | Hendrik Heinz
Source: Proceedings of the National Academy of Sciences (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress-strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts. Nanostructure ; Materials science ; Nanotechnology ; Carbon fibers ; Artificial intelligence ; Computer science ; Composite material Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications
Authors: Dagmar D’hooge
Source: Current Opinion in Chemical Engineering (article, 2025-05-19)
Field: Chemistry — Organic Chemistry

Radical-chemistry-driven polymer synthesis, modification, and recycling: trends in modeling to upgrade our knowledge and process design Upgrade ; Process (computing) ; Engineering ; Chemistry ; Computer science Advanced Polymer Synthesis and Characterization ; Chemistry and Chemical Engineering ; Microplastics and Plastic Pollution
Authors: Guangzhi Nie | Jing Xue | Yuyu Gao | Zhaoqing Wang | Hang Wang | Jiangbin Wu | Yun‐Ze Long | Wen‐Peng Han
Source: Microchemical Journal (article, 2025-02-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrospun nylon/Ag@ZIF-8 nanofibers for robust SERS detection of gaseous molecules assisted by deep learning Nanofiber ; Materials science ; Molecule ; Nanotechnology ; Nylon 6 ; Chemical engineering ; Electrospinning ; Polymer ; Chemistry ; Composite material ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Wei Ge | Ramindu De Silva | Yanan Fan | Scott A. Sisson | Martina H. Stenzel
Source: Macromolecular Rapid Communications (article, 2025-07-31)
Field: Chemistry — Spectroscopy

Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach This study aimed to identify solvent characteristics that enhance drug loading in polymeric micelles. Polyethylene glycol-block-polystyrene (PEG-b-PS) and curcumin were used as model compounds to investigate the impact of 40 different solvent mixtures on drug loading during flow-based assembly. We tested five algorithms: Random Forest (RF), Gradient Boosting (GP), XGBoost, Support Vector Regression (SVR), and Multilayer Perceptron (MLP), with the MLP model proving to be the most effective among them. To explain the model's predictions, we utilized SHapley Additive exPlanations (SHAP) values to identify solvent properties that contribute to high drug loading. Of the nine descriptors examined-curcumin solubility, polarity, Hildebrand solubility parameters, dipole moment, dielectric constants, viscosity, and Hansen solubility parameters (δD, δP, and δH)-solubility emerged as the most critical factor. Therefore, to achieve optimal drug loading, researchers should prioritize solvents with the highest solubility. Solubility ; Hildebrand solubility parameter ; Solvent ; Polyethylene glycol ; Nanocarriers ; Materials science ; Chemistry ; Organic chemistry ; Drug delivery Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Najamus Sahar Riyaz | Mazen Khaled | Ali Alshami | Ibnelwaleed A. Hussein
Source: Arabian Journal for Science and Engineering (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks Abstract Machine learning (ML) frameworks are transforming the development of corrosion inhibitors by enabling quantitative prediction of inhibition efficiency before synthesis. This work identifies the most reliable machine learning (ML) strategies for forecasting corrosion inhibitor efficiency before synthesis, thereby shortening development cycles and reducing experimental cost. Drawing on more than fifteen harmonized datasets that span pyrimidines, ionic liquids, graphene oxides, and additional compound families, we benchmark traditional algorithms, such as artificial neural networks, support vector machines, k-nearest neighbors, random forests, against advanced graph-based and deep architectures including three-level directed message-passing neural networks, 2D3DMol-CIC, and graph convolutional networks. Cohesive data collections exceeding four hundred molecules under standardized test conditions deliver coefficients of determination above 0.90 and root-mean-square errors below 0.05. In contrast, fragmented datasets suffer from overfitting with R 2 often under 0.70. Graph neural networks lower prediction error by up to thirty percent relative to descriptor-driven QSAR models for structurally diverse inhibitors. However, their accuracy diminishes for large, flexible molecules unless explicit three-dimensional information is provided. Ensemble schemes such as Gaussian process regression with simple averaging and gradient boosting regressors fortified by permutation feature importance improve robustness in noisy or multi-alloy environments. At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R 2 = 0.05 to 0.99 in a representative thiophene set. Persistent obstacles include limited public databases, inconsistent experimental protocols, and the opaque nature of deep learners. Researchers, engineers, and material scientists will gain valuable insights into optimizing ML-driven corrosion predictions, guiding future experimental studies. Corrosion ; Machine learning ; Computer science ; Artificial intelligence ; Engineering ; Algorithm ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: David F. Nippa | Alex T. Müller | Kenneth Atz | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: Molecular Informatics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Simple User‐Friendly Reaction Format Utilizing the growing wealth of chemical reaction data can boost synthesis planning and increase success rates. Yet, the effectiveness of machine learning tools for retrosynthesis planning and forward reaction prediction relies on accessible, well-curated data presented in a structured format. Although some public and licensed reaction databases exist, they often lack essential information about reaction conditions. To address this issue and promote the principles of findable, accessible, interoperable, and reusable (FAIR) data reporting and sharing, we introduce the Simple User-Friendly Reaction Format (SURF). SURF standardizes the documentation of reaction data through a structured tabular format, requiring only a basic understanding of spreadsheets. This format enables chemists to record the synthesis of molecules in a format that is understandable by both humans and machines, which facilitates seamless sharing and integration directly into machine learning pipelines. SURF files are designed to be interoperable, easily imported into relational databases, and convertible into other formats. This complements existing initiatives like the Open Reaction Database (ORD) and Unified Data Model (UDM). At Roche, SURF plays a crucial role in democratizing FAIR reaction data sharing and expediting the chemical synthesis process. Computer science ; Interoperability ; Documentation ; Simple (philosophy) ; Data sharing ; Cheminformatics ; Expediting ; Database ; Open data ; World Wide Web ; Data science ; Chemistry ; Engineering ; Systems engineering ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Abdul Hai | Muhamad Fazly Abdul Patah | G. Bharath | Hamad AlMohamadi | Mansoor Ul Hassan Shah | Muhammad Daud | Fawzi Banat | Md. Shahinoor Islam | Wan Mohd Ashri Wan Daud
Source: International Journal of Energy Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Single‐Step Synthesis of Porous Activated Carbon by CO <sub>2</sub> Activation: Process Optimization With Response Surface Methodology and Machine Learning Techniques This study presents an innovative, single‐step synthesis approach for producing porous activated carbon (AC) from palm kernel shells (PKS). A high‐quality AC was produced by integrating CO 2 and ZnCl 2 into the carbonization process as activating agents. The pyrolysis parameters, including activation temperature, residence time, activating agent ratio, and heating rate, were optimized using response surface methodology (RSM) to assess the yields and specific surface area (SSA) of the resultant AC. The experimental design was based on a central composite design (CCD), and the process variables were analyzed to understand their interactions and combined effect on the properties of the AC. Further, the extreme gradient boosting (XGB) and random forest (RF) machine learning regressor models predicted the pyrolysis performance and aligned well with the experimental data. The results revealed that optimal conditions were achieved at an activation temperature of 800°C, a heating rate of 10°C/min, and a residence time of 2.75 h with the activating agent to biomass ratio of 1.25, yielding 33% AC with a surface area of 1420 m 2 /g and significant pore development. This study provides a scalable and environmentally friendly approach to valorizing agricultural waste into value‐added products by supporting the goals of the circular economy and net‐zero initiatives. Activated carbon ; Response surface methodology ; Porosity ; Process engineering ; Process (computing) ; Chemical engineering ; Process optimization ; Materials science ; Computer science ; Chemistry ; Engineering ; Adsorption ; Chromatography ; Organic chemistry ; Composite material Catalytic Processes in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Polymer Foaming and Composites
Authors: Yoshifumi Amamoto | Chie Koganemaru | Ken Kojio | Atsushi Takahara | Sayoko Yamamoto | Kazuki Okazawa | Yuta Tsuji | Toshimitsu Aritake | Kei Terayama
Source: npj Computational Materials (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

A machine learning approach to designing and understanding tough, degradable polyamides The development of environmentally friendly plastics has received renewed attention for a sustainable society. Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers, the design of biodegradable polymers to overcome these issues is often difficult. In this study, we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 and α-amino acid segments that are mechanically tough and degradable. Multi-objective optimization based on Gaussian process regression for the degradation rate, strain at break, and Young’s modulus (the last two parameters correspond to toughness) suggested appropriate α-amino acid sequences for polyamides endowed with both properties. Ridge regression revealed that the physical factors associated with the sequences, as well as the higher-order multiblock-derived structures (such as the crystal lattice structure, melting points, and hydrogen bonding), were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data. Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques. Polyamide ; Computer science ; Biochemical engineering ; Polymer science ; Artificial intelligence ; Materials science ; Engineering ; Composite material Polymer crystallization and properties ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties
Authors: Tim Kircher | Martin Votsmeier
Source: The Journal of Physical Chemistry Letters (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine Learning Surrogate Models for Mechanistic Kinetics: Embedding Atom Balance <i>and</i> Positivity Multiscale simulations of reactive flows are critical in many fields. However, their application is often hindered by the high computational cost of solving detailed chemical kinetics. Recent advances in surrogate models for reactive chemistry offer promising speedups, but ensuring physical consistency remains challenging. In particular, machine learning models for chemical kinetics must enforce atom balance and guarantee the positivity of predicted concentrations. Here, we introduce a positivity preserving projection and a correction by linear interpolation backtracking which simultaneously guarantee both constraints. We demonstrate this using two practical examples from atmospheric chemistry and heterogeneous catalysis, as well as for a large number of random, synthetically generated reaction systems. In all cases, our approach yields exclusively positive model predictions conforming to the atom balance, without reducing the overall accuracy of the model. Kinetics ; Balance (ability) ; Embedding ; Atom (system on chip) ; Computer science ; Statistical physics ; Chemistry ; Artificial intelligence ; Physics ; Psychology ; Classical mechanics ; Neuroscience ; Parallel computing Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Yichuan Tang | Shaopeng Liu | Silong Li | Ruonan Ma | Yue Li | Kun Wang | Minxia Fang | Chao Zhou | Sen Yang | Yin Zhang
Source: MetalMat (review, 2025-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Review of Artificial Intelligence‐Driven Innovations in Soft Magnetic Materials Optimization: Current Trends and Future Horizons ABSTRACT With the continuous accumulation of data, machine learning is playing an increasingly important role in materials science, especially demonstrating significant advantages in predicting material compositions and developing new alloy systems for soft magnetic materials. However, currently, it mainly focuses on composition optimization while often neglecting the impact of structure and fundamental physical parameters. On this matter, we have discussed model selection in machine learning modeling, the issues encountered, and the limitations of the composition‐focused approach. Through insights gained from research in other material fields, it is highlighted that integrating machine learning with other computational methods such as first‐principles calculations and phase diagram computations can significantly enhance the predictive capabilities of machine learning. We analyzed these AI‐enhanced cases and highlighted how they have the potential to lead to further breakthroughs in soft magnetic materials. New horizons ; Current (fluid) ; Computer science ; Engineering ; Aerospace engineering ; Electrical engineering Magnetic Properties of Alloys ; Metallic Glasses and Amorphous Alloys ; Shape Memory Alloy Transformations
Authors: Zhiwen Bai | Ming-Cong Lan | Wenshan Yu | Shengping Shen
Source: International Journal of Mechanical Sciences (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Predicting strain effects on adsorption energy based on atomistic structure and density of states Materials science ; Strain (injury) ; Adsorption ; Strain energy density function ; Energy (signal processing) ; Thermodynamics ; Finite element method ; Physics ; Physical chemistry ; Chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Kohei Noda | Araki Wakiuchi | Yoshihiro Hayashi | Ryo Yoshida
Source: Communications Materials (article, 2025-02-22)
Field: Materials Science — Materials Chemistry

Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training Abstract Recent advancements in machine learning have demonstrated its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapidly computable property predictors, which allow identifying novel materials with the desired properties from vast material spaces. However, the limited availability of data resources poses a significant challenge in data-driven material research, particularly hindering the exploration of innovative materials beyond the boundaries of existing data. Although machine-learning predictors are inherently interpolative, establishing a general methodology to create an extrapolative predictor remains a fundamental challenge. In this study, we leveraged the attention-based architecture of neural networks and a meta-learning algorithm to enhance extrapolative generalization capabilities. Meta-learners trained repeatedly on arbitrarily generated extrapolative tasks show outstanding generalization for unexplored material spaces. Through the tasks of predicting the physical properties of polymeric materials and hybrid organic–inorganic perovskites, we highlight the potential of such extrapolatively trained models, particularly their ability to rapidly adapt to unseen material domains in transfer-learning scenarios. Training (meteorology) ; Artificial intelligence ; Computer science ; Cognitive psychology ; Psychology ; Geography ; Meteorology Machine Learning in Materials Science