jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: I. Fiduccia | D. Ricci | C. Rizzo | A. Pace | I. Pibiri
Source: Coordination Chemistry Reviews (article, 2025-12-24)
Field: Chemistry — Physical and Theoretical Chemistry

Non-canonical σ-hole interactions: halogen, chalcogen, and pnictogen bonds in biomolecular structure and drug design σ-Hole interactions, including halogen, chalcogen, and pnictogen bonds, are a versatile and directional class of noncovalent forces that expand the molecular design space in chemical biology and medicinal chemistry. Their emerging roles in biological systems and medicinal chemistry are now becoming increasingly intriguing, opening new avenues for exploration and this review focuses on the role of these interactions in biological system. Halogen bonds contribute measurably to binding in desolvated protein pockets. Chalcogen bonds stabilize protein structures, control conformation, enable catalysis, and facilitate redox-responsive drug delivery. Pnictogen bonds (PnB) combine electrostatic and orbital components, supporting supramolecular assembly, anion recognition, and selective catalysis, though careful attention to toxicity and ADME properties is required. Early consideration of developability limitations, such as lipophilicity, metabolism, and toxicity, is critical. Increasing attention to σ-hole interactions, combined with polarizable force fields and machine learning structural analysis allows these interactions to be used as predictive and reproducible tools in molecular design. • Comprehensive overview of halogen, chalcogen and pnictogen σ-hole interactions in biology. • Highlights σ-hole roles in drug design beyond classical hydrogen bond and π-bonding. • Integrates structural surveys with computational and medicinal insights. Pnictogen ; Supramolecular chemistry ; Chalcogen ; Hydrogen bond ; Polarizability ; Chemical space ; Biomolecule ; Drug discovery Crystallography and molecular interactions ; Crystal structures of chemical compounds ; Supramolecular Chemistry and Complexes
Authors: Seong-Min Kim | Joshua Schrier | Yousung Jung
Source: Angewandte Chemie (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design. Crystal structure prediction ; Crystal (programming language) ; Chemistry ; Crystal structure ; Materials science ; Crystallography ; Computer science ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Qi Huang | Yedi Li | Lei Zhu | Wenjie Yu
Source: Journal of Chemical Information and Modeling (article, 2025-03-22)
Field: Materials Science — Materials Chemistry

Hierarchical Deep Potential with Structure Constraints for Efficient Coarse-Grained Modeling Coarse-grained molecular dynamics is a powerful approach for simulating large-scale systems by reducing the number of degrees of freedom. Nonetheless, the development of accurate coarse-grained force fields remains challenging, particularly for complex systems, such as polymers. In this study, we introduce a novel framework, hierarchical deep potential with structure constraints (HDP-SC), designed to construct coarse-grained force fields for polymer materials. Our methodology integrates a prior energy term obtained through direct Boltzmann inversion with a deep neural network potential, which is trained using hierarchical bead environment descriptors. This framework facilitates the reproduction of structural distributions and the potential of mean force, thus enhancing the accuracy and efficiency of the coarse-grained model. We validate our approach using polystyrene systems, demonstrating that the HDP-SC model not only successfully reproduces the structural properties of these systems but also remains applicable at larger scales. Our findings underscore the promise of machine learning-based techniques in advancing the development of coarse-grained force fields for polymer materials. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Injection Molding Process and Properties ; X-ray Diffraction in Crystallography
Authors: Fabrizio Mastrolorito | Fulvio Ciriaco | Orazio Nicolotti | Francesca Grisoni
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhancing deep chemical reaction prediction with advanced chirality and fragment representation This work focuses on organic reaction prediction with deep learning, with the recently introduced fragSMILES representation - which encodes molecular substructures and chirality, enabling compact and expressive molecular representation in a textual form. In a systematic comparison with well-established molecular notations - simplified molecular input line entry system (SMILES), self-referencing embedded strings (SELFIES), sequential attachment-based fragment embedding (SAFE) and tree-based SMILES (t-SMILES) - fragSMILES achieved the highest performance across forward- and retro-synthesis prediction, with superior recognition of stereochemical reaction information. Moreover, fragSMILES enhances the capacity to capture stereochemical complexity - a key challenge in synthesis planning. Our results demonstrate that chirality-aware and fragment-level representations can advance current computer-assisted synthesis planning efforts. Fragment (logic) ; Representation (politics) ; Chirality (physics) ; Chemistry ; Computational chemistry ; Artificial intelligence ; Computer science ; Physics ; Algorithm ; Nuclear physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yue Wang | Anqi Liu | Lin Tan | Jinyu Sun | Hailiang Zhang | Tao Xie | Zhimin Zhang | LU Hong-mei
Source: Analytica Chimica Acta (article, 2025-08-09)
Field: Chemistry — Analytical Chemistry

A pre-training enhanced deep learning framework for robust sparse unmixing in chemical imaging Chemistry ; Artificial intelligence ; Chemical imaging ; Hyperspectral imaging Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Wenjie Zhang | Weitang Li | Zhigang Shuai
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An active learning force field for the thermal transport properties of organometallic complex crystals The accurate prediction of lattice thermal conductivity in organometallic thermoelectric materials is crucial for advancing energy conversion technologies. Methods based on molecular dynamics simulations can solve this problem well, but require force fields with sufficiently high accuracy. Due to the complexity of chemical bonding in organometallic complex materials, the development of force fields with high predictivity has been a long standing challenge, particularly when thermal transport is concerned which requires even greater accuracy. In recent years, the rapid advancement of machine learning force fields has offered substantial potential for addressing these issues. However, there remain challenges for materials with large organometallic complexes in one unit cell and both inter- and intra-molecular interactions. In this work, we employ an active learning approach combined with deep neural networks to develop a force field taking copper phthalocyanine as an example. The model utilizes a local environment descriptor for representation without explicitly characterizing the metal-organic coordination. The nonlinear mapping capabilities of deep neural networks enable the model to effectively capture higher-order many-body interactions. Furthermore, we utilized the Green-Kubo method to calculate the thermal conductivity of copper phthalocyanine, revealing a value of 0.49 W m<sup>-1</sup> K<sup>-1</sup> at 300 K, consistent with experimental findings (0.39 W m<sup>-1</sup> K<sup>-1</sup>). This result significantly surpasses previous work with classical force fields. This work represents a significant advancement in demonstrating that machine-learning force fields can effectively characterize interactions in metal-organic complex systems and can significantly advance the development and discovery of organometallic thermoelectric materials. Molecular dynamics ; Thermoelectric materials ; Force field (fiction) ; Thermal conductivity ; Thermal ; Thermoelectric effect ; Lattice (music) ; Materials science ; Energy transport ; Chemical physics ; Field (mathematics) ; Organometallic chemistry ; Nanotechnology ; Statistical physics ; Crystal structure ; Engineering physics ; Computational chemistry ; Chemistry ; Thermodynamics ; Computer science ; Physics ; Crystallography ; Mathematics ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Conor Waldt (20833196) | Rajeev Kumar (195329) | David Hibbitts (2176140)
Source: Figshare (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Understanding AuPdAlloy Nanoparticle Structure underVacuum Using DFT and Monte Carlo Methods AuPd is a miscible metal alloy that is often used in catalysis. Supported AuPd catalysts, at high Au/Pd ratios, form single-atom alloys (SAAs) that have been shown to enhance rates and/or selectivities for many catalytic reactions, including (de)hydrogenations, hydrogenolysis, and C–C and C–O coupling reactions. While many computational studies have examined the stability of AuPd structures (the arrangement of atoms within the miscible alloy), most focused on generic alloys rather than SAAs and those that have closely investigated SAAs focused on single crystal surfaces. In this work, we use density functional theory (DFT) to calculate exchange energies (swapping an Au atom with a Pd atom) in a 201-atom truncated octahedral nanoparticle model with a focus on particles with high Au/Pd ratios. We calculate these exchange energies as a function of Pd location within the nanoparticle, the number of Pd atoms neighboring and near those exchange sites, and the total Pd content in the nanoparticle. These DFT-calculated exchange energies are also used to inform simple physics-based models (in contrast to cluster expansion or neural network models) that show good agreement with DFT-calculated values with relatively few regressed parameters. These models are then implemented into Monte Carlo (MC) simulations to predict the nanoparticle structure as a function of composition and temperature. The results show that Pd prefers to be in the subsurface of nanoparticles and that Pd prefers to be isolated from itself within Au. Both observations agree well with prior experimental and computational studies of single-crystal systems. We also show that the overall composition of the nanoparticle influences exchange energies by changing the electronic properties (e.g., Fermi level) of the system, which is relevant as Pd has one fewer valence electron than Au. MC simulations show that, in a vacuum, Pd begins to populate the surface of these ∼2 nm nanoparticles at around 20 mol % Pd (at 298 K) and that the number of Pd surface monomers, desired for SAA applications, goes through a maximum near 40 mol % Pd. As the temperature increases, Pd is more prevalent at the surface, but the influence of temperature is relatively muted. While AuPd structures are known to change in the presence of reactive gases (e.g., CO or O<sub>2</sub>), these studies characterize the baseline thermodynamic arrangements that can be used to understand surface restructuring during catalyst characterization and reaction studies. Nanoparticle ; Monte Carlo method ; Alloy ; Cluster (spacecraft) ; Atom (system on chip) ; Density functional theory ; Crystal structure ; Octahedron ; Particle (ecology) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Vishikh Athavale | Nikita Fedik | William Colglazier | Anders M. N. Niklasson | Maksim Kulichenko | Sergei Tretiak
Source: ArXiv.org (preprint, 2025-05-30)
Field: Chemistry — Spectroscopy

PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock (TDHF) level of theory in the PySEQM software. Built on PyTorch, this implementation leverages GPU acceleration to significantly speed up molecular property calculations. Benchmark tests demonstrate that our approach can compute excited states for molecules with nearly a thousand atoms in under a minute. Additionally, the implementation also includes a machine learning interface to enable parameters re-optimization and neural network training for future machine learning applications for excited state dynamics. Excited state ; Benchmark (surveying) ; Artificial neural network ; State (computer science) ; Interface (matter) ; Quantum ; Quantum chemical Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Laser-Matter Interactions and Applications
Authors: Zhen Chang | Feng Li | Hongtao Xue | Yanhong Yang | Junqiang Ren | Fuling Tang | Xuefeng Lu | Junchen Li
Source: Journal of Chemical Information and Modeling (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al–Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomic potentials. In this study, the deep potential (DP) approach, aided by high-throughput first-principles calculations, was utilized to develop an Al-Er interatomic potential specifically for MD simulations. Systematic comparisons between the physical properties (e.g., energy-volume curves, melting point, elastic constants) predicted by the DP model and those obtained from density functional theory (DFT) demonstrated that the developed DP model for Al-Er alloys possesses reliable predictive capabilities while retaining DFT-level accuracy. Our findings confirm that Al<sub>3</sub>Er, Al<sub>2</sub>Er, and AlEr<sub>2</sub> exhibit mechanical stability. The calculated melting point of Al<sub>3</sub>Er (1398 K) shows a 57 K deviation from the experimental value (1341 K). With the Er content increasing from 0.01% to 0.064 at.% in Al-Er alloys, the grain boundary (GB) concentration of Er atoms increases from 0.03 to 0.07% following Monte Carlo (MC) annealing optimization. The Al-0.05 at.%Er alloy exhibits the highest yield strength, with an increase of 0.128 GPa (6.1%) compared to pure Al. For Al-0.05 at.%Er alloys with varying average grain sizes, the GB concentration of Er atoms increases by about 1.4-1.6 times after MC annealing compared to the average Er content. Additionally, the Al-Er alloys reach the peak yield strength of 2.214 GPa when the average grain size is 11.72 nm. The GB segregation of Er atoms lowers the system energy and thus enhances stability. Notable changes in the segregation behavior of Er atoms were observed with increasing Er concentration and decreasing grain size. These results would facilitate the understanding of the mechanical characteristics of Al-Er alloys and offer a theoretical basis for developing advanced nanopolycrystalline Al-Er alloys. Grain boundary ; Molecular dynamics ; Materials science ; Grain size ; Deformation (meteorology) ; Dynamics (music) ; Severe plastic deformation ; Chemical physics ; Thermodynamics ; Metallurgy ; Composite material ; Chemistry ; Microstructure ; Computational chemistry ; Physics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Sumner B. Harris (20634071) | Patrick T. Gemperline (20968706) | Christopher M. Rouleau (1373520) | Rama K. Vasudevan (1612174) | Ryan B. Comes (1700242)
Source: Figshare (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Deep Learningwith Reflection High-Energy ElectronDiffraction Images to Predict Cation Ratio in Sr<sub>2<i>x</i></sub>Ti<sub>2(1–<i>x</i>)</sub>O<sub>3</sub> ThinFilms Machine learning (ML) with in-situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synthesis conditions and material properties. In this study, we demonstrate the application of deep learning to predict the stoichiometry of Sr<sub>2<i>x</i></sub>Ti<sub>2(1–<i>x</i>)</sub>O<sub>3</sub> thin films using reflection high-energy electron diffraction images acquired during pulsed laser deposition. A gated convolutional neural network trained for regression of the Sr atomic fraction achieved accurate predictions with a small dataset of 31 samples. Explainable AI techniques revealed a previously unknown correlation between diffraction streak features and cation stoichiometry in Sr<sub>2<i>x</i></sub>Ti<sub>2(1–<i>x</i>)</sub>O<sub>3</sub> thin films. Our results demonstrate how ML can be used to transform a ubiquitous <i>in-situ</i> diagnostic tool, that is usually limited to qualitative assessments, into a quantitative surrogate measurement of continuously valued thin film properties. Such methods are critically needed to enable real-time control, autonomous workflows, and accelerate traditional synthesis approaches. Thin film ; Reflection (computer programming) ; Diffraction ; Convolutional neural network ; Stoichiometry ; Deep learning ; Electron diffraction ; Streak Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Xi Xu | Rajibul Islam | Ghulam Hussain | Yuting Huang | Xiaoguang Li | Pavlo O. Dral | Arif Ullah | Ming Yang
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Quantum-inspired Chemical Rule for Discovering Topological Materials Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, resource-intensive nature of experimental synthesis. Recent machine-learning approaches, such as the heuristic topogivity rule, offer data-driven alternatives by quantifying each element's intrinsic tendency toward topological behavior. Here, we develop a quantum-classical hybrid artificial neural network (QANN) that extends this rule into a quantum-inspired formulation. Within this framework, the QANN maps compositional descriptors to quantum probability amplitudes, naturally introducing pairwise inter-element correlations inaccessible to classical heuristics. The physical validity of these correlations is substantiated by constructing an equivalent complex-valued neural network (CVNN), confirming both the consistency and interpretability of the formulation. Retaining the simplicity of chemical reasoning while embedding quantum-native features, our quantum-inspired rule enables efficient and generalizable topological classification. High-throughput screening combined with first-principles (DFT) validation reveals five previously unreported topological compounds, demonstrating the enhanced predictive power and physical insight afforded by quantum-inspired heuristics. Interpretability ; Heuristic ; Consistency (knowledge bases) ; Pairwise comparison ; Topology (electrical circuits) ; Artificial neural network ; Simplicity ; Embedding Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Topological Materials and Phenomena
Authors: Paula Marielle Ababao | Ian B. Benitez
Source:  (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

AI-Driven Computational Materials Science for Advanced Energy Materials Development The integration of artificial intelligence (AI) into computational materials science (CMS) has introduced powerful approaches for accelerating the discovery and optimization of advanced energy materials. As energy demands shift toward renewable systems, the development of efficient materials for batteries, fuel cells, and electrocatalysts becomes increasingly critical. This paper systematically reviews recent AI methodologies applied within CMS, particularly those leveraging density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (KMC) simulations. Emphasis is placed on the use of machine learning (ML) models, including supervised learning, deep learning, and hybrid strategies for property prediction, structure optimization, and inverse design. The review categorizes current applications across key energy technologies and discusses how AI is reshaping material screening and development pipelines. It concludes with an outlook on future directions, highlighting the need for standardized datasets, interpretable models, and physics-informed frameworks to improve predictive accuracy and facilitate AI adoption in practical materials research. Renewable energy ; Key (lock) ; Development (topology) ; Energy (signal processing) ; Applications of artificial intelligence ; Materials informatics ; Uncertainty quantification Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Inorganic Chemistry and Materials