jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph Oche Agada | Andrew McAninch | Haley A. Day | Yasemin Tanyu | Ewan McCombs | S. M. Koohpayeh | Brian H. Toby | Yishu Wang | Arpan Biswas
Source: arXiv (Cornell University) (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement. Rietveld refinement ; Diffraction ; Bayesian optimization ; Scaling ; Crystal structure ; Crystal structure prediction ; Powder diffraction ; Tetrahedron Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Amrinder Badgujjar
Source: International Journal on Science and Technology (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Intelligent Systems in Engineering: A Framework for the Application of Artificial Intelligence in Mechanical Research The field of mechanical engineering research is undergoing a paradigm shift driven by integrating Artificial Intelligence (AI) and Machine Learning (ML). Traditional methods, often reliant on computationally expensive simulations and physical experimentation, are being augmented and, in some cases, replaced by data-driven AI models that offer unprecedented speed, accuracy, and insight. This paper explores the transformative application of AI tools across key domains of mechanical research, including materials discovery, design optimization, structural health monitoring, and fluid dynamics. We present a generalized framework for implementing AI solutions, detailing the data acquisition, model selection, and validation processes. Supported by conceptual block diagrams, we illustrate the architecture of AI-driven research pipelines. Finally, we discuss current challenges such as data requirements, model interpretability ("black box" problem), and computational costs, while outlining future directions for this rapidly evolving interdisciplinary field Systems engineering ; Computer science ; Artificial intelligence ; Engineering ; Engineering management ; Construction engineering Machine Learning in Materials Science
Authors: Jiawei Mao | Yinglu Jia | Gaoyang Gou | Shi Liu | Xiao Cheng Zeng
Source: ArXiv.org (preprint, 2025-08-21)
Field: Materials Science — Materials Chemistry

Unraveling Shear Strain Induced Ferroelectric-to-Antiferroelectric Phase Transition and Accessing Intrinsic Antiferroelectricity in Two-dimensional NbOCl2 Compared to the well studied two-dimensional (2D) ferroelectricity, much rare is the appearance of 2D antiferroelectricity, where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely orientated. Using NbOCl2 monolayer with competing ferroelectric (FE) and antiferroelectric (AFE) phases as a 2D material platform, we demonstrate the emerging of intrinsic antiferroelectricity in NbOCl2 monolayer under the experimentally accessible shear strain, and new functionality associated with electric field induced AFE-to-FE phase transition. Specifically, the complex configuration space accommodating FE and AFE phases, polarization switching kinetics and finite temperature thermodynamic properties of 2D NbOCl2, are all accurately predicted by large-scale molecular dynamic (MD) simulations based on deep learning interatomic potential (DP) model. Moreover, room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional (1D) collinear polarization arrangement is predicted in shear deformed NbOCl2 monolayer. Transition from AFE to FE phase in 2D NbOCl2 can be triggered by the low critical electric field, leading to the double polarization-electric (P-E) loop with small hysteresis. A new type optoelectronic device composed of AFE-NbOCl2, enabling electric "writing" and nonlinear optical "reading" logical operation with fast operation speed and low power consumption is also proposed. Antiferroelectricity ; Electric field ; Dipole ; Monolayer ; Polarization (electrochemistry) ; Phase transition 2D Materials and Applications ; Perovskite Materials and Applications ; Ferroelectric and Piezoelectric Materials
Authors: Xiaowei Shen | Danning Song | Hui Zhang | Qian Yang | Biqiang Chen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-28)
Field: Chemistry — Inorganic Chemistry

Deep-learning model–guided discovery and characterization of bacterial unspecific peroxygenases Abstract Unspecific peroxygenases (UPOs) are capable of catalyzing the selective oxidation of organic substrates under mild conditions, using hydrogen peroxide (H 2 O 2 ) as the sole oxidant. This makes them one of the most promising biocatalysts for chemical synthesis. However, the major limitation restricting the application of UPOs to date is their difficulty in heterologous expression. Although more than 4,000 putative UPO enzymes have been recorded in databases, only about 50 of them can currently be heterologously expressed. All UPOs discovered so far originate from eukaryotes (mainly basidiomycetes and some ascomycetes), and they rely on the complex expression systems and post-translational modifications of their native hosts. This further exacerbates the challenges associated with heterologous expression of eukaryotic