jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nawaf Alampara | Mara Schilling-Wilhelmi | Kevin Maik Jablonka
Source: Computational Materials Science (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Lessons from the trenches on evaluating machine learning systems in materials science Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning systems increasingly transform scientific fields, the question of how to effectively evaluate these systems becomes crucial for ensuring reliable progress. In this review, we examine the current state and future directions of evaluation frameworks for machine learning in science. We organize the review around a broadly applicable framework for evaluating machine learning systems through the lens of statistical measurement theory, using materials science as our primary context for examples and case studies. We identify key challenges common across machine learning evaluation such as construct validity, data quality issues, metric design limitations, and benchmark maintenance problems that can lead to phantom progress when evaluation frameworks fail to capture real-world performance needs. By examining both traditional benchmarks and emerging evaluation approaches, we demonstrate how evaluation choices fundamentally shape not only our measurements but also research priorities and scientific progress. These findings reveal the critical need for transparency in evaluation design and reporting, leading us to propose evaluation cards as a structured approach to documenting measurement choices and limitations. Our work highlights the importance of developing a more diverse toolbox of evaluation techniques for machine learning in materials science, while offering insights that can inform evaluation practices in other scientific domains where similar challenges exist. • Machine learning evaluation in materials science faces critical challenges in construct validity and metric design. • “Phantom progress” occurs when benchmark improvements do not translate to real-world applications. • Evaluation frameworks exist on a spectrum from representational to pragmatic measurements. • Structured “evaluation cards” can increase transparency about measurement design choices. Nanotechnology ; Computer science ; Engineering physics ; Materials science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Thomas Hehre | Philip E. Klunzinger | Bernard Deppmeier | William Sean Ohlinger | Warren J. Hehre
Source: The Journal of Organic Chemistry (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Practical Machine Learning Strategies 4: Using Neural Networks to Replicate Proton and <sup>13</sup>C NMR Chemical Shifts Obtained from ωB97X-D/6-31G* Density Functional Calculations Described are neural networks that accurately reproduce proton and <sup>13</sup>C chemical shifts obtained from ωB97X-D/6-31G*//ωB97X-D/6-31G* density functional model GIAO calculations. They support uncharged, closed-shell molecules comprising H, C, N, O, F, S, Cl, and Br. Development involved training to ≈2.7 million equilibrium geometry and chemical shift calculations for a diverse collection of organic molecules (including synthetic drugs and natural products). Referenced to ωB97X-D/6-31G*//ωB97X-D/6-31G* calculations, chemical shifts from neural networks for 601 marine natural products show RMS errors of 0.05 ppm (proton) and 0.76 ppm (<sup>13</sup>C). RMS errors of 0.09 ppm (proton) and 1.02 ppm (<sup>13</sup>C) shifts result when equilibrium geometries from a previously described "estimated ωB97X-D/6-31G*" neural network model (trained to reproduce ωB97X-D/6-31G* geometries) are utilized. A second assessment of experimental <sup>13</sup>C chemical shifts for 246 natural products is provided. Using neural network models to provide both geometries and chemical shifts: 45% of <sup>13</sup>C shifts reproduce experimental values within 1 ppm, 73% within 2 ppm, and 86% within 3 ppm. Utilizing neural network models for both equilibrium geometries and chemical shifts reduces the computational time required for accurate proton and <sup>13</sup>C chemical shifts from tens to hundreds of minutes to just a few seconds per molecule. Proton ; Replicate ; Artificial neural network ; Density functional theory ; Computer science ; Chemistry ; Physics ; Machine learning ; Computational chemistry ; Nuclear physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Fouzia Mashkoor | Mohd Shoeb | Sayed Mohammed Adnan | Javed Alam Khan | Mohammed Ashraf Gondal | Changyoon Jeong
