jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chunyi Zhang | Yu-Xiang Zheng | Roberto Car | Annabella Selloni
Source: Proceedings of the National Academy of Sciences (article, 2025-08-20)
Field: Chemistry — Electrochemistry

Tuning water dissociation at oxide–electrolyte interfaces with electric fields Understanding how electric fields influence water dissociation at heterogeneous interfaces is crucial for controlling interfacial chemical reactions and advancing next-generation energy technologies. Herein, ab initio-based machine learning simulations show that even small electric field changes can significantly alter the water dissociation fraction at planar TiO<sub>2</sub>-electrolyte interfaces. The resulting free energy difference between undissociated and dissociated interfacial water exhibits a linear dependence on the field change with a slope of 1.97 <i>e</i>Å, which far exceeds the dissociation-induced dipole change of a water molecule. Employing a machine-learned collective variable to investigate the reaction statistics of thousands of water dissociation/recombination events, we find that small electric field changes exert minor effects on individual reaction energy barriers but significantly influence the populations of local configurations associated with initial states that are most favorable for reactions. These findings elucidate the pronounced impact of electric fields on interfacial water dissociation and reveal a mechanism for electric-field-controlled chemical reactions. Dissociation (chemistry) ; Electric field ; Chemical physics ; Electrolyte ; Dipole ; Self-ionization of water ; Ab initio ; Chemistry ; Oxide ; Materials science ; Atomic physics ; Physical chemistry ; Physics ; Electrode ; Organic chemistry Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Miguel Martinez-Fernandez | Md Bin Yeamin | David Dalmau | Jorge J. Carbó | Albert Poater | Juan V. Alegre‐Requena
Source: Advanced Synthesis & Catalysis (article, 2025-01-27)
Field: Chemistry — Inorganic Chemistry

Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes Abstract The semihydrogenation of alkynes to alkenes has historically been an essential technique in organic chemistry. In this context, researchers often employ transition metal complexes to achieve this conversion. Given the pronounced polarization of results, often yielding either very high or very low values, it remains challenging to discern the factors influencing reactivity and selectivity in many cases. In this work, we combine different sub‐disciplines of digital chemistry with experimental outcomes to rationalize the results of a model Ni‐catalyzed semihydrogenation that leads to E ‐alkenes. First, we analyze the main factors behind successful reactions using a machine learning classification model. The descriptors are computed directly from the SMILES strings of the reacting alkynes using an automated protocol that relies on structural features, molecular mechanics, and semi‐empirical techniques. This workflow requires minimal human intervention and provides a fast and effective approach. Next, we couple the same descriptors with activation barriers calculated with density functional theory, generating a regression model that explains reactivity based on the properties of the alkyne substrates. Overall, this study demonstrates the potential of using a combination of digital chemistry techniques to uncover reaction trends in Ni‐catalyzed semihydrogenations of alkynes, an area where human intuition proves limited in application. Chemistry ; Catalysis ; Nickel ; Combinatorial chemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis ; Catalytic C–H Functionalization Methods
Authors: Hailin Li | Bo Zhang | Yongpeng Shen | Lei Zhang | Kun Liu
Source: Scientific Reports (article, 2025-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on prediction of nanocrystalline alloy hysteresis properties based on long short-term memory network In order to predict the hysteresis characteristics of nanocrystalline alloy materials at different frequencies, a data-driven hysteresis prediction model based on the encoder-decoder architecture, which combines long short-term memory network and feedforward neural network, is proposed in this paper. The data-driven based magnetic hysteresis prediction model can take advantage of the powerful nonlinear learning ability of artificial neural network to train and learn its magnetic hysteresis characteristics of nanocrystalline alloy materials at different frequencies. Firstly, based on the encoder-decoder architecture, a hysteresis prediction model is constructed by combining long short-term memory network and feedforward neural network. Subsequently, in order to obtain the training set and validation set used for the data-driven based hysteresis prediction model, the Jiles-Atherton (J-A) hysteresis model is identified based on the B-H measurement data of a small number of nanocrystalline alloy materials at different frequencies for expediency since it is quite cumbersome and time-consuming to get these B-H data by measurement. Finally, the validity and accuracy of the data-driven based hysteresis prediction model are proved by the validation set. The maximum error is about 10.29%. The results show that the hysteresis model of neural network is able to predict hysteresis characteristics with considering the effect of frequency, which provides a new way for the simulation of hysteresis characteristics. Nanocrystalline material ; Hysteresis ; Term (time) ; Alloy ; Computer science ; Long short term memory ; Materials science ; Metallurgy ; Nanotechnology ; Artificial intelligence ; Artificial neural network ; Condensed matter physics ; Physics Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: B. Veera Siva Reddy | Deepak Kumar | C. Chandrasekhara Sastry | J. Krishnaiah | S. Suryakumar | Abdur Rahman | J. Rajesh Kumar
Source: Advanced Engineering Materials (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Machine Learning‐Based Prediction of High‐Strain‐Rate Response in Additively Manufactured Lattice Structures Using Split Hopkinson Pressure Bar Experiments This study explores the predictive modeling of high‐strain‐rate mechanical behavior of additively manufactured lattice structures using machine learning (ML) and deep learning frameworks. Body‐centered‐cubic, gyroid, and honeycomb lattice architectures are fabricated using laser powder bed fusion with A286 superalloy and subjected to split Hopkinson pressure bar (SHPB) testing at impact pressures ranging from 2 to 6 bar. Experimental data are extracted for peak stress, max strain, max strain rate, and energy absorbed and structured into a multioutput supervised learning dataset. Four regression models (linear regression, support vector regression, random forest, and a fully connected neural network) are trained and compared. The neural network achieves the highest predictive performance with R 2 values of 0.9859, 0.9884, 0.9673, and 0.9815 for PeakStress, MaxStrain, MaxStrainRate, and EnergyAbsorbed, respectively, and corresponding root mean squared errors of 7.58 MPa, 0.0121, 131.59 s − 1 , and 1.59 J. Shapley additive explanation‐based interpretability analysis reveals that Pressure_Bar is the most influential predictor for MaxStrainRate, while Lattice_Type dominates the prediction of PeakStress and EnergyAbsorbed. Parity plots and error histograms confirm model fidelity, with the majority of predictions within ±5% of experimental values. The developed ML framework demonstrates strong generalization and offers a data‐driven alternative to traditional SHPB experiments for performance evaluation of architected materials. This methodology provides a foundation for rapid surrogate modeling, material screening, and AI‐assisted design optimization in high‐strain‐rate applications, with future potential for integration into physics‐informed ML systems. Split-Hopkinson pressure bar ; Artificial neural network ; Interpretability ; Lattice (music) ; Support vector machine ; Modular design ; Ranging ; Parametric statistics ; Inertial confinement fusion ; Experimental data High-Velocity Impact and Material Behavior ; Additive Manufacturing and 3D Printing Technologies ; Cellular and Composite Structures
Authors: Dong-Hee Shin | Young-Han Son | Tae‐Eui Kam
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Sharpness-aware minimization with physics-informed regularizations for predicting semiconductor material properties in molecular dynamics Molecular dynamics ; Dynamics (music) ; Statistical physics ; Minification ; Semiconductor ; Computer science ; Physics ; Materials science ; Mathematics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jie Liu | Lin Zhang
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A physics-informed machine learning perspective to present the structures and properties of titanium matrixes and nanoclusters through atomic modeling A machine learning potential within the framework of an artificial neural network model was developed to describe the interactions among the atoms of titanium bulks and nanoclusters, wherein atomic simulations were used to present their structures and mechanical properties. The developed machine learning potential, which was trained on extensive first-principles datasets, demonstrated remarkable accuracy in predicting various lattices, elastic constants, and defect properties, along with high-temperature characteristics, including α-β structural transition, thermal expansion, and melting point for titanium matrixes. The generalised stacking fault energy lines and surfaces on multiple slip planes were used to compare the MLP performance with other potential models in assessing the material mechanical properties. The atomic-level stress maps were used to describe the atomic stress characteristics of five twin boundaries. Molecular dynamics simulations were used to present the lattice evolution of Ti bulks under high pressure at room temperature and the structural transition of titanium nanoclusters during heating. The pair analysis technique was used to describe the local packing of the atoms in the titanium nanoclusters. Nanoclusters ; Titanium ; Perspective (graphical) ; Nanotechnology ; Materials science ; Physics ; Chemical physics ; Artificial intelligence ; Computer science ; Metallurgy Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Casper Larsen | Sami Kaappa | Andreas Lynge Vishart | Thomas Bligaard | Karsten W. Jacobsen
Source: npj Computational Materials (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Global atomic structure optimization through machine-learning-enabled barrier circumvention in extra dimensions We introduce and discuss a method for global optimization of atomic structures based on the introduction of additional degrees of freedom describing: 1) the chemical identities of the atoms, 2) the degree of existence of the atoms, and 3) their positions in a higher-dimensional space (4-6 dimensions). The new degrees of freedom are incorporated in a machine-learning model through a vectorial fingerprint trained using density functional theory energies and forces. The method is shown to enhance global optimization of atomic structures by circumvention of energy barriers otherwise encountered in the conventional energy landscape. The method is applied to clusters as well as to periodic systems with simultaneous optimization of atomic coordinates and unit cell vectors. Finally, we use the method to determine the possible structures of a dual atom catalyst consisting of a Fe-Co pair embedded in nitrogen-doped graphene. Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jiexin Shi | Xiaohong Zhang | Venkata Rohit Punyapu | Rachel B. Getman
Source: The Journal of Chemical Physics (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer-Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water-adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. These findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)-water interfaces. Solvation ; Implicit solvation ; Chemistry ; van der Waals force ; Density functional theory ; Water model ; Molecular dynamics ; Solvation shell ; Chemical physics ; Thermodynamics ; Computational chemistry ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis
Authors: Pieter Dobbelaere | Sander Vandenhaute | Véronique Van Speybroeck
Source: Chemistry of Materials (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Cluster-Based Machine Learning Potentials to Describe Disordered Metal–Organic Frameworks up to the Mesoscale Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model's training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over 20 thousand atoms. Afterward, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behavior. Our analysis includes large supercell structures, demonstrating that (near-) ab initio accuracy is within reach at the mesoscale. Mesoscale meteorology ; Cluster (spacecraft) ; Metal-organic framework ; Materials science ; Nanotechnology ; Computer science ; Chemical physics ; Artificial intelligence ; Physics ; Chemistry ; Adsorption ; Physical chemistry ; Meteorology Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Huiping Zeng | Guangcai Ma | Xiaoying Zheng | Da Lin | Jie Zhang | Dong Li
Source: International Journal of Biological Macromolecules (article, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Preparation, application and regeneration of chitosan composite adsorbents for arsenic removal: A review from a sustainable perspective Adsorption ; Chitosan ; Arsenic ; Environmental remediation ; Raw material ; Composite number ; Desorption Dye analysis and toxicity ; Arsenic contamination and mitigation
Authors: Nangamso Nathaniel Nyangiwe
Source: Next research. (review, 2025-08-06)
Field: Materials Science — Materials Chemistry

The role of computational materials science in achieving sustainable development goals: A review Computational materials science advances progress toward the United Nations’ Sustainable Development Goals by applying the rational design of sustainable materials and technologies. However, a systematic assessment of how computational materials science helps bridge the gap between theoretical predictions and real-world applications is still underexplored. This review aims to critically assess the role of computational materials science techniques such as density functional theory, molecular dynamics, and machine learning for relevant challenges in clean energy (SDG 7), water purification (SDG 6), climate-resilient infrastructure (SDG 9 and 13), and sustainable material cycles (SDG 12). This review presents evidence on how these approaches provide means to overcome the limitations of conventional experiments in terms of high throughput screening, reduced water, and manpower consumption, as well as increased pace of innovation. The review also discusses the model validation through experimental benchmarking and multi scale simulation for guaranteeing the reliability of the predictive design of materials. This leads to implications for realising advancements with CMS, such as tying for energy efficient catalysis and polymeric recyclable materials, indeed contributing toward sustainability goals. However, some issues persist, mainly revolving around data availability, computational costs, and small mismatches between simulations and experimental results. To enhance further, interdisciplinary collaborations, open-data initiatives, and experimental research alongside CMS should be promoted. These approaches will help the materials science consortium to convert computational knowledge into scalable solutions to foster development that is equitable and sustainable. Sustainable development ; Engineering ethics ; Management science ; Political science ; Computer science ; Engineering Machine Learning in Materials Science
Authors: Carson Farmer | Hector Medina
Source: Journal of Computational Chemistry (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

IMPACT‐4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections Collision cross section (CCS) values can enhance the identification and classification of molecular contaminants such as per- and polyfluororoalkyl substances (PFAS). However, the computational burden required for large molecules, combined with the increasing number of potential PFAS candidates, can render existing methods incapable of providing sufficiently accurate results in a timely manner. Furthermore, machine learning methods struggle to generalize when the (de)protonated structure undergoes structural changes that are not common in the training dataset. In this study, we introduce IMPACT4-CCS (Integrated Modeling and Prediction using Ab initio and Trained potentials for Collision Cross Section), a novel computational workflow ensemble that comprises ab initio with machine learning tasks to accelerate accurate prediction of CCS for PFAS molecules. IMPACT-4CCS achieves comparable accuracy to current machine learning approaches, as validated using a test set of 100 molecules. Furthermore, IMPACT-4CCS exhibits better accuracy when implemented on some specific emerging PFAS subclasses, such as the nH-perfluoroalkyl carboxylic acids (nH-PFCA) family, for which other methods overestimate their CCS values. As far as the authors know, IMPACT-4CCS is the only existing method capable of capturing structural dynamics (i.e., hydrogen bridging) present in some large and flexible PFAS molecules. Our work demonstrates that the careful use of machine learning to accelerate traditional methods is likely to be more accurate than relying purely on machine learning on molecular graphs. Future (or recommended) work includes assessing the usefulness of IMPACT-4CCS for extending nontarget analysis to larger PFAS datasets such as the OECD (Organization for Economic Co-operation and Development) PFAS list in PubChem, which could be greater than 7 million molecules with diverse chemistry. Computer science ; Ab initio ; Machine learning ; Workflow ; Artificial intelligence ; Collision ; Bridging (networking) ; CASP ; Set (abstract data type) ; Chemistry ; Protein structure prediction ; Protein structure ; Database Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Aya Fujiwara | Sunao Nakanowatari | Yohei Cho | Toshiaki Taniike
Source: Science and Technology of Advanced Materials (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La<sub>2</sub>O<sub>3</sub>-, TiO<sub>2</sub>-, and ZrO<sub>2</sub>-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning. Robustness (evolution) ; Catalysis ; Limiting ; Computer science ; Feature (linguistics) ; Throughput ; Biochemical engineering ; Artificial intelligence ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Mechanical engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science