UPOs. In this work, we developed a deep-learning-based enzyme mining strategy and, for the first time, discovered novel UPO enzymes from bacteria, achieving successful heterologous expression in Escherichia coli. Bacterial UPOs differ greatly from fungal UPOs in sequence similarity, displaying completely distinct evolutionary trajectories. The discovery of bacterial UPOs advances our understanding of the catalytic mechanisms and expression characteristics within the UPO family, breaking the long-held assumption that UPOs can only originate from eukaryotes. Their excellent heterologous expression performance and broad catalytic versatility will further expand the application potential of UPOs. Heterologous ; Heterologous expression ; Enzyme ; Hydrogen peroxide ; Escherichia coli Metal-Catalyzed Oxygenation Mechanisms ; Enzyme-mediated dye degradation ; Enzyme Catalysis and Immobilization
Authors: van Tilborg, Derek
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Data repository for Joint Molecular Model This is all the data and processed results that belongs to the paper: Molecular deep learning at the edge of chemical space. Since the commercial screening libraries we used in some parts of this paper do not explicitly allow resharing of their molecular structures, data and results from these libraries are not included. data: clean: contains all cleaned data used to train all models up to the prospective screening split: contains all split data. Train, test_ID, and test_OOD best model: contains the pretrained autoencoder backbone of the Joint Molecular Model (JJM) and one pretrained SMILES MLP (for one specific target protein). These are combined to finetune a JJM. Because all pretrained and finetuned MLPs take up about 10x the max space of a Zenodo repo, I did not include them. You should be able to train your own with the data and code provided. datasets_with_metrics: some precomputed metrics used to process results results: processed: contains all processed results based on the publicly available datasets for the JJM and control models dataset_clustering: TSNE coordinates used for Supp. Fig. S1 Autoencoder ; Enhanced Data Rates for GSM Evolution ; Chemical space ; Deep learning ; Process (computing) ; Code (set theory) ; Space (punctuation) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Pungponhavoan Tep | Marc Bernacki‫
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations Grain growth simulation is crucial for predicting metallic material microstructure evolution during annealing and resulting final mechanical properties, but traditional partial differential equation-based methods are computationally expensive, creating bottlenecks in materials design and manufacturing. In this work, we introduce a machine learning framework that combines a Convolutional Long Short-Term Memory networks with an Autoencoder to efficiently predict grain growth evolution. Our approach captures both spatial and temporal aspects of grain evolution while encoding high-dimensional grain structure data into a compact latent space for pattern learning, enhanced by a novel composite loss function combining Mean Squared Error, Structural Similarity Index Measurement, and Boundary Preservation to maintain structural integrity of grain boundary topology of the prediction. Results demonstrated that our machine learning approach accelerates grain growth prediction by up to \SI{89}{\times} faster, reducing computation time from \SI{10}{\minute} to approximately \SI{10}{\second} while maintaining high-fidelity predictions. The best model (S-30-30) achieving a structural similarity score of \SI{86.71}{\percent} and mean grain size error of just \SI{0.07}{\percent}. All models accurately captured grain boundary topology, morphology, and size distributions. This approach enables rapid microstructural prediction for applications where conventional simulations are prohibitively time-consuming, potentially accelerating innovation in materials science and manufacturing. Autoencoder ; Deep learning ; Computation ; Grain growth ; Grain boundary ; Pattern recognition (psychology) ; Encoder ; Feature (linguistics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Grace M. Lu | Dallas R. Trinkle
Source: Physical Review Materials (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Explainable machine learning for oxygen diffusion in perovskites and pyrochlores Explainable machine learning can help to discover new physical relationships for material properties. To understand the material properties that govern the activation energy for oxygen diffusion in perovskites and pyrochlores, we build a database of experimental activation energies and apply a grouping algorithm to the material property features. These features are then used to fit seven different machine learning models. An ensemble consensus determines that the most important features for predicting the activation energy are the ionicity of the A-site bond and the partial pressure of