Source: Surfaces and Interfaces (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular-level insight into CNT-decorated La2Mo4O15 nanocomposites for antimony adsorption from aqueous media: Machine learning simulation, interfacial behavior, and recyclability in supercapacitor electrolytic environments Supercapacitor ; Nanocomposite ; Adsorption ; Aqueous solution ; Capacitance ; Electrolyte Supercapacitor Materials and Fabrication ; Extraction and Separation Processes ; Adsorption and biosorption for pollutant removal
Authors: Hongwei Du | Xiang Huang | Jian Hui | Lanting Zhang | Yuanxun Zhou | Hong Wang
Source: ACS Materials Letters (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Assessment and Application of Universal Machine Learning Interatomic Potentials in Solid-State Electrolyte Research High-performance solid-state electrolytes (SSEs) are crucial for next-generation lithium batteries. However, conventional methods like density functional theory and empirical force fields face challenges in computational cost, scalability, and transferability across diverse systems. Machine learning interatomic potentials (MLIPs) offer a promising alternative by balancing accuracy and efficiency. Nevertheless, their performance and applicability for SSEs remain poorly defined, limiting reliable model selection. In this study, we benchmark 12 MLIPs─including GRACE, DPA, MatterSim, MACE, SevenNet, CHGNet, TensorNet, M3GNet, and ORB─across energies, forces, phonons, electrochemical stability, thermodynamic properties, elastic moduli, and Li+ diffusivity. GRACE-2L-OAM, MACE-MPA, MatterSim, DPA-3.1-3M, and SevenNet-MF-ompa show superior accuracy. Using MatterSim, we study Li3YCl6 and Li6PS5Cl, revealing that ∼40–50% S/Cl anion disorder enhances Li+ migration connectivity in Li6PS5Cl, while higher Li+ content in Li3Ycl6 expands conduction channels and reduces energy barriers. These insights highlight the power of MLIP-driven simulations for mechanistic understanding and rational design of high-conductivity SSEs. Transferability ; Limiting ; Benchmark (surveying) ; Density functional theory ; Face (sociological concept) ; Lithium (medication) ; Electrolyte ; Power (physics) ; Interatomic potential Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Solid-state spectroscopy and crystallography
Authors: Emilio Nuñez-Andrade | Isaac Vidal | James W. Ryan | Rafael Gómez‐Bombarelli | Francisco J. Martín‐Martínez
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Embedded machine-readable molecular representation for resource-efficient deep learning applications The implementation of an embedded encoding of molecular representations reduces memory and disk usage while maintaining performance in deep learning models. Computer science ; Representation (politics) ; Artificial intelligence ; Resource (disambiguation) ; Deep learning ; Machine learning ; Natural language processing ; Computer network ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Natalia Osiecka | Marcin Piwowarczyk | Mirosław Gałązka
Source: Journal of Molecular Liquids (article, 2025-04-05)
Field: Chemistry — Analytical Chemistry

Key factors in neural network performance for liquid crystal texture classification Texture (cosmology) ; Key (lock) ; Artificial neural network ; Computer science ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Image (mathematics) ; Computer security Spectroscopy and Chemometric Analyses ; Optical Polarization and Ellipsometry
Authors: Hiroyuki Isobe | Xinyi Xiao | T. Fukunaga | Koki Ikemoto
Source: Angewandte Chemie International Edition (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Revealing Kinetic Features of a Macrocyclization Reaction Using Machine‐Learning‐Augmented Data We developed a method to reveal the kinetic features of macrocyclization using machine-learning-augmented data. Thirty-six experimental yield datasets were expanded to two hundred datasets through machine learning, enabling kinetic analyses of concentration-dependent yields. This augmented data allowed the chemistry underlying the predictive black box to be elucidated, providing quantitative insights such as rate constants and effective molarity through least-squares fitting of differential rate equations. These quantitative measures offered physicochemical explanations for the optimal conditions identified by the machine learning. Additionally, linear relationships between effective molarity and strain energy were found, highlighting key aspects of oligomeric macrocyclization via nickel-mediated coupling. Kinetic energy ; Molar concentration ; Yield (engineering) ; Computer science ; Reaction rate constant ; Support vector machine ; Coupling (piping) ; Machine learning ; Biological system ; Chemistry ; Artificial intelligence ; Materials science ; Kinetics ; Physics ; Thermodynamics ; Organic chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Adnan Roshid Shawon | Rahul Ghosh | Md. Ashraful Islam
Source: Scientific Reports (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Analysis of thermal conductivity of aluminum alloys by compositions and tempering process using machine learning Thermal conductivity (TC) of commercially available Aluminum alloys is often hard to predict by machine learning (ML) algorithms due to the lack of a large dataset. The costly simulations and time-consuming experiments slow down the advancement to explore the thermal conductivity of aluminum alloy as it depends on the continuous iteration of compositional fractions and the manufacturing process. To accelerate this process, a small dataset of 271 Al alloys was organized from widely used alloys available on the market from series 1XXX to 8XXX. The dataset contains 14 alloying elements, mechanical properties, and tempering methods in manufacturing, which were preprocessed with label encoding. After performing correlation analysis among the variables, the dataset was found to be unique and reliable to train several supervised ML models. During training, 5-fold cross validation was performed to get the best set of hyperparameters employing Bayesian optimization to ensure the best performance for each model. Among them, eXtreme Gradient Boosting (XGB) predicted the TC with the highest R² value of 0.91. For this best model, feature importance analysis was performed to compare the actual metallurgical and statistical importance that affects TC of Al alloys. To improve the model accuracy further, we dropped the mechanical variables from the input variables, which depend on lab results. This additionally boosted the performance of the XGB model to an R² of 0.95. Our study demonstrates that the thermal conductivity of Al alloys can be forecasted quickly and accurately using limited datasets in ML algorithms rather than costly iterative experiments. Tempering ; Hyperparameter ; Thermal conductivity ; Gradient boosting ; Bayesian optimization ; Aluminium ; Boosting (machine learning) ; Alloy Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Iron and Steelmaking Processes
Authors: Yang Mo | Dezhi Zhou
Source: Energy and AI (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Ignition delay prediction for fuels with diverse molecular structures using transfer learning-based neural networks In this study, a transfer learning-based neural network approach to predict ignition delays for a variety of fuels is proposed to meet the demand for accurate combustion analysis. A comprehensive dataset of ignition delays was generated using a random sampling technique across different temperatures and pressures, focusing on hydrocarbon fuels with 1–4 carbon atoms. Two machine learning models, an artificial neural network and a graph convolutional network, are trained on this dataset, and their prediction performance was evaluated. A transfer learning framework was subsequently developed, enabling the models trained on smaller molecules (1–3 carbon atoms) to predict ignition delays for larger molecules (4 carbon atoms) with minimal additional data. The proposed framework demonstrated reliable and high prediction accuracy, achieving a high level of reliability for fuels with limited experimental measurements. This approach offers significant potential to streamline the prediction of ignition delays for novel fuels, reducing the dependence on resource-intensive experiments and complex simulations while contributing to the advancement of clean and efficient energy technologies. • A transfer learning-based framework for ignition delay prediction across diverse fuel molecular structures. • Artificial neural networks and graph convolutional networks applied for accurate prediction of ignition delay. • Comprehensive datasets generated via simulation, covering hydrocarbons with 1 to 4 carbon atoms. Artificial neural network ; Ignition system ; Transfer of learning ; Computer science ; Artificial intelligence ; Environmental science ; Engineering ; Aerospace engineering Chemical Thermodynamics and Molecular Structure ; Advanced Combustion Engine Technologies ; Mass Spectrometry Techniques and Applications
Authors: Sevda Saltık | Ozan KIYIKCI | Türkan Akman | Erdinç Öz | Esra Kavaz
Source: Materials (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for potential use in nuclear applications. Four alloy compositions with varying Fe contents (7.21, 6.35, 5.47, and 4.58 wt%) were analyzed using a combination of Monte Carlo simulations, machine learning (ML) predictions based on multilayer perceptrons (MLPs), EpiXS, and SRIM-based charged particle transport modeling. Key photon interaction parameters-including mass attenuation coefficient (MAC), half-value layer (HVL), buildup factors, and effective atomic number (Z<sub>eff</sub>)-were calculated across a wide energy range (0.015-15 MeV). Results showed that the 7.21Fe alloy exhibited a maximum MAC of 12 cm<sup>2</sup>/g at low energies and an HVL of 0.19 cm at 0.02 MeV, indicating improved gamma attenuation with increasing Fe content. The ML model accurately predicted MAC values in agreement with Monte Carlo and XCOM data, validating the applicability of AI-assisted modeling in material evaluation. SRIM calculations demonstrated enhanced charged particle shielding: the projected range of 10 MeV protons decreased from ~55 µm (low Fe) to ~50 µm (high Fe), while alpha particle penetration reduced accordingly. In terms of fast neutron attenuation, the 7.21Fe alloy reached a maximum removal cross-section (Σ<sub>R</sub>) of 0.08164 cm<sup>-1</sup>, showing performance comparable to conventional materials like concrete. Overall, the results confirm that Fe-rich Al-based alloys offer a desirable balance of lightweight design, structural stability, and dual-function radiation shielding, making them strong candidates for next-generation protective systems in high-radiation environments. Electromagnetic shielding ; Monte Carlo method ; Radiation shielding ; Materials science ; Nuclear engineering ; Nuclear radiation ; Radiation ; Radiochemistry ; Composite material ; Nuclear physics ; Physics ; Engineering ; Chemistry ; Mathematics ; Statistics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear Materials and Properties
Authors: Bo Xiao | Nafees Ahmad | Asif Mahmood | Mohamed H. Helal
Source: Advanced Theory and Simulations (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Data Mining and Machine Learning Analysis to Find Polymers for Electronic and Photovoltaics Applications: A Goal to Achieve Higher Dielectric Constant Abstract The discovery of polymers with high dielectric constants is of significant interest for advanced electronic applications, such as capacitors, flexible electronics, and energy storage devices. In this study, data mining and machine learning (ML) techniques are applied to identify polymers with superior dielectric constant. Molecular descriptors are calculated. These descriptors are used to train several machine learning models, including linear regression, gradient booting regression, histgradient boosting regression, bagging regression, decision tree regression, and random forest regression. By employing cross‐validation and hyperparameter tuning, best model is optimized for robust predictive performance. A database of 10k polymers is generated and their dielectric constant is predicted best ML model. Thirty polymers with higher dielectric constant values are selected. This work demonstrates the power of data‐driven approaches in accelerating the discovery of high‐performance polymers for electronic applications. Dielectric ; Photovoltaics ; Constant (computer programming) ; Polymer ; Computer science ; Materials science ; Electrical engineering ; Optoelectronics ; Engineering ; Photovoltaic system ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Haiying Yang | Lin Li | Ping Yang
Source: Applied Physics Letters (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Deep learning-based molecular dynamics simulation reconfiguration of efficient heat energy transport of Gra/h-BN heterointerface We investigate the reconfiguration scheme of efficient heat energy transport of the Gra/h-BN heterointerface by a hybrid enhanced machine learning method combining an integrated iterative method, automatic modeling, non-equilibrium molecular dynamics calculation, and convolutional neural network (CNN). The results show that the method identifies the optimal defect distribution in gra/h-BN from tens of millions of possible defect configurations, and the interfacial thermal conductivity (ITC) of the vdW gra/h-BN heterointerface in the optimal defect distribution is 97% higher than that in the original gra. Furthermore, the heat transfer transformation of the vdW gra/h-BN heterointerface