jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julian Konrad | Robert H. Meißner
Source: Journal of Chemical Information and Modeling (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation Accurately modeling chemical reactions in molecular dynamics simulations requires detailed pre- and postreaction templates, often created through labor-intensive manual workflows. This work introduces a Python-based algorithm that automates the generation of reaction templates for the LAMMPS REACTION package, leveraging graph-theoretical principles and subgraph isomorphism techniques. By representing molecular systems as mathematical graphs, the method enables the automated identification of conserved molecular domains, reaction sites, and atom mappings, significantly reducing manual effort. The algorithm was validated on three case studies: poly addition, poly condensation, and chain polymerization, demonstrating its ability to map conserved domains, identify reaction-initiating atoms, and resolve challenges such as symmetric reactants and indistinguishable atoms. Additionally, the generated templates were optimized for computational efficiency by retaining only essential reactive domains, ensuring scalability and consistency in high-throughput workflows for computational chemistry, materials science, and machine learning applications. Future work will focus on extending the method to mixed organic-inorganic systems, incorporating adaptive scoring mechanisms, and integrating quantum mechanical calculations to enhance its applicability. Computer science ; Molecular dynamics ; Graph ; Biological system ; Chemistry ; Theoretical computer science ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anastasia A. Danshina | Ivan Zlobin | С. Е. Соловьева | Nikolai Rekut | Yulia V. Nelyubina
Source: Crystal Growth & Design (article, 2025-05-29)
Field: Chemistry — Spectroscopy

Ammonium Organosulfonates as Machine Learning-Driven “Crystallization Cocktails” for the Structure Determination of Liquid Molecules A concept of “crystallization cocktails” for crystal structure determination of small (liquid) molecules is introduced, offering a simple one-step procedure of one-pot mixing commercial sulfonic acids (such as p-toluenesulfonic and 1,5-naphtalenedisulfonic) and geometrically diverse amines (such as p-phenylenediamine, 4,4′-oxidianiline, and 4,4′-diamino-3,3′-dichlorodiphenylmethane) in different combinations to cocrystallize with a chosen liquid compound by simple solvent evaporation. These “crystallization cocktails” may adapt to different guests owing to “labile” intermolecular interactions between sulfonate and ammonium ions of diverse geometries and flexibility, as demonstrated by a successful cocrystallization of three model phenol derivatives (2,4-dimethyl-, 2-isopropyl-, and 2-ethylphenol); one of them being characterized by X-ray diffraction for the first time. The guest-to-host ratio in the obtained cocrystals, three of which featured the same ammonium organosulfonate but different phenol guests, was found to depend on the size of the individual components of the “crystallization cocktails”. To get insight into the preferable formation of a cocrystal by some of these “cocktails”─as common (descriptive) crystallographic tools failed to provide a definitive answer─a machine learning (ML) algorithm was developed, which allowed limiting the list of their components to a few most suitable ones for a successful cocrystallization with liquid phenols. “Crystallization cocktails” that can be “mixed to order”─and boosted by the ML─pave the way toward routine structure determination of liquids or other poorly crystallizing substances by X-ray diffraction, streamlining the search for new chemical compounds and identification of “old” ones in natural products, waste and natural waters, and living organisms. Crystallization ; Molecule ; Ammonium ; Chemistry ; Chemical engineering ; Chromatography ; Materials science ; Crystallography ; Organic chemistry ; Engineering Molecular spectroscopy and chirality ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Rinkumoni Chaliha | Manish Kumar Kothakonda | Cheng‐Wei Lee | Jeffrey Law | Qian Yang | Svilen Bobev | Prashun Gorai
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Charting the chemical space of Zintl phases with graph neural networks and bonding insights We combine graph neural networks and an upper bound energy minimization framework to explore &gt;90 000 candidates, discovering 1810 stable Zintl phases with high precision and revealing the key role of ionic bonding in their stability. Chemical space ; Graph ; Space (punctuation) ; Chemical bond ; Computer science ; Chemistry ; Cognitive science ; Theoretical computer science ; Artificial intelligence ; Materials science ; Psychology ; Biochemistry ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Takuya Taniguchi | Ryo Fukasawa
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crystal structure prediction of organic molecules by machine learning-based lattice sampling and structure relaxation An ML-based workflow doubles crystal structure prediction success by narrowing the search space for organic molecules. Crystal structure ; Crystal structure prediction ; Lattice (music) ; Organic molecules ; Relaxation (psychology) ; Sampling (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Samantha M. McIntyre | Anna L. Garden
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computational modelling of nanoparticle catalysis In recent years, computational methods have complemented a range of experimental techniques to enrich our understanding of chemical systems. This is particularly true in catalysis, as it is possible to gain atomic level insight into the active sites, adsorbate interactions, and the potential mechanisms occurring. Nanoparticles or nanoscale materials have become increasingly popular as catalysts in recent years, requiring additional computational considerations. In this review, we present an overview of existing and emerging computational methods in nanocatalysis and the unique catalytic behaviour that is revealed by these methods. We first discuss numerical tools used to calculate structures, energies and rates of catalytic reactions as well as different approaches to representing active sites of nanoparticle catalysts. We then focus on alloy and supported nanoparticle catalysts and explore how computational methods can reveal the more complex behaviours of these catalytic systems. Finally, we highlight some of the additional challenges in moving towards realistic systems, such as the dynamic nature of catalysts, the role of solvent, ensemble effects, the distribution of active sites and the influence of defects, which also have the potential to change the observed catalytic properties. We also present a discussion on emerging machine learning approaches as applied to nanocatalysis. Overall, this article aims to highlight the various methods available to model nanocatalysis and the catalytic insights that can be gained from a computational approach, using a range of examples from recent literature. Nanoparticle ; Catalysis ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Muhammad Qasim | Sidra Manzoor | Muhammad Nabeel | Raja Waqas | Sabir Hussain | Collin G. Joseph | Jonathan Suazo-Hernández
Source: Preprints.org (preprint, 2025-10-08)
Field: Materials Science — Materials Chemistry

Harnessing High-Valent Metals for Catalytic Oxidation: Next-Gen Strategies in Water Remediation and Circular Chemistry High-valent metal species such as Fe(IV), Mn(V), Co(IV), and Cu(III) based advanced oxidation processes (AOPs) have emerged as sustainable technologies for water remediation. These processes offer high selectivity, electron transfer efficiency, and compatibility with circular chemistry principles compared to conventional radical-driven systems. This comprehensive review discusses recent advances in the synthesis, stabilization, and catalytic applications of high-valent metals in aqueous environments. Here the focus is on their role not only as conventional oxidants, but also as mechanistic mediators of redox cycles in next-generation AOPs. In this review, the formation mechanisms of these species in various oxidant systems (peroxymonosulfate, peracetic acid, periodate) are critically evaluated, highlighting the significance of ligand design, supramolecular confinement, and single-atom engineering in enhancing their stability. The integration of high-valent metal AOPs into photocatalysis, sono-catalysis, and electrochemical regeneration is examined through a newly proposed classification framework, emphasizing their potential for energy-efficient hybrid systems. Furthermore, we address the critical yet underexplored domain of environmental fate, detailing the post-oxidation transformation pathways of high-valent species and their potential for metal recovery and nutrient valorization. By bridging molecular-level reaction mechanisms with system-scale sustainability metrics, this review positions high-valent metal AOPs as a promising approach for zero-waste water treatment within circular economies. Future frontiers, including bioinspired catalyst design, machine learning-guided optimization, and closed-loop reactor engineering, will bridge the gap between laboratory research to real-world applications. Environmental remediation ; Catalysis ; Aqueous medium ; Sustainability ; Redox ; Water treatment ; Metal Catalytic Processes in Materials Science ; Environmental remediation with nanomaterials ; Radioactive element chemistry and processing
Authors: Felix Döppel | Tim Kircher | Martin Votsmeier
Source: ChemRxiv (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Formation–Consumption Neural Networks for Efficient Chemical Kinetics Modeling Artificial neural network ; Constraint (computer-aided design) ; Chemical kinetics ; Representation (politics) ; Process (computing) ; Simple (philosophy) ; Scaling ; Path (computing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Model Reduction and Neural Networks
Authors: Guanghao Zhang | Zicheng Wang | Caihua Shi | Ye Han
Source: Computational Materials Science (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics Phonon ; Interatomic potential ; Thermal conductivity ; Molecular dynamics ; Thermal ; Boltzmann equation ; Monolayer ; Density functional theory Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Takumi Sato | Ikki Yasuda | Noriyoshi Arai | Kenji Yasuoka
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Surfaces, Coatings and Films

Molecular Dynamics Investigation of Static and Dynamic Interfacial Properties in Ice-Polymer Premelting Layers Premelting at the ice-polymer interfaces, in which a quasi-liquid layer (QLL) forms below the melting point, is strongly influenced by polymer surface chemistry; however, the molecular-scale mechanisms underlying these effects remain poorly understood. This study employs large-scale molecular dynamics simulations combined with machine learning-assisted analysis to elucidate how polymer type (hydrophilic vs hydrophobic) modulates interfacial premelting. Our simulations reveal that hydrophilic and hydrophobic polymer surfaces have distinct effects on the QLL thickness, interfacial water structure, and diffusivity. Specifically, a hydrophilic polymer interface promotes a thicker QLL with more ordered interfacial water and lower diffusivity, whereas a hydrophobic interface induces a thinner QLL with a less ordered interfacial water structure and higher diffusivity. These results advance the understanding of polymer-mediated interfacial melting phenomena and offer guidance for designing anti-icing and low-friction materials. Premelting ; Molecular dynamics ; Polymer ; Surface (topology) ; Dynamics (music) ; Layer (electronics) ; Water model Surface Modification and Superhydrophobicity ; Material Dynamics and Properties ; nanoparticles nucleation surface interactions
Authors: Ayodeji O. Adegun | Thompson A. Akinnifesi | M. Adejumo | Adejumoke Moriliat Hashimi | Gregory O. Adewuyi | Kayode Adeniyi Aseperi | James Barker
Source:  (article, 2025-05-04)
Field: Materials Science — Materials Chemistry

Integrating AI into Environmental Chemistry: Predicting and Preventing Toxicity in Chemical Products This review critically examines the current state of AI in environmental chemistry, highlighting both its potential and limitations in predicting and preventing toxicity in chemical products. Integrating Artificial Intelligence (AI) into environmental chemistry for predicting and preventing toxicity in chemical products is an emerging field that promises to revolutionize toxicological assessments and sustainable chemical design. AI techniques, particularly machine learning (ML) and deep learning (DL), offer the potential to predict the toxicity of chemical substances rapidly, reducing reliance on traditional experimental methods that are often time-consuming, expensive, and ethically problematic. By utilizing large datasets on chemical properties, molecular structures, and biological effects, AI models can forecast the environmental and health impacts of chemicals at an early stage, enabling more efficient risk assessments. However, there are several critical challenges and limitations to consider. The accuracy of AI predictions is dependent on the availability of high-quality, comprehensive data, which is often lacking, especially for new or untested chemicals. Furthermore, the interpretability of AI models remains a significant issue, as many models function as "black boxes," making it difficult to understand the rationale behind their predictions. This lack of transparency may hinder trust in AI-driven decision-making. Additionally, AI tools may lead to biases if the data is incomplete. Despite these challenges, AI presents opportunities for the design of greener chemicals by optimizing molecular structures to reduce environmental harm and enhance biodegradability. However, integrating AI into environmental chemistry requires careful consideration of ethical, legal, and regulatory frameworks to ensure the responsible use of technology. Interpretability ; Chemical toxicity ; Transparency (behavior) ; Function (biology) ; Harm ; Chemical products ; Risk assessment Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Johannes Karwounopoulos | Jasper De Landsheere | Leonard Galustian | Esther Heid
Source: ChemRxiv (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product graphs were shown to provide promising accuracies for reaction property prediction. They only rely on the change in molecular graphs as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly, only given the reaction graph. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for all investigated datasets. Graph ; On the fly ; Transition (genetics) ; Computer science ; Econometrics ; Mathematics ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Johannes Karwounopoulos | Jasper De Landsheere | Leonard Galustian | Tobias Jechtl | Esther Heid
Source: ChemRxiv (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product graphs were shown to provide promising accuracies for reaction property prediction. They only rely on the change in molecular graphs as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly, only given the reaction graph. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for all investigated datasets. Graph ; On the fly ; Transition (genetics) ; Computer science ; Econometrics ; Mathematics ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Kai Ren | Chao Lv | Yang Wang | Chao Zhang | Dongqi Wang
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep learning interatomic potential for boron phosphide: accurate prediction of mechanical and thermal properties Boron phosphide (BP) is a promising high temperature thermoelectric material with good thermal stability and chemical inertness. Recently, interatomic potentials based on machine learning methods with neural networks have attracted a lot of attention due to their high accuracy and efficiency in atomistic simulations. In this work, a deep potential (DP) of BP was trained using machine learning (ML) methods. The structure and properties of BP were investigated using the trained DP. It was found that the DP simulation accurately reproduces the radial and angular distribution functions of BP, and that the lattice constants and density are in good agreement with the first-principles calculations and experimental results. It accurately reproduces the key physical properties of boron phosphide, including radial and angular distribution functions, lattice constants, density, structural properties, mechanical properties (such as elastic constants and hardness), fracture toughness, and thermal properties (such as entropy, enthalpy, free energy, heat capacity, thermal conductivity, and phonon spectrum). These results show that the trained BP deep learning potential can accurately describe BP materials. Interatomic potential ; Boron ; Phonon ; Thermal ; Lattice (music) ; Thermal stability ; Lattice constant ; Thermoelectric effect Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials
Authors: Xinyu Jiang | Haofan Sun | Kamal Choudhary | Houlong Zhuang | Qiong Nian
Source: npj Computational Materials (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Interpretable ensemble learning for materials property prediction with classical interatomic potentials Abstract Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the preprocess to transfer a crystal structure into the input of ML, called descriptor, needs to be designed carefully. To efficiently predict important properties of materials, we propose an approach based on ensemble learning consisting of regression trees to predict formation energy and elastic constants based on small-size datasets of carbon allotropes as an example. Without using any descriptor, the inputs are the properties calculated by molecular dynamics with nine different classical interatomic potentials. Overall, the results from ensemble learning are more accurate than those from classical interatomic potentials, and ensemble learning can capture the relatively accurate properties from the nine classical potentials as criteria for predicting the final properties. Ensemble learning ; Property (philosophy) ; Crystal structure prediction ; Microcanonical ensemble ; Process (computing) ; Molecular dynamics ; Interatomic potential ; Canonical ensemble ; Ensemble forecasting Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques