jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sole Gomez, Jaume Alexandre | Mosella-Montoro, Albert | Cardona, Joan | Gómez Coca, Silvia | Aravena Ponce, Daniel Alejandro | Ruiz Sabín, Eliseo | Ruiz-Hidalgo, Javier
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

A Cartesian encoding graph neural network for crystal structure property prediction: application to thermal ellipsoid estimation In the diffraction resolution of crystal structures, thermal ellipsoids are a critical parameter that is usually more difficult to determine than atomic positions. These ellipsoids are quantified through Anisotropic Displacement Parameters (ADPs), which provide critical insights into atomic vibrations within crystalline structures. ADPs reflect the thermal behaviour and structural properties of crystal structures. However, traditional methods to compute ADPs are computationally intensive. This paper presents CartNet, a novel graph neural network (GNN) architecture designed to predict properties of crystal structures efficiently by encoding the atomic structural geometry to the Cartesian axes and the temperature of the crystal structure. Additionally, CartNet employs a neighbour equalization technique for message passing to help emphasise the covalent and contact interactions and a novel Cholesky-based head to ensure valid ADP predictions. Furthermore, a rotational SO(3) data augmentation technique has been proposed during the training phase to generalize unseen rotations. To corroborate this procedure, an ADP dataset with over 200 000 experimental crystal structures from the Cambridge Structural Database (CSD) has been curated. The model significantly reduces computational costs and outperforms existing previously reported methods for ADP prediction by 10.87%, while demonstrating a 34.77% improvement over the tested theoretical computation methods. Moreover, we have employed CartNet for other already known datasets that included different material properties, such as formation energy, band gap, total energy, energy above the convex hull, bulk moduli, and shear moduli. The proposed architecture outperformed previously reported methods by 7.71% in the JARVIS dataset and 13.16% in the Materials Project dataset, proving CarNet's capability to achieve state-of-the-art results in several tasks. The project website with online demo available at: https://www.ee.ub.edu/cartnet. Ellipsoid ; Computation ; Cartesian coordinate system ; Crystal (programming language) ; Artificial neural network ; Phaser ; Graph ; Thermal ; Cartesian product ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Prashil S. Joshi | Diksha Mahadule | Rajesh K. Khatirkar
Source: Materials Chemistry and Physics (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

A machine learning and microstructural synergy for flow stress prediction in hot-deformed Ti–6Al–4Mo–1V–0.1Si alloy Misorientation ; Flow stress ; Artificial neural network ; Support vector machine ; Deformation (meteorology) ; Inverse ; Titanium alloy ; Kriging ; Random forest Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Machine Learning in Materials Science
Authors: Daniel Kaplan | Alexander C. Tyner | Eva Y. Andrei | J. H. Pixley
Source: ArXiv.org (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine learning assisted high throughput prediction of moiré materials The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Often, however, it is not clear a priori whether a pair of monolayers twisted at a small angle will exhibit correlated or interaction-driven phenomena. The computational cost to make accurate predictions of the single particle states is significant, as small twists require very large unit cells, easily encompassing 10,000 atoms, and therefore implementing a high throughput prediction has been out of reach. Here we show a path to overcome this challenge by introducing a machine learning (ML) based methodology that efficiently estimates the twisted interlayer tunneling at arbitrarily low twist angles through the local-configuration based approach that enables interpolating the local stacking for a range of twist angles using a random forest regression algorithm. We leverage the kernel polynomial method to compute the density of states (DOS) on large real space graphs by reconstructing a lattice model of the twisted bilayer with the ML fitted hoppings. For twisted bilayer graphene (TBG), we show the ability of the method to resolve the magic angle DOS at a substantial improvement in computational time. We use this new technique to scan through the database of stable 2D monolayers (MC2D) and reveal new twistable candidates across the five possible points groups in two-dimensions with a large DOS near the Fermi energy, with potentially exciting interacting physics to be probed in future experiments. Leverage (statistics) ; Stacking ; Twist ; Bilayer graphene ; Random forest ; Lattice (music) ; Kernel (algebra) ; A priori and a posteriori ; Computational complexity theory Graphene research and applications ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Putera, Zefrika Gauraf | Khairannisa, Najwa | Maulana, Aldi
Source: OSF Preprints (OSF Preprints) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

REVIEW OF PHYSICAL CHEMISTRY ON THE ASPECT OF THE DISSOLUTION EFFECT DUE TO SYSTEM ENTROPY IN THE PROCESS SAFETY AND ENVIRONMENTAL PROTECTION ARTICLE WITH THE TOPIC: PREDICTIVE ANALYSIS OF METHANE-ENHANCED CONVERSION OF ORGANIC WASTE INTO SUSTAINABLE FUEL: A MACHINE LEARNING APPROACH PROCESS SAFETY AND ENVIRONMENTAL PROTECTION The dissolution effect driven by system entropy represents a fundamental concept in physical chemistry that plays a decisive role in understanding the methane-enhanced conversion of organic waste into sustainable fuel. In the context of the Process Safety and Environmental Protection article, “Predictive analysis of methane-enhanced conversion of organic waste into sustainable fuel: A machine learning approach,” entropy is considered a key thermodynamic parameter governing solute–solvent interactions and phase stability. Dissolution of organic substrates in the presence of methane generates an increase in molecular disorder, thereby enhancing system entropy and lowering Gibbs free energy, which facilitates spontaneous reactions. This entropic contribution supports the homogenization of reactants, promotes molecular collisions, and accelerates energy transfer, all of which are critical for sustainable fuel formation. The review of physical chemistry aspects underscores that dissolution is not a purely enthalpy-driven process; rather, entropy governs the efficiency of solubilization and the kinetics of intermediate formation in the reaction medium. Machine learning integration further strengthens this perspective by predicting thermodynamic behavior and optimizing process conditions to maximize entropic advantages. By correlating dissolution entropy with reactivity trends, the analysis highlights how system disorder can be harnessed to improve fuel yield, ensure process safety, and enhance environmental protection. Overall, the dissolution effect due to entropy provides both a mechanistic explanation and a predictive tool for sustainable waste-to-fuel technologies. Keyword: Dissolution Entropy,Gibbs Free Energy (ΔG), Methane-Enhanced Waste Conversion,Thermodynamic Optimization, Machine Learning in Physical Chemistry Dissolution ; Entropy (arrow of time) ; Gibbs free energy ; Process safety ; Context (archaeology) ; Physical system ; Process (computing) Thermal and Kinetic Analysis ; Process Optimization and Integration ; Machine Learning in Materials Science
Authors: Sakshi Choudhary | Ravi Kumar | Venkateswarlu Annapureddy | GANGIREDDY SALLA
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Embedding-Driven Physics Informed Neural Network for Predicting Optical Constants Across Materials Artificial neural network ; Bidirectional reflectance distribution function ; Fresnel equations ; Inverse problem ; Identifier ; Differentiable function ; Dispersion (optics) ; Embedding ; Function (biology) ; Constant (computer programming) Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Thermal Radiation and Cooling Technologies
Authors: Amirali Shateri | Zhiyin Yang | Lei Xing | Yuying Yan | Zhen Wu | Jianfei Xie
Source: Research Square (preprint, 2025-10-09)
Field: Materials Science — Materials Chemistry

Chemical-Guided Machine Learning Framework for Reactive Simulations of NOx Mitigation in Alcohol-Enhanced Ammonia–Methane Combustion NOx ; Combustion ; Nitrogen oxides ; Nitrogen oxide ; Methanol ; Methane ; Field (mathematics) Catalytic Processes in Materials Science ; Combustion and flame dynamics ; Catalysis and Oxidation Reactions
Authors: Guy W. Dayhoff | Sameer Varma
Source: ChemRxiv (preprint, 2025-09-11)
Field: Materials Science — Materials Chemistry

Velocity Updates from Machine Learning for Stable Molecular Dynamics Integration We present a machine-augmented molecular dynamics (MAMD) framework in which particle velocities are updated using predictions from stacked long short-term memory (LSTM) networks trained on historical velocity and coordinate data. MAMD propagates coordinate trajectories in time without access to forces or energies during training or inference. Applied to isolated harmonic diatomics, MAMD conserves total energy, preserves molecular structure, and reproduces velocity autocorrelation functions. Small integration errors can accumulate over long trajectories, but we show that molecular dynamics stability can be recovered through periodic, but infrequent injections of velocity updates computed from Hamiltonian forces (frequency ≤ 0.01). We also find that the optimal history length for each diatom closely matches the first inflection point of its velocity autocorrelation function, suggesting a link between model architecture and statistical mechanics. These results demonstrate the feasibility of MAMD as a proof-of-concept integration strategy, in which finite velocity memory supports short-horizon predictions and sparse Hamiltonian check-ins provide long-horizon stability. Specifically, we show that force-free, velocity-based machine learning updates can be embedded directly into conventional molecular dynamics algorithms while retaining essential physical invariants, providing a physically interpretable basis for hybrid MD/ML integration. Autocorrelation ; Molecular dynamics ; Inflection point ; Hamiltonian mechanics ; Hamiltonian (control theory) ; Stability (learning theory) ; Point (geometry) Machine Learning in Materials Science
Authors: Judah Immanuel | Avik Mahata | Aniruddha Maiti
Source: arXiv (Cornell University) (preprint, 2025-12-26)
Field: Materials Science — Materials Chemistry

Toward Generalizable Surrogate Models for Molecular Dynamics via Graph Neural Networks We present a graph neural network (GNN) based surrogate framework for molecular dynamics simulations that directly predicts atomic displacements and learns the underlying evolution operator of an atomistic system. Unlike conventional molecular dynamics, which relies on repeated force evaluations and numerical time integration, the proposed surrogate model propagates atomic configurations forward in time without explicit force computation. The approach represents atomic environments as graphs and combines message-passing layers with attention mechanisms to capture local coordination and many-body interactions in metallic systems. Trained on classical molecular dynamics trajectories of bulk aluminum, the surrogate achieves sub angstrom level accuracy within the training horizon and exhibits stable behavior during short- to mid-horizon temporal extrapolation. Structural and dynamical fidelity are validated through agreement with reference radial distribution functions and mean squared displacement trends, demonstrating that the model preserves key physical signatures beyond pointwise coordinate accuracy. These results establish GNN-based surrogate integrators as a promising and computationally efficient complement to traditional molecular dynamics for accelerated atomistic simulations within a validated regime. Molecular dynamics ; Pointwise ; Surrogate model ; Complement (music) ; Graph ; Artificial neural network ; Operator (biology) ; Surrogate data Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Hanli Qiao | Mohammad Jooshaki | Massimo Rolle | Timo Lähivaara | Marko Vauhkonen | Tommi Kauppila | Muhammad Muniruzzaman
Source: Water Resources Research (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Hidden Reactive Transport Neural Network: A Physics‐ and Chemistry‐Informed Surrogate Modeling Approach and Application to Pyrite Oxidation Abstract Reactive transport models (RTMs) are essential tools to describe and integrate a wide range of physical and biogeochemical processes in natural and engineered porous media. However, the high computational cost often limits their applications for many practical purposes. These challenges mainly stem from the solution of a set of coupled partial differential equations (PDEs), describing multicomponent transport and geochemical reactions along with their multilevel coupling across different spatial and temporal scales. To mitigate this issue, we propose and develop a surrogate modeling approach, hidden reactive transport neural network (HRTNet). The proposed model relies on a flexible architecture based on two networks, which share a common loss function and allows incorporating both data‐driven and physics‐chemistry‐informed contributions. We consider pyrite oxidation in a 1‐D geochemically heterogeneous domain as a model example to investigate and demonstrate the capability of HRTNet as well as to analyze the performance of the developed surrogate modeling approach. HRTNet was trained based on the training data generated from the process‐based reactive transport simulations, and, successively, the trained model was used as a surrogate to predict the behavior of the reactive transport system. The results reveal that the predictions obtained by the trained surrogate model agree well with those from mechanistic RTMs in a considerably reduced computation time. Furthermore, the physics‐ and chemistry‐informed learning was promising to achieve a good generalization capability, because HRTNet could predict the desired spatio‐temporal dynamics for a wide range of initial concentrations beyond the training data sets. Surrogate model ; Artificial neural network ; Generalization ; Set (abstract data type) ; Pyrite ; Range (aeronautics) ; Applicability domain ; Multiscale modeling ; Domain (mathematical analysis) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Hydrological Forecasting Using AI
Authors: K. P. Beena
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Medicinal Chemistry: A Comprehensive Review Artificial intelligence (AI) has developed as a transformative force in medicinal chemistry, redesigning early drug discovery, hit identification, lead optimisation, and synthetic route planning. Effective exploration of chemical space and determination of key molecular properties has been enabled by advances in machine learning (ML), deep learning (DL), graph neural networks (GNNs), and generative models (such as variational autoencoders and transformer-based chemical language models). AI now plays a vital role across virtual screening, de-novo design, reaction prediction, retrosynthesis planning, ADMET profiling, and structure-based drug design. Remains challenging despite its rapid progress which includes data quality issues, model interpretability, experimental validation gaps, and integration into existing medicinal chemistry workflows. This review summarises the current AI methodologies, major applications, advantages, limitations and future opportunities in medicinal chemistry. Transformative learning ; Chemical space ; Generative grammar ; Key (lock) ; Quality (philosophy) ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Hyunbin choi | Suyong Jung | Sang Wook Lee
Source: ACS Applied Nano Materials (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Data-Efficient Machine Learning for Layer Number Identification of 2D Materials Using Color Difference Analysis The identification of the number of layers in two-dimensional (2D) materials is critical for exploiting their unique quantum and optoelectronic properties. In this study, we propose a machine learning-assisted approach to classify the number of layers of five different 2D materials based on pixel-level microscopic image data, accommodating diverse experimental conditions. Our methodology leverages RGB and YIQ color spaces to capture the color differences between 2D material flakes and their substrates, with YIQ demonstrating superior performance. Among various models, such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision tree models, we demonstrate that the decision tree algorithm with YIQ data provides the highest classification accuracies across all materials, particularly effective for intermediate layers with values of 2–5. Even with the limited data for training, our pixel-level approach demonstrates a superb layer-number identification capability for up to five layers. Our study highlights the versatility of machine learning-assisted layer-number classification methods with minimal image data, offering a time-efficient and cost-effective solution for identifying various 2D materials with diverse imaging environments. Identification (biology) ; Computer science ; Layer (electronics) ; Artificial intelligence ; Color difference ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Graphene research and applications ; 2D Materials and Applications ; Conducting polymers and applications
Authors: Kyle D. Miller | Michele Campbell | Danilo Puggioni | James M. Rondinelli
Source: Physical Review Materials (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Decoratypes: An extensible crystal taxonomy for machine learning-guided materials discovery The authors present decoratypes, a new extensible crystal taxonomy that offers a more granular lens for structure--property relationships by classifying materials by hierarchical property-site mappings. For example, the framework generalizes anti-structures into polaritypes, a subclass of decoratype. They demonstrate its utility by building a polaritype-based active learning workflow for discovering ferroelectric and hyperferroelectric materials. Our approach identified six novel candidates, including three strain-activated ferroelectrics and three strain-activated hyperferroelectrics. These findings highlight how decoratypes can provide a novel perspective on the search for functional materials in underexplored chemical spaces. Workflow ; Taxonomy (biology) ; Perspective (graphical) ; Extensibility ; Lens (geology) ; Ferroelectricity Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Alatalo, Lukas
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Minskad komplexitet i prediktion av reaktionsutbyte : funktionsurval och träd-baserade modeller för Buchwald–Hartwig-kopplingar Accurate prediction of chemical reaction yields is a critical task in synthetic chemistry, directly impacting the design and optimization of reaction pathways. This thesis focuses on yield prediction for Pd-catalyzed Buchwald– Hartwig amination reactions—a widely used method for forming C–N bonds. While deep learning models, particularly artificial neural networks, have shown promise in modeling such reactions, they often struggle with practical limitations: they are sensitive to noisy descriptors, vulnerable to skewed target distributions, and typically require large, high-quality datasets to perform well. This thesis proposes a more robust and interpretable alternative to deep learning approaches by combining gradient boosted tree models with a modular, chemically informed feature engineering pipeline. Initially, benchmark models based on artificial neural networks using quantum mechanical descriptors were reproduced. These were then systematically compared to gradient boosted tree models trained on both quantum mechanical and alternative descriptor sets, including Morgan fingerprints, Differential Reaction Fingerprints, and RDKit molecular descriptors. To address the resulting high-dimensional feature space, a two-stage feature pruning strategy was implemented using correlation-based and model-aware genetic algorithms. The results show that gradient boosted tree models consistently outperformed their artificial neural networks counterparts in terms of robustness and predictive accuracy, particularly on skewed and noisy datasets. Moreover, models trained on pruned, multimodal descriptors achieved similar or superior performance to those using computationally expensive quantum mechanical features, while greatly improving training efficiency and interpretability. However, external validation on proprietary Chemify reactions highlight the need to include broader experimental conditions such as reaction time, temperature, and molecular quantities in the modeling process to allow for generalizability. By demonstrating that simpler, tree-based models with tailored feature selection can match or surpass deep learning in this context, this work offers a scalable and transparent framework for reaction yield prediction. These insights contribute to the development of more efficient and accessible tools for cheminformatics and automated synthesis planning. Artificial neural network ; Robustness (evolution) ; Differential evolution ; Deep learning ; Benchmark (surveying) ; Feature (linguistics) ; Feature engineering ; Deep neural networks Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions