jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenglong Qiu | Tore Brinck | Jiacheng Wang
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modeling the potential energy surface by force fields for heterogeneous catalysis: classification, applications, and challenges The concept of the potential energy surface (PES) in computational simulations is essential for studying material properties and heterogeneous catalytic processes. However, constructing the PES using quantum mechanical methods is computationally expensive and typically limited to small systems. Force field methods, which rely on quantum mechanical data, use simple functional relationships to establish a mapping between system energy and atomic positions or charges. Force field methods are more efficient for handling large-scale systems, such as catalyst structures, adsorption and diffusion of reaction molecules, and heterogeneous catalytic processes. To further promote in-depth research in this field, this review introduces the classification, development, and characteristics of various force field methods including classical force fields, reactive force fields, and machine learning force fields. It summarizes the forms, fitting methods, and distinct periods of these force field methods. Additionally, these force field approaches are compared in terms of their applicability, accuracy, efficiency, and fitting methods. Finally, the optimization and challenges of force field methods in constructing the PES are discussed. It is expected that this review will assist researchers in selecting and applying different force field methods more effectively to promote in-depth understanding of catalytic reaction mechanisms and the efficient design of catalysts. Force field (fiction) ; Field (mathematics) ; Quantum ; Potential energy ; Potential field ; Energy (signal processing) ; Surface (topology) ; Potential energy surface Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Li-Ying Xu | Siqi Liu | Dandan Hu | Junhao Liu | Yuze Zhang | Ziqiang Li | Zhixi Su | Daxin Liang
Source: Gels (article, 2025-06-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors Understanding the relationships between design factors is crucial for the development of hydrogel supercapacitors, yet the relative importance and interdependencies of material properties and operating conditions remain unclear. This study employs interpretable machine learning to analyze the design factors that affect hydrogel supercapacitor performance, using 232 experimental samples from 41 recent studies. SHAP analysis was implemented to quantify parameter importance and reveal feature interactions among 16 key design parameters, including polymer types, electrolyte formulations, and operating conditions. Results show that synthetic vinyl polymers most strongly influence specific capacitance, while conductive polymers predominantly affect cycle stability. Ionic conductivity emerged as the most impactful parameter despite moderate feature importance, indicating complex nonlinear relationships. Critical interdependencies between polymer concentration and electrolyte formulation suggest that optimal design requires coordinated parameter selection rather than independent optimization. This interpretable framework provides quantitative insights into design factor hierarchies and parameter interdependencies, offering evidence-based guidelines for rational material selection in hydrogel supercapacitor development. Rational design ; Supercapacitor ; Interdependence ; Computer science ; Capacitance ; Materials science ; Electrolyte ; Biological system ; Biochemical engineering ; Nanotechnology ; Chemistry ; Engineering Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Yucong Gu | Kaiwen Wang | Zhengyu Zhang | Yi Yao | Zhi Jun Xin | Wenjun Cai | Lin Li
Source: npj Materials Degradation (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms Lightweight aluminum alloy is one of the widely used structural materials for various industries due to its low density, high strength-to-weight ratio, good corrosion resistance, and excellent recyclability. However, complex service conditions often result in material degradation due to simultaneous mechanical and corrosion attacks on the metal surfaces, such as tribocorrosion. This phenomenon represents a complex multiphysics challenge, wherein the tribocorrosion-induced material loss emerges as a function of varied environmental, mechanical, and electrochemical descriptors, each entailing distinct yet interlinked physical processes. The pursuit of simultaneous optimization across multiple material properties to enhance the overall tribocorrosion resistance is hampered by the inherent trade-offs between wear and corrosion resistance. Addressing this complexity, our study develops a novel methodology fusing machine-learning (ML) and genetic algorithm (GA)-based optimization techniques to tailor aluminum-based alloys for enhanced tribocorrosion resistance. Leveraging an experimentally validated multiphysics finite element analysis (FEA) model, we have used six key material parameters to model the tribocorrosion performance of Al alloys over a large property space. The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. Crucially, our high-throughput screening pinpoints corrosion current density and yield strength as two pivotal parameters influencing tribocorrosion behavior. Harnessing GA optimization alongside the ML model, we efficiently identify a suite of optimal material properties—encompassing both mechanical and electrochemical aspects—for aluminum alloys, resulting in superior tribocorrosion resistance. This selection is substantiated through validation against high-fidelity FEA simulation results. This data-driven framework holds promise for tailoring tribocorrosion-resistant materials beyond aluminum alloys, adaptable to a wide range of metals and service environments. Interfacing ; Tribocorrosion ; Multiphysics ; Computer science ; Machine learning ; Artificial intelligence ; Algorithm ; Software engineering ; Engineering ; Finite element method ; Computer hardware ; Physics ; Structural engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced materials and composites
Authors: Amir Karton
Source: Australian Journal of Chemistry (article, 2025-03-16)
Field: Materials Science — Materials Chemistry

Three decades of quantum science: how quantum chemistry transformed thermochemical database generation for benchmarking DFT and machine learning In celebration of the United Nations’ declaration of 2025 as the International Year of Quantum Science and Technology, marking 100 years since the development of quantum mechanics, this review highlights how accurate quantum mechanical calculations have transformed gas-phase thermochemistry. In particular, the developments of high-level composite ab initio methods over the past 30 years enable the calculations of thermochemical properties with confident chemical accuracy (i.e. with 95% confidence intervals ≤1 kcal mol−1) for molecules with up to 12 non-hydrogen atoms. Lower-level composite ab initio methods can be applied to molecules containing up to ~50 non-hydrogen atoms; however, they cannot achieve confident chemical accuracy in terms of 95% confidence intervals. Over the past three decades, hundreds of composite ab initio methods have been developed, covering different theoretical frameworks, levels of accuracy and computational costs. To guide users in selecting an appropriate composite ab initio method for a given system size and level of accuracy, we present a general approach for categorising the accuracy of these methods. This approach places composite ab initio methods on four rungs of Jacob’s Ladder. Lower rungs offer less accuracy but are applicable to larger systems, and higher rungs offer greater accuracy but are applicable to smaller systems. Each consecutive rung of this ladder represents an improvement in the treatment of the one-particle space, n-particle space, or both, leading toward the exact solution of the relativistic Schrödinger equation. The Jacob’s Ladder of composite ab initio methods can be considered as an extension to the Jacob’s Ladder of density functional theory (DFT), which leads from ‘Hartree Hell’ to the ‘Heaven’ of double-hybrid DFT methods. Quantum chemistry ; Chemistry ; Benchmarking ; Quantum chemical ; Green chemistry ; Biocatalysis ; Supramolecular chemistry ; Density functional theory ; Quantum ; Ab initio quantum chemistry methods ; Computational chemistry ; Theoretical chemistry ; Reaction mechanism ; Nanotechnology ; Catalysis ; Quantum mechanics ; Crystal structure ; Organic chemistry ; Molecule ; Physics ; Materials science Machine Learning in Materials Science ; Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Qiyuan Zhao | Y. L. Han | Duo Zhang | Jiaxu Wang | Peichen Zhong | Taoyong Cui | Bangchen Yin | Yirui Cao | Haojun Jia | Chenru Duan
Source: Advanced Science (article, 2025-07-13)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models Transition state (TS) search is crucial for illuminating chemical reaction mechanisms but remains the major bottleneck in automated discovery because of the high computational cost. Recently, machine learning interatomic potentials (MLIPs) and generative models have shown promise in accelerating TS search, but their comparative strengths and limitations remain unclear. In this study, the first systematic and rigorous benchmarking framework is established to evaluate the effectiveness of ML methods in TS search, enabling a standardized and application-relevant assessment of their performance. Using an end-to-end TS search workflow, seven representative MLIPs are benchmarked alongside React-OT, a state-of-the-art generative model. These results demonstrate that pre-trained foundation MLIPs frequently fall short in reliably localizing TSs without task-specific fine-tuning. Furthermore, traditional energy and force metrics alone do not reliably predict TS search success, underscoring the need for more tailored evaluation criteria. Notably, with the same graph neural network architecture, React-OT frequently outperforms its MLIP counterpart, highlighting the potential of generative approaches for TS discovery. This benchmark serves as a critical foundation for the development and evaluation of future ML methods in chemical reactions, offering guidance for improving their generalizability and reliability in reactive chemistry. Computer science ; Generalizability theory ; Benchmark (surveying) ; Benchmarking ; Machine learning ; Generative grammar ; Bottleneck ; Artificial intelligence ; Reliability (semiconductor) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Thalis H. B. da Silva | Théo Cavignac | Tiago F. T. Cerqueira | Hai‐Chen Wang | Miguel A. L. Marques
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated prediction of two-dimensional conventional superconductors We perform a large scale search for two-dimensional (2D) superconductors, by using electron-phonon calculations with density-functional perturbation theory combined with machine learning models. In total, we screened over 140 000 2D compounds from the Alexandria database. Our high-throughput approach revealed a multitude of 2D superconductors with diverse chemistries and crystal structures. Moreover, we find that 2D materials generally exhibit stronger electron-phonon coupling than their 3D counterparts, although their average phonon frequencies are lower, leading to an overall lower <i>T</i><sub>c</sub>. In spite of this, we discovered several out-of-distribution materials with relatively high-<i>T</i><sub>c</sub>. In total, 105 2D systems were found with <i>T</i><sub>c</sub> > 5 K. Some interesting compounds, such as CuH<sub>2</sub>, NbN, and V<sub>2</sub>NS<sub>2</sub>, demonstrate high <i>T</i><sub>c</sub> values and good thermodynamic stability, making them strong candidates for experimental synthesis and practical applications. Our findings highlight the critical role of computational databases and machine learning in accelerating the discovery of novel superconductors. Superconductivity ; Perturbation theory (quantum mechanics) ; Statistical physics ; Scale (ratio) ; Density functional theory ; Perturbation (astronomy) ; Condensed matter physics ; Phonon ; Physics ; Computer science ; Artificial intelligence ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Superconductivity in MgB2 and Alloys
Authors: Julian Götz | E. G. Richards | Iain A. Stepek | Yu Takahashi | Yi‐Lin Huang | Louis Bertschi | Bertran Rubi | Jeffrey W. Bode
Source: Science Advances (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations Efficient drug discovery depends on reliable synthetic access to candidate molecules, but emerging machine learning approaches to predicting reaction outcomes are hampered by poor availability of high-quality data. Here, we demonstrate an on-demand synthesis platform based on a three-component reaction that delivers drug-like molecules. Miniaturization and automation enable the execution and analysis of 50,000 distinct reactions on a 3-microliter scale from 193 different substrates, producing the largest public reaction outcome dataset. With machine learning, we accurately predict the result of unknown reactions and analyze the impact of dataset size on model training, both enabling accurate outcome predictions even for unseen reactants and providing a sufficiently large dataset to critically evaluate emerging machine learning approaches to chemical reactivity. Computer science ; Component (thermodynamics) ; Drug discovery ; Machine learning ; Automation ; Scale (ratio) ; Miniaturization ; Outcome (game theory) ; Artificial intelligence ; Virtual screening ; Data mining ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Jia-Lan Chen | Xin‐Rui Qi | Jinze Zhu | Jin Li | Xuechun Jiang | Wei‐Xue Li | Jin‐Xun Liu
Source: Journal of Chemical Theory and Computation (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Grand-Canonical Equivariant Neural Potentials for Electrochemical Interfaces Electrochemical reactions under constant potential underpin critical processes in energy storage, catalysis, and corrosion but remain challenging to model owing to the voltage insensitivity of conventional machine learning potentials. The lack of a unified framework incorporating grand-canonical constraints into machine-learned models fundamentally limits accurate, scalable simulations of potential-dependent interfacial phenomena. Here, we present a constant-potential, E(3)-equivariant message-passing neural network (CPMPNN) that integrates grand-canonical electronic structure principles with a global excess-charge parameter that is dynamically redistributed via a multihead attention mechanism. The atomic geometry is encoded through a graph neural network that preserves the full symmetry of the Euclidean group in three dimensions (E(3))─including translations, rotations, and reflections. Benchmarking against the grand-canonical DFT confirms that the CPMPNN retains first-principles accuracy while achieving a three-orders-of-magnitude computational speedup. Applied to key electrocatalytic processes─CO dimerization in CO<sub>2</sub> reduction and the Volmer step in hydrogen evolution on Cu(100)─CPMPNN captures how the applied potential modulates the reaction thermodynamics, charge distribution, and transition-state structures, providing mechanistic insight into potential-dependent kinetics. By bridging first-principle accuracy with molecular dynamics scalability, CPMPNN provides a transferable framework for operando modeling of electrified interfaces, enabling new mechanistic insights into potential-controlled electrocatalysis. Scalability ; Artificial neural network ; Equivariant map ; Key (lock) ; Electrochemistry ; Benchmarking ; Potential energy ; Bridging (networking) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Thermography and Photoacoustic Techniques
Authors: Benhour Amirian | Abhijit Brahme | Ricardo A. Lebensohn | Kaan Inal
Source: International Journal of Plasticity (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials Materials science ; Fourier transform ; Crystallite ; Algorithm ; Fast Fourier transform ; Strain (injury) ; Composite material ; Mechanical engineering ; Computer science ; Mathematical analysis ; Mathematics ; Engineering ; Metallurgy Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metal Forming Simulation Techniques
Authors: Chenyang Cao | Liyi Bai | Shuo Cao | Ye Su | Yanzhou Wang | Zheyong Fan | Ping Qian
Source: The Journal of Physical Chemistry C (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Structural and Transport Properties of LiTFSI/G<sub>3</sub> Electrolyte with Machine-Learned Molecular Dynamics The lithium bis(trifluoromethylsulfonyl)azanide-triglyme (LiTFSI/G3) electrolyte plays a critical role in the performance of lithium-ion batteries. However, its solvation structure and transport properties at the atomic scale remain incompletely understood. In this study, we develop an efficient and accurate neuroevolution potential (NEP) model by integrating bootstrap and active learning strategies. Using machine-learned NEP-driven molecular dynamics simulations, we explore the structural and diffusion properties of LiTFSI/G3 across a wide range of the solute-to-solvent ratios, systematically analyzing electrolyte density, ion coordination, viscosity, and lithium self-diffusion. The computed densities show excellent agreement with experimental data, and pair correlation analysis reveals significant interactions between lithium ions and surrounding oxygen atoms, which strongly impact Li+ mobility. Viscosity and diffusion calculations further demonstrate that increasing LiTFSI concentration enhances Li–O interactions, resulting in higher viscosity and reduced lithium diffusion. Additionally, machine learning-based path integral molecular dynamics (PIMD) simulations confirm the small impact of quantum effects on Li+ transport. The electrolyte-specific protocol developed in this work provides a systematic framework for constructing high-fidelity machine learning potentials for complex systems. Electrolyte ; Molecular dynamics ; Dynamics (music) ; Computer science ; Materials science ; Chemistry ; Chemical physics ; Computational chemistry ; Physics ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Alireza Asadi | Erfan Khaksar | Setareh Hosseinpoor | Roshanak Abbasi | Yasamin Ghahramani
Source: Advances in Applied NanoBio-Technologies (article, 2025-06-30)
Field: Chemistry — Organic Chemistry

Aluminum Nanoparticles, a New Approach in Sustainable Chemistry and Usage in Medicine Recent advancements in material science have leveraged artificial intelligence (AI) to significantly enhance the design and synthesis of aluminum nanoparticles (Al NPs), enabling sub-nanometer control over structural and functional properties. By integrating machine learning (ML) algorithms with computational modeling, researchers have optimized nanoparticle size, morphology, and surface characteristics to support applications such as energy storage, catalytic reactions, and lightweight composites. AI-driven methods such as active learning and generative design streamline the discovery of novel synthesis routes, reducing trial-and-error and enabling scalable production. These nanoparticles (NPs) demonstrate a 15-30% increase in thermal stability, up to 2x enhancement in surface reactivity, and improved energy density compared to conventional counterparts, making them essential for sustainable technologies, aerospace engineering, and next-generation batteries. This innovation underscores the synergy between AI and nanotechnology, enabling rapid, cost-effective development of advanced materials with tailored functionalities. Nanoparticle ; Business ; Nanotechnology ; Chemistry ; Materials science Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering
Authors: Rideb Chakraborty | Suriya Prakaash Kannan | Naureen Afrose | Damodharan Narayanasamy
Source: International Journal of Biological Macromolecules (review, 2025-07-28)
Field: Materials Science — Biomaterials

Tumor microenvironment expressed enzymes (Matrix metalloproteinases, cathepsins, urokinase-type plasminogen activator) triggered polymersomes for liquid biopsy and cancer diagnostics: A review Matrix metalloproteinase ; Plasminogen activator ; Urokinase ; Tumor microenvironment ; Cancer research ; Matrilysin ; Chemistry ; Liquid biopsy ; Cancer ; Biology ; Biochemistry ; Medicine ; Tumor cells ; Internal medicine ; Endocrinology Nanoparticle-Based Drug Delivery ; Protease and Inhibitor Mechanisms ; Molecular Biology Techniques and Applications