jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. Jelle Lagerweij (21511591) | Sana Bougueroua (14044965) | Parsa Habibi (11355577) | Poulumi Dey (11355580) | Marie-Pierre Gaigeot (1291467) | Othonas A. Moultos (7306286) | Thijs J. H. Vlugt (7306289)
Source: Figshare (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

From GrotthussTransfer to Conductivity: Machine LearningMolecular Dynamics of Aqueous KOH Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH<sup>–</sup> is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations. Molecular dynamics ; Aqueous solution ; Ab initio ; Transfer (computing) ; Ab initio quantum chemistry methods ; Hydrogen bond ; Work (physics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Xiaowei Xie | John L. Weber | Mats Svensson | Ryne C. Johnston | Edward Harder | Leif D. Jacobson
Source: ChemRxiv (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field Force field (fiction) ; Ab initio ; Solvation ; Workflow ; Free energy perturbation ; Ab initio quantum chemistry methods ; Field (mathematics) ; Potential energy ; Quantum Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Fuel Cells and Related Materials
Authors: Zheng Mingliang | Taoufik Saidani | Muhammad Imran Khan | Ali U. Chaudhry | A. Zeeshan | Nouman Ijaz | Nidhal Ben Khedher
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Machine learning-based surrogate model for bubble dynamics and cavitation behaviour under sinusoidal waves Surrogate model ; Nonlinear system ; Cavitation ; Artificial neural network ; Bubble ; Microbubbles ; Computation ; Sonar ; Energy (signal processing) Ultrasound and Cavitation Phenomena ; Ultrasound and Hyperthermia Applications ; Cavitation Phenomena in Pumps
Authors: Long Xu | B. F. Mohamed | G. Tang | Qiang Guo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modeling Anisotropic Elasto-Plasticity in Cu-Nb Nanolaminates: Effects of Size and Texture Via Physics-Based and Data-Driven Approaches Anisotropy ; Texture (cosmology) ; Plasticity ; Materials science ; Condensed matter physics ; Statistical physics ; Physics ; Composite material ; Computer science ; Artificial intelligence ; Optics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: J. B. Jiao | Xi Alice Gao | Jie Li
Source: ArXiv.org (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Pure Component Property Estimation Framework Using Explainable Machine Learning Methods Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modeling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework, the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted R2 is introduced to penalize the inclusion of additional features, providing an assessment of the true contribution of features. The prediction results for normal boiling point (Tb), liquid molar volume, critical temperature (Tc) and critical pressure (Pc) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for Tb, Tc, and Pc confirms that different molecular properties are influenced by different structural features, aligning with mechanistic interpretations. In conclusion, the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling. Interpretability ; Feature (linguistics) ; Component (thermodynamics) ; Property (philosophy) ; Artificial neural network ; Kriging ; Representation (politics) ; Pattern recognition (psychology) ; Gaussian process ; Ranking (information retrieval) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Anurupa Maiti (8372811) | Sutanu Nandi (9707720) | Biplop Jyoti Hazarika (20592782) | Bibek Pramanik (21247234) | Amit Biswas (8728074) | Anup Bhunia (1743352)
Source: Figshare (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine LearningAssisted Materials Classificationto Boost Catalyst Design for Electrochemical Oxidation Machine learning (ML) is revolutionizing materials science with electrocatalysis emerging as a particularly promising area. While numerous noble and non-noble materials have been explored for chlorine evolution reactions (CER), identifying robust and readily accessible electrocatalysts for broader electrochemical oxidation remains a significant challenge. In this study, we leverage ML to address this gap and identify such materials. We examine the complex relationships between the Fermi level and conduction band position of cobalt-based oxides, alongside various uncommon descriptors such as formation energy, energy above the hull, density, number of magnetic sites, and total magnetization. The data underwent careful cleaning and feature engineering using different ML processes to ensure accuracy. Models were trained on 70% of the data and tested on the remaining 30%. Using a Random Forest classifier, we analyzed electrochemical data and identified Co<sub>3</sub>O<sub>4</sub> as a cost-effective and scalable material for electrochemical oxidation. This machine-learning-driven approach revealed that Co<sub>3</sub>O<sub>4</sub> is more susceptible to oxidation, leading to high reaction efficiency in synthetic applications such as arene chlorination and epoxide conversion. Electrocatalyst ; Leverage (statistics) ; Electrochemistry ; Scalability ; Catalysis ; Electrochemical energy conversion ; Random forest Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Jingyang Zhang | Zhixue Guo | Bing Wang | Liying Liu | Ru‐Zhi Wang
Source: Small (review, 2025-06-19)
Field: Materials Science — Materials Chemistry

Tailoring Superlattice Dimensions: A Pathway to Emergent Quantum Functional Devices The evolution of materials engineering has been constrained by the limitations inherent in naturally occurring materials. However, superlattices (SLs) with tunable dimensions (0D-3D) have revolutionized this field by enabling precise control over atomic-scale periodicity and interfacial interactions, synergistically integrating multifunctional properties from diverse materials. Beyond natural atomic configurations, these engineered structures can achieve unconventional atomic arrangements, thus unlocking unprecedented material capabilities toward emergent quantum functional devices. This review systematically examines recent advancements in SLs' research, including synthesis methods through physical and chemical approaches tailored to various dimensionalities. A comparative analysis of fabrication techniques and application domains is provided to guide future method selection. Furthermore, emerging applications of SLs in optical modulation, bandgap engineering, photoelectronic conversion, and magnetic property transitions are comprehensively discussed. The advantages and limitations of SLs across dimensions are critically evaluated, followed by proposals for innovative solutions that integrate machine-learning-aided design and in situ characterization techniques. This review aims to provide actionable insights for developing next-generation SL-based quantum functional devices. Superlattice ; Materials science ; Nanotechnology ; Quantum ; Density functional theory ; Quantum dot ; Optoelectronics ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Semiconductor Quantum Structures and Devices ; Semiconductor materials and devices
Authors: Ehsan Shahini (10296807) | Narendra Chaulagain (11382687) | Karthik Shankar (1948327) | Tian Tang (66588)
Source: Figshare (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Hierarchy in Binary Liquid Phase Exfoliation of GraphiticCarbon Nitride: Dissecting the Dominance of One Solvent This study targets an important question in the synthesis of graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>) nanosheets via liquid-phase exfoliation (LPE): how does a binary solvent mixture perform relative to its components? A machine learning model based on Extra Trees Regressor, when applied to 171 pure solvents and 14,535 binary solvents resulting from their arbitrary combination, reveals an interesting phenomenon where the LPE performance of the binary mixture can be dominated by one of its components. Quantitatively the free energy of exfoliation (ΔG<sub>exf</sub>), defined as the free energy to separate a unit area of two stacked nanosheets, can have close values for the binary solvent and one of its components that has either a large ΔG<sub>exf</sub> (low LPE efficacy) or a small ΔG<sub>exf</sub> (high LPE efficacy). Such nonstandard performance is validated by umbrella sampling molecular dynamics (MD) simulations, and examined in detail for two representative binary solvents: one where <i>N</i>-Methyl-2-pyrrolidone (NMP) dominates in NMP:Cyclohexane mixture showing good LPE performance, and the other where methanol (MET) dominates in MET:Dichloromethane mixture showing poor LPE performance. Results from this study challenge existing, largely surface tension based, solvent selection criteria for LPE, and emphasize the importance of molecular details in the solvation layers around the nanosheets. Our numerical framework, integrating MD with machine learning, can be used to further explore strategies in the synthesis of 2D materials, such as optimizing solvent mixtures and introducing functional groups to g-C<sub>3</sub>N<sub>4</sub>. Exfoliation joint ; Binary number ; Solvent ; Solvation ; Methanol ; Graphite ; Molecular dynamics ; Carbon fibers Graphene research and applications ; 2D Materials and Applications ; Covalent Organic Framework Applications
Authors: Revanth Elangovan | Sompriya Chatterjee | Dhiman Ray
Source: Proceedings of the National Academy of Sciences (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Data-driven enhanced sampling of mechanistic pathways The mechanisms of molecular processes can be characterized by following the minimum free energy pathway (MFEP) on the underlying multidimensional conformational landscapes. Despite recent advancements in enhanced sampling algorithms, obtaining a converged high-dimensional molecular free energy landscape remains a considerable challenge. To circumvent this issue, we employ a deep multitask learning algorithm that integrates deep neural networks with the established enhanced sampling method of well-tempered metadynamics to iteratively learn the MFEP between reactant and product conformations, without the knowledge of the underlying free energy landscape. Our approach improves upon existing pathway exploration algorithms by following a simpler protocol, thereby eliminating the need to identify intermediate structures along a guess path. From the learned pathway, an automatic reconstruction of a mechanistic fingerprint can be performed by following the sequence of events in the molecular process, allowing for a direct characterization of the molecular mechanism. We demonstrate applications of our algorithm to prototypical chemical reactions, protein folding, and ligand-receptor binding problems. Due to its low computational cost and overall simplicity, this framework is expected to find widespread applications in elucidating molecular mechanisms at all-atom resolution. Metadynamics ; Sampling (signal processing) ; Energy (signal processing) ; Sequence (biology) ; Molecular dynamics ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ehsan Shahini | Narendra Chaulagain | Karthik Shankar | Tian Tang
Source: Langmuir (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Hierarchy in Binary Liquid Phase Exfoliation of Graphitic Carbon Nitride: Dissecting the Dominance of One Solvent This study targets an important question in the synthesis of graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>) nanosheets via liquid-phase exfoliation (LPE): how does a binary solvent mixture perform relative to its components? A machine learning model based on Extra Trees Regressor, when applied to 171 pure solvents and 14,535 binary solvents resulting from their arbitrary combination, reveals an interesting phenomenon where the LPE performance of the binary mixture can be dominated by one of its components. Quantitatively the free energy of exfoliation (ΔG<sub>exf</sub>), defined as the free energy to separate a unit area of two stacked nanosheets, can have close values for the binary solvent and one of its components that has either a large ΔG<sub>exf</sub> (low LPE efficacy) or a small ΔG<sub>exf</sub> (high LPE efficacy). Such nonstandard performance is validated by umbrella sampling molecular dynamics (MD) simulations, and examined in detail for two representative binary solvents: one where <i>N</i>-Methyl-2-pyrrolidone (NMP) dominates in NMP:Cyclohexane mixture showing good LPE performance, and the other where methanol (MET) dominates in MET:Dichloromethane mixture showing poor LPE performance. Results from this study challenge existing, largely surface tension based, solvent selection criteria for LPE, and emphasize the importance of molecular details in the solvation layers around the nanosheets. Our numerical framework, integrating MD with machine learning, can be used to further explore strategies in the synthesis of 2D materials, such as optimizing solvent mixtures and introducing functional groups to g-C<sub>3</sub>N<sub>4</sub>. Solvent ; Exfoliation joint ; Solvation ; Materials science ; Binary number ; Cyclohexane ; Methanol ; Molecular dynamics ; Phase (matter) ; Graphitic carbon nitride ; Chemical engineering ; Chemical physics ; Nanotechnology ; Chemistry ; Organic chemistry ; Graphene ; Computational chemistry ; Mathematics Graphene research and applications ; Semiconductor materials and devices ; Nanopore and Nanochannel Transport Studies
Authors: Mengxin Lu | Jianfu Li | Mengyuan Zhu | Yang Lv | Zuoyan Zhang | Yong Han Liu | Jianan Yuan | Jiani Lin | Xiaoli Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Universal Three-Dimensional Superionic Diffusion Template: B56 Diffusion ; Computer science ; Materials science ; Physics ; Thermodynamics Advanced ceramic materials synthesis
Authors: Van-Sang Ha | Jae-Wook Jeon
Source:  (article, 2025-01-19)
Field: Materials Science — Surfaces, Coatings and Films

Optimizing XPS Vacuum Data Drift Detection: A Comparative Study of CUSUM, Kernel CUSUM, and LSTM Maintaining an ultra-high vacuum is critical for the stable operation of X-ray sources in X-ray Photoelectron Spectroscopy (XPS) within semiconductor manufacturing. However, challenges in sustaining such vacuums often lead to equipment failures, particularly when vacuum levels drop below 4×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−8</sup> Torr, causing damage to the E-Gun source and extended maintenance periods. Traditional methods like Fault Detection and Classification (FDC) and Time-Based Preventive Maintenance (TBM) struggle to predict these failures accurately. This paper proposes a Kernel CUSUM method for detecting subtle vacuum data drifts, enabling early identification of potential issues before critical damage occurs. By implementing this approach, we enhance maintenance strategies, reduce unplanned downtime, and achieve significant cost savings in maintenance and repairs. Additionally, we compare the general CUSUM, a statistical method, with Kernel CUSUM, which incorporates machine learning techniques, and LSTM, a deep learning method, analyzing the advantages and disadvantages of each. Our findings demonstrate the financial and operational benefits of adopting advanced data analysis techniques for equipment maintenance in semiconductor manufacturing. CUSUM ; Computer science ; Kernel (algebra) ; Artificial intelligence ; Statistics ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Advanced Data Storage Technologies
Authors: Michael J. Hall
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

High-Throughput Crystallisation of Organic Molecules on the Nanoscale - Successes, Challenges and Future Directions Despite the considerable advances in single crystal X-ray and electron diffraction technologies, a key bottleneck in the investigation of any organic molecular crystal, be it to obtain molecular structure, absolute stereochemistry or crystal packing information, is the challenge of accessing suitable crystals for measurement. Crystallisation of a molecule requires successful navigation of a complex multivariant experimental landscape, currently reliant on classical crystallisation methodologies, which are expensive in both time and material requirements. Through the use of liquid handling robots (sptlabtech, mosquito®), we have developed an Encapsulated Nanodroplet Crystallisation methodology (ENaCt) in which a few milligrams of an organic molecule are used in high-throughput nanoscale crystallisation experiments. Molecules are crystallised from 50 nL volumes of organic solvent, encapsulated within a 200 nL oil droplet, arranged in 96-well plate formats. Subsequent slow concentration or mixing of the sample over time (up to 14 days) results in the growth of crystals suitable for diffraction analysis by SCXRD (Figure 1) [1,2]. Through the use of ENaCt methods we can rapidly screen 1000’s of experimental crystallisation conditions, using only a few milligrams of sample, resulting in high success rates. We have applied ENaCt methods to diverse areas in the crystal growth of small molecules, including polymorph discovery,[3] natural products,[4] pharmaceuticals [5], and hydrogen-bonded organic frameworks (HOFs) [6]. Herein we will discuss our recent advances in high-throughput ENaCt techniques, including the creation of a platform for natural product structural elucidation, the discovery of the 14th polymorph of ROY (a world record in small molecule polymorphs), the development of high-throughput optical and X-ray based crystallisation readout technologies, methods for accessing co-crystals including higher order co-crystals (HOCs) containing three or more molecules, the interfacing of ENaCt with microED, and the development of “predictive” crystallisation through a machine learning/Bayesian analysis approach. Throughput ; Nanoscopic scale ; Organic molecules ; Crystallization ; Computer science ; Nanotechnology ; Materials science ; Molecule ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Shuhao Zhang | R.I. Zubatyuk | Yinuo Yang | Adrián E. Roitberg | Olexandr Isayev
Source: ChemRxiv (preprint, 2025-02-28)
Field: Materials Science — Materials Chemistry

ANI-1xBB: an ANI based reactive potential Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semi-empirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive datasets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena. Geology Machine Learning in Materials Science