jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhilong Song | Chongyi Ling | Qiang Li | Qionghua Zhou | Jinlan Wang
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Stability and Symmetry-Assured Crystal Structure Generation for Inverse Design of Photocatalysts in Water Splitting Generative models are revolutionizing materials discovery by enabling inverse design-direct generation of structures from desired properties. However, existing approaches often struggle to ensure inherent stability and symmetry while precisely generating structures with target compositions, space groups, and lattices without fine-tuning. Here, we present SSAGEN (Stability and Symmetry-Assured GENerative framework), which overcomes these limitations by decoupling structure generation into two distinct stages: crystal information (lattice, composition, and space group) generation and coordinate optimization. SSAGEN first generates diverse yet physically plausible crystal information, then derives stable and metastable atomic positions through universal machine learning potentials, combined global and local optimization with symmetry and Wyckoff position constraints, and dynamically refined search spaces. Compared to prior generative models such as CDVAE, SSAGEN improves the thermodynamic and kinetic stability of generated structures by 148% and 180%, respectively, while inherently satisfying target compositions, space groups, and lattices. Applied to photocatalytic water splitting (PWS), SSAGEN generates 200,000 structures-81.2% novel-with 3,318 meeting all stability and band gap criteria. Density functional theory (DFT) validation confirms 95.6% structures satisfy PWS requirements, with 24 optimal candidates identified through comprehensive screening based on electronic structure, thermodynamic, kinetic, and aqueous stability criteria. SSAGEN not only precisely generates materials with desired crystal information but also ensures inherent stability and symmetry, establishing a new paradigm for targeted inverse design of functional materials. Stability (learning theory) ; Decoupling (probability) ; Inverse ; Crystal structure prediction ; Density functional theory ; Metastability ; Crystal structure ; Space (punctuation) ; Symmetry (geometry) ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Juan Carlos Salas | Javiera Minder | Felipe Núñez
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Towards Industry 4.0: A Data-Driven Model of Electrolyte Composition and Performance Variables for Copper Electrorefining Electrowinning ; Electrolyte ; Copper ; Composition (language) ; Metallurgy ; Materials science ; Chemistry ; Electrode Machine Learning in Materials Science ; Metal Extraction and Bioleaching ; Extraction and Separation Processes
Authors: Zhong Lin | Feng Shi
Source: Preprints.org (preprint, 2025-08-21)
Field: Materials Science — Biomaterials

Polymeric Nanocarriers for Targeted Therapy: Current Advances and Future Perspectives Polymeric nanomedicine has emerged as a versatile and transformative platform for the delivery of therapeutic agents, offering controlled release, enhanced stability, and targeted delivery across a broad spectrum of medical applications. This review comprehensively discusses the design, functionalization, and therapeutic potential of polymeric nanoparticles, with an emphasis on both natural and synthetic polymers and diverse nanocarrier systems, including nanospheres, nanocapsules, micelles, dendrimers, polymersomes, and nanogels. Advances in surface modification, stimuli-responsive carriers, and multifunctional platforms have enabled precise targeting, improved pharmacokinetics, and reduced off-target toxicity. Key therapeutic applications are explored, spanning cancer therapy, gene therapy, infectious diseases, neurological disorders, and regenerative medicine. The review also addresses challenges in clinical translation, including regulatory hurdles, scalable manufacturing, safety and toxicity considerations, and the design of personalized treatment strategies. Future perspectives highlight the integration of personalized and precision medicine, smart stimuli-responsive polymers, AI/machine learning-driven design optimization, nano-immunotherapy, and hybrid polymeric systems, underscoring their potential to revolutionize global healthcare. By synthesizing current knowledge and outlining translational strategies, this review provides a roadmap for advancing polymeric nanomedicine from bench to bedside, ultimately paving the way for safer, more effective, and patient-specific therapeutic interventions. Nanocarriers ; Current (fluid) ; Nanotechnology ; Medicine ; Engineering ; Materials science ; Drug delivery ; Electrical engineering Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications
Authors: Tristan Maxson | Tibor Szilvási
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Training Accurate and Physically Meaningful Machine Learning Force Fields for Water Water is an important material as it constitutes the majority fraction of electrochemical liquid phases, however, the accurate simulation of water is challenging because it requires long molecular dynamics simulations to equilibrate the system and is governed by the complex interactions between the molecules. The MB-Pol potential has been developed to reproduce CCSD(T) quality results, but at a lower cost than full quantum mechanical simulations and allows to reproduce the ground truth for physical properties (e.g. density, compressibility, radial distribution function, etc.) which are typically inaccessible using quantum mechanical calculations due to long equilibration times (10 – 100 ns). Machine learning interatomic potentials (MLIPs) serve as a method for circumventing the computational cost of density functional theory (DFT) to accelerate atomistic simulations by learning the complex set of interactions from existing data. We show that the equivariant Allegro MLIP architecture can accurately predict the liquid, gas, and solid phases of H 2 O when trained from only the liquid phase which has previously been observed to be difficult when using DeePMD in literature, which is a non-equivariant MLIP. Importantly, we find that our MLIPs can describe accurate gas phase many-body interactions not directly part of the training set and allow even faster simulations than MB-pol. Finally, we give insights into training methods and parameters that should be used for training electrolyte systems based on DFT data based on our MB-Pol work. Figure 1 Set (abstract data type) ; Liquid water ; Quantum ; Training (meteorology) ; Physical system ; Molecular dynamics ; Phase (matter) ; Quality (philosophy) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: ADEWUYI REUBEN ADEBARE
Source: European Journal of Materials Science and Engineering (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

PREDICTION OF HARDNESS CHARACTERISTICS OF TIG WEELDED Co-Mo STEEL FLAT BRAS USING NEUTRAL NETWORKS This study investigates the development and application of a neural network (NN) model to predict hardness characteristics in Cr-Mo steel welded joints, utilizing the SPSS software suite. The model incorporates four key welding parameters; material thickness, welding current, the number of weld passes, and electrode diameter as inputs, with hardness values in the weld zone (WZ) and heat-affected zone (HAZ) as outputs. The neural network features a single hidden layer with eight neurons, using the Softmax activation function for non-linear regression tasks. A comprehensive dataset comprising 18 combinations of the input parameters was employed to train, validate, and test the model, ensuring it could generalize across diverse welding conditions. Results demonstrate the model’s high predictive accuracy, particularly in the HAZ, where an R² value of 0.997 and a low Mean Squared Error (MSE) of 0.94 indicate minimal prediction error. The analysis also reveals that material thickness is the most influential parameter, significantly affecting hardness outcomes, while welding current, number of weld passes, and electrode diameter play secondary roles. However, the model&amp;#39;s performance varies between zones, with greater dispersion observed in the HAZ, suggesting complexities in predicting hardness due to microstructural changes in this region. Overall, the study confirms that the SPSS-developed neural network is a robust tool for predicting hardness in welded joints, offering valuable insights for optimizing welding parameters to achieve desired mechanical properties. This approach can reduce the need for extensive physical experimentation, streamlining the welding process in industrial applications. Metallurgy ; Materials science ; Gas tungsten arc welding ; Welding Metal Alloys Wear and Properties ; Advancements in Materials Engineering ; Surface Treatment and Coatings
Authors: Majdi I. Radaideh | Matt Raymond | Paolo Elvati | Jacob C. Saldinger | Angela Violi
Source: Energy and AI (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Efficient sampling of polycyclic aromatic compounds for free energy predictions through active learning The physical growth of Polycyclic Aromatic Compounds (PACs) to soot particles plays a significant role in understanding the chemistry of soot formation. Insights into the process can be gained from PACs’ free energy of dimerization landscape. However, because the infeasibly large space of possible PAC dimers cannot be exhaustively simulated, researchers must train machine learning models on a subset of data to impute the rest. To this end, we propose and assess an active learning approach to discovering the optimal PACs for training a machine learning model to predict PACs’ association and dissociation free energies. The comparison between active learning and random sampling showed that active learning has faster loss convergence, requiring fewer training samples to reach the same level of accuracy. The trained model accurately modeled unseen PACs and exhibited robustness against changes in the sampling space used to train the model. More broadly, this work shows how active learning can optimize the design and improve the understanding of more expensive models in specific domains. • Polycyclic Aromatic Compounds (PAC) growth to soot is key to understanding soot formation chemistry. • Machine learning models can predict PAC dimerization free energy from a limited dataset. • Active learning outperforms random sampling for PAC modeling, needing fewer samples to achieve high accuracy. • The trained model is robust and predicts unseen PACs, aiding in complex model optimization. Sampling (signal processing) ; Energy (signal processing) ; Environmental science ; Chemistry ; Computer science ; Statistics ; Mathematics Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Machine Learning and Algorithms
Authors: Matthew Witman | Lauren Way | Catalin D. Spataru | Reese E. Jones | Sean R. Bishop | Anthony H. McDaniel | Andrew J. E. Rowberg | Tadashi Ogitsu | Manish Kumar | Robert B. Wexler | Anuj Goyal | Stephan Lany
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Surfaces, Coatings and Films

(<i>Invited) </i>Structure-Property Defect Models for Materials Discovery in Clean Energy Applications Formation and migration energies of crystallographic defects dictate material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based calculations are a powerful computational technique for predicting defect formation energies and migration energy barriers, yet they can become prohibitively expensive for high-throughput materials discovery exercises. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors and using only pristine host structures as input, we propose generalized structure-property defect models by hybridizing defect graph neural networks with transformer encoders to predict these properties. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for thermochemical water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be rapidly identified and experimentally validated. Figure 1 Property (philosophy) ; Clean energy ; Energy (signal processing) ; Materials science ; Process engineering ; Computer science ; Biochemical engineering ; Environmental science ; Engineering ; Mathematics ; Environmental protection ; Statistics ; Philosophy Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Jiayang Xie | Zhihao Zhang | Xiaoming Cao
Source: Chemistry of Materials (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Density of States Prediction by a Metric-Optimized Equivariant Graph Neural Network Density of states (DOS) is a crucial factor in elucidating the physical and functional properties of materials, playing a significant role in materials design. However, traditional density functional theory (DFT) calculations of DOSs are computationally expensive and inefficient, particularly when dealing with large structures and high-throughput electronic structure calculations. To address this challenge, we propose a metric-optimized equivariant graph neural network, called EqDOS, for DOS prediction, which can efficiently learn interatomic interactions from atomic position information. This capability allows the model to accurately predict DOS even with limited data, exhibiting excellent generalization capability. Additionally, by incorporating probability distribution metrics of high-order difference and Wasserstein distance for multiobjective learning, we effectively address the issues of oversmoothness and the sensitivity of sharp peaks in DOS curve prediction. These are especially important when handling materials with multiple compositions and structures in the periodic table. EqDOS enables the zero-shot prediction on the DOS profiles of ternary single-atom alloy surfaces with excellent performance outcomes, using the well-trained model based on the binary alloy surface data set. Furthermore, the predicted DOS by our model could serve as input for the downstream material performance prediction models. The results show that our model not only achieves higher accuracy in DOS curve fitting but also performs exceptionally well on the prediction of adsorption energy when combined with a downstream DOS Transformer for Adsorption (DOTA) model. This confirms that our model can accurately predict the DOS while preserving its physical significance. These findings not only validate the generalization and effectiveness of our model framework but also provide a new tool for the field of materials science to accelerate the discovery and optimization of new materials. Ternary operation ; Generalization ; Binary number ; Artificial neural network ; Equivariant map ; Divide and conquer algorithms ; Position (finance) ; Graph ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Smritilekha Bera
Source: Carbohydrate Research (article, 2025-08-29)
Field: Chemistry — Organic Chemistry

Methods for the synthesis of natural UDP-sugars and synthetic analogues Chemistry ; Natural (archaeology) ; Natural compound ; Combinatorial chemistry ; Stereochemistry ; Biochemical engineering Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Diet, Metabolism, and Disease
Authors: Sudheesh
Kumar Ethirajan (22530443) | Ambarish R. Kulkarni (1726468)
Source: Figshare (article, 2025-10-30)
Field: Chemistry — Inorganic Chemistry

Modeling Diffusionin Metal–Organic FrameworksUsing On-the-fly Probability Enhanced Sampling-Based Machine LearningPotentials Machine learning potentials (MLPs) can help bridge the length- and time-scale gaps required to study diverse physicochemical phenomena in nanoporous materials with ab initio accuracy. These MLPs are typically trained on quantum chemical data obtained from traditional molecular dynamics (MD) simulations that predominantly sample near-equilibrium configurations. This often limits the model’s ability to describe high-energy, off-equilibrium states required to study rare events. To address this capability gap, we introduce a novel active learning curriculum using the On-the-fly Probability Enhanced Sampling (OPES) method. Using imidazole diffusion in the SALEM-2 metal–organic framework (MOF) as a prototypical example, we apply time-dependent OPES biases with temperature- and distance-based collective variables to systematically sample the most relevant regions of the potential energy surface. This strategy enables nanosecond-scale MD simulations with near-DFT accuracy, capturing both the expected diffusion pathway across the 6-membered window and a previously unreported ring-opening mechanism through the more constrained 4-membered window. The latter process, which involves the transient dissociation of a Zn–N bond, cannot be captured using classical force fields and is prohibitively expensive with DFT. Thus, this study highlights how enhanced sampling methods can overcome data scarcity challenges associated with training MLPs for studying rare events in nanoporous materials. Sampling (signal processing) ; Ab initio ; A priori and a posteriori ; Nanoporous ; Ensemble learning ; Molecular dynamics ; Sample (material) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: Muhammad Zia Afzal | Shahid S. Siddiqi
Source: Punjab University Journal of Mathematics (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

AConcise Bond-Distance Summation Descriptor for Effective Melting PointPrediction of Boronic Acids Predicting the melting points of boronic acids is crucial for guiding synthetic strategies and understanding their physicochemical be haviors. In this study, we introduce a novel bond-distance summation descriptor, a concise 20-component vector that numerically encodes the molecular structure by summing atomic numbers over the shortest paths from the boron atom. We benchmarked this descriptor against four es tablished feature extraction methods Coulomb Matrix, Mordred, Morgan Fingerprints, and Molecular ACCess System (MACCS) and evaluated the predictive accuracy of five machine learning models: Decision Tree, Ran dom Forest, XGBoost, LightGBM, and Support Vector Machine. Despite having far fewer features than the high-dimensional Mordred and Mor gan representations, our 20-length descriptor achieves competitive results, particularly when paired with XGBoost, which consistently exhibits supe rior performance in terms of Mean Absolute Error (MAE) and R2 score. These findings underscore the potential of a concise, interpretable descrip tor for effective melting point prediction, paving the way for the future integration of this scheme into broader cheminformatics applications. Melting point ; Cheminformatics ; Point (geometry) ; Feature (linguistics) ; Support vector machine ; Coulomb ; Scheme (mathematics) ; Ranking (information retrieval) Machine Learning in Materials Science ; Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry
Authors: Nakyung Lee | Małgorzata Sójka | Annie La | Saurabh Sharma | Seán R. Kavanagh | Docheon Ahn | David O. Scanlon | Jakoah Brgoch
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Machine Learning a Phosphor's Excitation Band Position Creating superior lanthanide-activated inorganic phosphors is pivotal for advancing energy-efficient LED lighting and backlit flat panel displays. The most fundamental property these luminescent materials must possess is effective absorption/excitation by a blue InGaN LED for practical conversion into white light. The 5$d_1$ excited state energy level of lanthanides, which determines the excitation peak position, is influenced by the inorganic host structure, including the local environment, crystal structure, and composition, making it challenging to predict in advance. This study introduces a new extreme gradient boosting machine learning method that quantitatively determines a phosphor's longest (lowest energy) excitation wavelength. We focus on the Ce$^{3+}$ 4$f$ $\rightarrow$ 5$d$ transition due to its well-defined 5$d_1$ energy level observed in excitation and diffuse reflectance spectra. The model was trained on experimental data for 357 Ce$^{3+}$ cation substitution sites sourced from literature and in-house measurements and ultimately experimentally validated through the successful synthesis of a novel, blue-excited, green-emitting phosphor: Ca$_2$SrSc$_6$O$_{12}$:Ce$^{3+}$. This compound's excitation under commercial blue LED wavelength aligned remarkably well with the model's predictions. These results highlight the transformative potential of data-driven approaches in expediting the discovery of blue-absorbing phosphors for next-generation LED lighting. Excitation ; Backlight ; Boosting (machine learning) ; Phosphor ; Excited state ; Luminescence ; Wavelength ; Expediting Luminescence Properties of Advanced Materials ; Ammonia Synthesis and Nitrogen Reduction ; Machine Learning in Materials Science
Authors: Guangcheng Niu (21087196) | Yilei Wu (1367877) | Xinyu Chen (47670) | Yehui Zhang (5518478) | Shijun Yuan (1848583) | Jinlan Wang (1235505)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Efficient andAccurate Prediction of Double PerovskiteQuasiparticle Band Gaps via Machine Learning and a Descriptor Perovskites have attracted considerable attention in materials science due to their promising applications in photovoltaics and photocatalysis. Accurate prediction of their electronic band gap is essential for optimizing the performance. Traditional computational methods for band gap prediction often face a trade-off between accuracy and computational efficiency. General density functional theory (DFT) calculations typically underestimate band gap values, while the more accurate quasi-particle method demands substantial computational resources. In this study, a multistep machine learning framework was developed for efficient screening of semiconductor double perovskites. Furthermore, we proposed an interpretable descriptor that can predict quasi-particle band gaps of perovskites with a precision of over 90% accuracy. Using this approach, we screened 4,507 perovskite candidates and identified 94 structures that have suitable band gaps and are lead-free. Among these, six candidate structures were selected for further verification based on their photocatalytic potential and thermal stability. Band gap ; Photovoltaics ; Perovskite (structure) ; Quasiparticle ; Semiconductor ; Density functional theory ; Electronic band structure Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties