jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reeda Shakeel | Abrar Ahmad | Habiba Mushtaq | Touseef Haider | Mamnat Javeria | Areeba Asif | Kianat Zia | Hajra Zafar | Burhan Ishaque | Waheed Zaman Khan
Source: Scholars Journal of Engineering and Technology (article, 2025-08-30)
Field: Chemistry — Organic Chemistry

Multifunctional Nanomaterials in Materials Chemistry: A Cross-Disciplinary Review of Applications in Energy, Environment, Catalysis, and Biomedicine Multifunctional nanomaterials are at the frontier of materials chemistry, having the ability to allow customised property and structure-integrated functionalities to facilitate various applications in energy conversion, environmental remediation, catalysis, biomedical systems and chemical sensing. With sophisticated control of composition, morphology and surface chemistry, extensive families of nanostructures have been designed that satisfy the requirements of a particular application, including metal and metal oxide nanoparticles, carbon-based materials (e.g. graphene and carbon nanotubes), metalorganic frameworks, perovskite materials and hybrid nanocomposites. This familiarization is a critical assessment of recent progress in their synthesis, structure--function relationships and performance of applicability across various disciplines. In spite of the changes that have been achieved, there are still a number of challenges that have not been dealt with. These are gaps in mass manufacturing, lack of long-term stability in its functioning, and combination of numerous functionalities in one system, and toxicity, biodegradability and persistence issues. Responding to these issues, the review proposes the following key future directions, namely sustainable and green synthesis methodologies, machine learning to predict and optimize materials, design of bioinspired multifunctional systems, and standardization of safety and regulatory assessment. This brings together three fields chemistry, nanotechnology, and materials engineering to present a comprehensive view on how to develop and implement next-generation nanomaterials to have a cross-sectoral influence. Biomedicine ; Nanomaterials ; Nanotechnology ; Cross disciplinary ; Catalysis ; Chemistry ; Materials science ; Organic chemistry ; Computer science ; Bioinformatics ; Data science Nanomaterials for catalytic reactions
Authors: Bangchen Yin | Jiaao Wang | Weitao Du | Pengbo Wang | Penghua Ying | Haojun Jia | Zisheng Zhang | Yuanqi Du | Carla P. Gomes | Chenru Duan | Graeme Henkelman | Hai Xiao
Source: npj Computational Materials (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

AlphaNet: scaling up local-frame-based neural network interatomic potentials Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions and enabling contractions through spatial domain and temporal domain, AlphaNet enhances the representational capacity of atomic environments, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials. Our code and data are available at https://github.com/zmyybc/AlphaNet . Scalability ; Interatomic potential ; Molecular dynamics ; Scaling ; Transferability ; Multiscale modeling ; Ranging ; Artificial neural network ; Computation Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Azal S. Waheeb | Duha M. Hasan | Shaimaa Hassan Mallah | Sajjad Hussain Sumrra | Sadaf Noreen | Ashraf Y. Elnaggar | Abrar U. Hassan | Islam H. El Azab | Hussein Ali Kadhim Kyhoiesh | M.H.H. Mahmoud
Source: Journal of Fluorescence (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Exploring New Azobenzene Type Photoswitches by Machine Learning with Lowest Possible Fluorescence Excitons with Ease of Synthesis Azobenzene ; Chemistry ; Fluorescence ; Exciton ; Photochemistry ; Nanotechnology ; Molecule ; Organic chemistry ; Optics ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Valentin V. Vlasov | A. Yu. Kuzin | Irina Florya | Nikita Sergeevich Buriak | Vasiliy S. Chernyshev | Alexander Golikov | L.V. Krasnov | Simon Mikhaylov | Mikhail Pugach | Albert G. Nasibulin | P An | Vadim Kovalyuk | Gregory Goltsman | Dmitry A. Gorin
Source: Journal of Energy Storage (article, 2025-01-18)
Field: Chemistry — Electrochemistry

Hybrid nanophotonic-microfluidic sensor integrated with machine learning for operando state-of-charge monitoring in vanadium flow batteries Microfluidics ; Vanadium ; Nanotechnology ; Nanophotonics ; Charge (physics) ; Materials science ; Flow (mathematics) ; State (computer science) ; Vanadium dioxide ; Flow chemistry ; State of charge ; Optoelectronics ; Computer science ; Engineering ; Continuous flow ; Physics ; Biochemical engineering ; Battery (electricity) Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced battery technologies research
Authors: Yann L. Müller | Anirudh Raju Natarajan
Source: npj Computational Materials (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Constructing multicomponent cluster expansions with machine-learning and chemical embedding Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the <i>embedded cluster expansion</i> (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements. Cluster (spacecraft) ; Embedding ; Computer science ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xujian Wang | Xiongwu Wu | Bernard R. Brooks | Junmei Wang
Source: Journal of Chemical Theory and Computation (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials This work develops a hybrid machine learning/molecular mechanics (ML/MM) interface integrated into the AMBER molecular simulation package. The resulting platform is highly versatile, accommodating several advanced machine learning interatomic potential (MLIP) models while providing stable simulation capabilities and supporting high-performance computations. Building upon this robust foundation, we developed new computational protocols to enable pathway-based and end point-based free energy calculation methods utilizing ML/MM hybrid potential. In particular, we proposed an ML/MM-compatible thermodynamic integration (TI) framework that adequately addressed the challenge of applying MLIPs in TI calculations due to its indivisible nature of energy and force. Our results demonstrated that the hydration free energies calculated using this framework achieved an accuracy of 1.0 kcal/mol, outperforming the traditional approaches. Moreover, ML/MM enables more precise sampling of conformational ensembles for improved end point-based free energy calculations. Overall, our efficient, stable, and highly compatible interface not only broadens the application of MLIPs in multiscale simulations but also enhances the accuracy of free energy calculations from multiple aspects. By introducing a novel ML/MM-compatible thermodynamic integration framework, we offered a novel foundation for combining advanced multiscale simulation methodologies with highly accurate free energy calculation techniques, thereby opening new avenues and providing a robust theoretical framework for future developments in this field. Computer science ; Energy (signal processing) ; Molecular dynamics ; Molecular mechanics ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Khaled M A El-Feki | Mohammad Magdy El‐Metwally | Tarek H. Taha | Youssef M. M. Mohammed | Fatimah Al-Otibi | Abdulaziz S. Fakhouri | Farid Menaa | WesamEldin I. A. Saber
Source: International Journal of Biological Macromolecules (article, 2025-05-15)
Field: Materials Science — Biomaterials

Artificial neural network-based fungal chitin production for submicron-chitosan synthesis: effects on bioremediation for heavy metal pollution Bioremediation ; Chitin ; Chitosan ; Pollution ; Chemistry ; Heavy metals ; Environmental chemistry ; Environmental science ; Biochemical engineering ; Biology ; Ecology ; Engineering ; Contamination ; Organic chemistry Nanocomposite Films for Food Packaging ; Studies on Chitinases and Chitosanases
Authors: Thomas Plé | Olivier Adjoua | Anouar Benali | Evgeny Posenitskiy | Corentin Villot | Louis Lagardère | Jean‐Philip Piquemal
Source: ChemRxiv (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

A Foundation Model for Accurate Atomistic Simulations in Drug Design Neural network potentials now offer robust alternatives to electronic structure and empirical force fields computations for the on-the-fly production of the potential energy surfaces required in atomistic Molecular Dynamics (MD) simulations. However, widespread application in Chemistry and Biology faces several challenges: the need for fast inference and economical training; stringent model transferability requirements, particularly including charged-species interactions. Trained exclusively on synthetic quantum chemistry data, FeNNix-Bio1 sets a new standard for Foundation machine learning Models to provide predictive condensed-phase MD simulations including quantum nuclear effects. Its full-range of capabilities is demonstrated by modelling: water properties, ions in solution, small molecules hydration free energies, complex folding free-energy landscapes, large-scale protein dynamics, protein-ligand binding, chemical reactions and by coupling it to protein structure prediction foundation models' outputs for further refinement. FeNNix-Bio1 is accurate and systematically improvable while limiting human parametrization efforts: it is likely to have a strong impact in Drug Design. Foundation (evidence) ; Computer science ; Management science ; Statistical physics ; Engineering ; Political science ; Physics ; Law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jichen Li | Lisanne Knijff | Zhan‐Yun Zhang | Linnéa Andersson | Chao Zhang
Source: Journal of Chemical Theory and Computation (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems Electrochemical energy storage and conversion play increasingly important roles in electrification and sustainable development across the globe. A key challenge therein is to understand, control, and design electrochemical energy materials with atomistic precision. This requires inputs from molecular modeling powered by machine learning (ML) techniques. In this work, we have upgraded our pairwise interaction neural network Python package PiNN via introducing equivariant features to the PiNet2 architecture for fitting potential energy surfaces along with PiNet2-dipole for dipole and charge predictions as well as PiNet2-χ for generating atom-condensed charge response kernels. By benchmarking publicly accessible data sets of small molecules, crystalline materials, and liquid electrolytes, we found that the equivariant PiNet2 shows significant improvements over the original PiNet architecture and provides a state-of-the-art overall performance. Furthermore, leveraging on plug-ins such as PiNNAcLe for an adaptive learn-on-the-fly workflow in generating ML potentials and PiNNwall for modeling heterogeneous electrodes under external bias, we expect PiNN to serve as a versatile and high-performing ML-accelerated platform for molecular modeling of electrochemical systems. Suite ; Computer science ; Artificial neural network ; Equivariant map ; Data science ; Artificial intelligence ; Geography Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications
Authors: Salomé Trillot | Nathalie Tarrat | Nicolas Combe | Patrizio Benzo | Caroline Bonafos | Magali Benoit
Source: The Journal of Chemical Physics (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Evidence and origin of anomalous diffusion of Ag+ ion in amorphous silica: A molecular dynamics study with neural network interatomic potentials The release of Ag+ ions into the environment through silica layers is a promising strategy for the development of anti-microbial surface coating devices. The aim of the present study is to provide some insight into the elementary mechanisms of diffusion of Ag+ ions through silica with the objective of proposing control strategies. Thanks to the development of interaction potentials based on neural networks, the diffusion processes were studied via molecular dynamics simulations. Silver diffusion was found to be anomalous and sub-diffusive, the origin of which could be attributed to deceleration and temporal anti-correlations. This sub-diffusion has been attributed primarily to the disordered nature of the silica matrix. Furthermore, it is magnified by the presence of coordination defects within the silica matrix. These defects, in particular the under-coordinated oxygen atoms, act as traps for Ag+ by forming O-Ag bonds, thereby limiting the jump length and retaining the ion for long duration. By comparison with existing diffusion models, the diffusion mechanism in the absence of defects appears to be of the fractional Brownian motion type, substantially modified by the presence of defects. Two possible approaches have emerged to tune the release of Ag+ ions through the silica layer: the monitoring of the number of defects and the opening/closing of diffusion paths via, e.g., a modification of the silica density. Diffusion ; Chemical physics ; Amorphous silica ; Ion ; Molecular dynamics ; Materials science ; Amorphous solid ; Brownian motion ; Nanotechnology ; Chemistry ; Chemical engineering ; Computational chemistry ; Crystallography ; Thermodynamics ; Organic chemistry ; Physics Material Dynamics and Properties ; Electrostatics and Colloid Interactions ; Photonic Crystals and Applications
Authors: Mohamed Ait Tamerd | Xiaoting Lin | Jian Wang | Limin Cai | Abdelilah Lahmar | Jiwei Ma | Menghao Yang
Source: Science China Materials (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Accelerated discovery of solid-state battery properties enabled by active learning approaches Materials science ; Solid-state ; Battery (electricity) ; State (computer science) ; Nanotechnology ; Engineering physics ; Biochemical engineering ; Computer science ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Genming Lai | Ruiqi Zhang | Chi Fang | Juntao Zhao | Taowen Chen | Yunxing Zuo | Bo Xu | Jiaxin Zheng
Source: npj Computational Materials (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode Abstract Although the electrode-electrolyte interface is a crucial electrochemical region, the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools. Theoretical simulations with this delicate interface also remain one of the most significant challenges for atomistic modeling, particularly for the stable long-timescale simulation of the interface. Here we introduce a novel scheme, hybrid ab initio molecular dynamics combined with machine learning potential (HAML), to accelerate the modeling of electrode-electrolyte interface reactions. We demonstrate its effectiveness in modeling the interfaces of Li metal with both liquid and solid-state electrolytes, capturing critical processes over extended time scales. Furthermore, we reveal the role of interface reaction kinetics in interface regulation through HAML simulations, combined with the similarity analysis method. It is demonstrated that element (Se, F, O) doping in the Li 6 PS 5 Cl system is an effective strategy for enhancing interface reaction kinetics, facilitating the formation of a more stable interface protective layer faster at room temperature. Moreover, moderate structural instability can positively contribute to interface stabilization. HAML offers a promising approach for addressing the challenge of designing stable interfaces while reducing computational costs. This work provides valuable insights for advancing the understanding and optimization of interface behaviors in Li metal batteries. Interface (matter) ; Electrolyte ; Materials science ; Lithium (medication) ; Anode ; Electrochemistry ; Computer science ; Nanotechnology ; Electrode ; Chemical physics ; Chemistry ; Physical chemistry ; Composite material Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Ajeet Kumar Yadav | Meher K. Prakash | Pradipta Bandyopadhyay
Source: The Journal of Physical Chemistry B (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Physics-Based Machine Learning to Predict Hydration Free Energies for Small Molecules with a Minimal Number of Descriptors: Interpretable and Accurate Hydration free energy (HFE) of molecules is a fundamental property having importance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Machine learning (ML) models are increasingly being used to predict HFE. Although the accuracy of ML models for data sets for small molecules is impressive, these models suffer from lack of interpretability. In this work, we have developed a physics-based ML model with only six descriptors, which is both accurate and fully interpretable, and applied it to a database for small molecule HFE, <i>FreeSolv</i>. We evaluated the electrostatic energy by an approximate closed form of the Generalized Born (GB) model and polar surface area. In addition, we have log <i>P</i> and hydrogen bond acceptor and donors as descriptors along with the number of rotatable bonds. We have used different ML models, such as random forest and extreme gradient boosting. The best result from these models has a mean absolute error of only 0.74 kcal/mol. The main power of this model is that the descriptors have clear physical meaning, and it was found that the descriptor describing the electrostatics and the polar surface area, followed by the hydrogen bond donors and acceptors, are the most important factors for the calculation of hydration free energy. Artificial intelligence ; Machine learning ; Statistical physics ; Computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tianqi Liu | Lufeng Xue | Bo Cheng | Yu Zhao | Bang Dou | Marcelo Paredes | Lu-Kai Song
Source: Digital Twin (review, 2025-06-05)
Field: Materials Science — Materials Chemistry

Machine learning-driven alloy digital design for hydrogen storage: a review Alloy ; Hydrogen ; Nickel alloy ; Work (physics) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Yihan Yun | Mengfan Wu | Zhihua Yang | Guangmao Li | Shilie Pan
Source: Advanced Science (article, 2025-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergistic Machine Learning Guided Discovery of ABa <sub>3</sub> (BSe <sub>3</sub> ) <sub>2</sub> X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials Discovering novel infrared functional materials (IRFMs) hold tremendous significance for laser industry. Incorporating artificial intelligence into material discovery has been recognized as a pivotal trend driving advancements in materials science. In this work, an IRFM predictor based on machine learning (ML) is developed for the pre-selection of the most promising candidates, in which interpretable analyses reveal the prior domain knowledge of IRFMs. Under the guidance of this IRFM predictor, a series of selenoborates, ABa<sub>3</sub>(BSe<sub>3</sub>)<sub>2</sub>X (A = Rb, Cs; X = Cl, Br, I) are successfully predicted and synthesized. Comprehensive characterizations together with first-principles analyses reveal that these materials exhibit preferred properties of wide bandgaps (2.92 - 3.04 eV), moderate birefringence (0.145 - 0.170 at 1064 nm), high laser-induced damage thresholds (LIDTs) (4 - 6 Ý AGS) and large second harmonic generation (SHG) responses (0.9 - 1 × AGS). Structure-property relationship analyses indicate that the [BSe<sub>3</sub>] unit can be regarded as a potential gene for exploring novel IRFMs. This work may open an avenue for exploring high-performance materials. Machine learning ; Artificial intelligence ; Materials science ; Property (philosophy) ; Laser ; Computer science ; Nanotechnology ; Physics ; Optics Crystal Structures and Properties ; Inorganic Chemistry and Materials ; Luminescence Properties of Advanced Materials