with different defect distributions at room temperature can be observed efficiently. The dependency law between the defect distribution and the ITC is revealed by combining the CNN model of the ResNet network. The efficient heat energy transport design can promote the sustainable service life of materials and structures. Control reconfiguration ; Molecular dynamics ; Materials science ; Energy (signal processing) ; Energy transport ; Physics ; Chemical physics ; Chemistry ; Computer science ; Engineering physics ; Computational chemistry ; Embedded system ; Quantum mechanics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Youcai Zhu | Yue Mu | Xiaoke Shi | Yang Shu | Li Sun | Zhen Liu
Source: Journal of Catalysis (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization Catalysis ; Selectivity ; Steric effects ; Ethylene ; Chromium ; Workflow ; Electronic effect ; Molecular descriptor Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Inorganic Chemistry and Materials
Authors: P. Jeyalakshmi | M. Naveen | P. Ramkumar
Source: Journal of Bio- and Tribo-Corrosion (article, 2025-05-29)
Field: Materials Science — Metals and Alloys

Machine Learning Assisted Corrosion Behaviour Prediction of Dual-Engineered Ti6Al4V Alloy Dual (grammatical number) ; Titanium alloy ; Materials science ; Corrosion ; Alloy ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Xiu Li Fu | Boya Li | Binguo Fu | Tianshun Dong | Jingkun Li
Source: Materials (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Composition Design of a Novel High-Temperature Titanium Alloy Based on Data Augmentation Machine Learning The application fields of high-temperature titanium alloys are mainly concentrated in the aerospace, defense and military industries, such as the high-temperature parts of rocket and aircraft engines, missile cases, tail rudders, etc., which can greatly reduce the weight of aircraft while resisting high temperatures. However, traditional high-temperature titanium alloys containing multiple types of elements (more than six) have a complex impact on the solidification, deformation, and phase transformation processes of the alloys, which greatly increases the difficulty of casting and deformation manufacturing of aerospace and military components. Therefore, developing low-component high-temperature titanium alloys suitable for hot processing and forming is urgent. This study used data augmentation (Gaussian noise) to expedite the development of a novel quinary high-temperature titanium alloy. Utilizing data augmentation, the generalization abilities of four machine learning models (XGBoost, RF, AdaBoost, Lasso) were effectively improved, with the XGBoost model demonstrating superior prediction accuracy (with an R<sup>2</sup> value of 0.94, an RMSE of 53.31, and an MAE of 42.93 in the test set). Based on this model, a novel Ti-7.2Al-1.8Mo-2.0Nb-0.4Si (wt.%) alloy was designed and experimentally validated. The UTS of the alloy at 600 °C was 629 MPa, closely aligning with the value (649 MPa) predicted by the model, with an error of 3.2%. Compared to as-cast Ti1100 and Ti6242S alloy (both containing six elements), the novel quinary alloy has considerable high-temperature (600 °C) mechanical properties and fewer components. The microstructure analysis revealed that the designed alloy was an α+β type alloy, featuring a typical Widmanstätten structure. The fracture form of the alloy was a mixture of brittle and ductile fracture at both room and high temperatures. Materials science ; Titanium alloy ; Quinary ; Alloy ; Deformation (meteorology) ; Aerospace ; Casting ; Microstructure ; Metallurgy ; Mechanical engineering ; Composite material ; Engineering ; Aerospace engineering Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Metal and Thin Film Mechanics
Authors: Lei Hong-yan | Muntadher Abed Hussein | Tarak Vora | Anupam Yadav | Asha Rajiv | Aman Shankhyan | Sachin Jaidka | Mehul Manu | Issa Mohammed Kadhim | Zainab Jamal Hamoodah | Fadhil Faez | Mehrdad Mottaghi
Source: Flow Measurement and Instrumentation (article, 2025-06-04)
Field: Chemistry — Analytical Chemistry

Accurate machine-learning based modeling of apparent viscosity of waxy oils mixed with polymeric wax inhibitors Wax ; Viscosity ; Materials science ; Polymer science ; Chromatography ; Thermodynamics ; Chemical engineering ; Computer science ; Petroleum engineering ; Chemistry ; Composite material ; Geology ; Physics ; Engineering Petroleum Processing and Analysis ; Lubricants and Their Additives ; Rheology and Fluid Dynamics Studies