oxygen for perovskites. For pyrochlores, the two most important features are the A-site $s$ valence electron count and the B-site electronegativity. The most important features are all constructed using the weighted averages of elemental metal properties, despite weighted averages of the constituent binary oxides being included in our feature set. This is surprising because the material properties of the constituent oxides are more similar to the experimentally measured properties of perovskites and pyrochlores than the features of the metals that are chosen. The easy-to-measure features identified in this work enable rapid screening for new materials with fast oxide-ion diffusivity. Valence (chemistry) ; Binary number ; Oxygen ; Energy (signal processing) ; Work (physics) ; Diffusion ; Metal Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Gergő Róth (11534005) | Ákos Kuki (1968994) | Aron Kipyegon Rop (22194388) | Lajos Nagy (599803) | Zuura Kyzy Kaldybek (22194391) | Máté Benedek (16415665) | Miklós Zsuga (1968991) | Tibor Nagy (425445) | Sándor Kéki (1968988)
Source: Figshare (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Rapid CopolymerAnalysis of Unresolved Mass Spectraby Artificial Intelligence In this Article, we present a novel data analysis method for the determination of copolymer composition from low-resolution mass spectra, such as those recorded in the linear mode of time-of-flight (TOF) mass analyzers. Our approach significantly extends the accessible molecular weight range, enabling reliable copolymer composition analysis even in the higher mass regions. At low resolution, the overlapping mass peaks in the higher mass range hinder a comprehensive characterization of the copolymers. Our approach, however, extracts the hidden information on the unique macromolecular chains embedded in these unresolved peaks. Regression relationships were developed between easily computable mass spectral parameters derived from the measured data and key characteristics of the copolymer structure including the average mole fraction, the number-average repeat unit count, and the monomer distribution. Two regression models were constructed: one using conventional statistical methods and the other employing a machine learning approach based on an Artificial Neural Network (ANN). The spectrum evaluation process is demonstrated for the analysis of various poly(<i>N</i>-acryloylmorpholine)-<i>block</i>-poly(<i>N</i>-isopropylacrylamide) (PNAM-<i>b</i>-PNIPAM) diblock copolymers. Our simplified approach is validated using experimental data and by comparison with our recently developed and reported highly computationally demanding method. Copolymer ; Artificial neural network ; Mass spectrum ; Characterization (materials science) ; Range (aeronautics) ; Monomer ; Principal component analysis ; Pattern recognition (psychology) ; Regression analysis Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Ali A. Khairbek | Ali Moulhim | Hiba H. Ibraheem | Abdullah Yahya Abdullah Alzahrani | T. Pooventhiran | Renjith Thomas
Source: Journal of Molecular Modeling (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Combining data-driven and quantum chemical approaches to evaluate Minnesota functionals (M06-2X, MN12-SX, and MN15) for the enthalpy of formation predictions Quantum chemical ; Gaussian ; Valence (chemistry) ; Density functional theory ; Quantum ; Hydrogen ; Quantum chemistry ; Python (programming language) ; Workflow Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Derek van Tilborg
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data repository for Joint Molecular Model This is all the data and processed results that belongs to the paper: Molecular deep learning at the edge of chemical space. Since the commercial screening libraries we used in some parts of this paper do not explicitly allow resharing of their molecular structures, data and results from these libraries are not included. data: clean: contains all cleaned data used to train all models up to the prospective screening split: contains all split data. Train, test_ID, and test_OOD best model: contains the pretrained autoencoder backbone of the Joint Molecular Model (JJM) and one pretrained SMILES MLP (for one specific target protein). These are combined to finetune a JJM. Because all pretrained and finetuned MLPs take up about 10x the max space of a Zenodo repo, I did not include them. You should be able to train your own with the data and code provided. datasets_with_metrics: some precomputed metrics used to process results results: processed: contains all processed results based on the publicly available datasets for the JJM and control models dataset_clustering: TSNE coordinates used for Supp. Fig. S1 Autoencoder ; Joint (building) ; Process (computing) ; Code (set theory) ; Enhanced Data Rates for GSM Evolution ; Source code ; Deep learning ; Data modeling ; Encoding (memory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Georganna Benedetto (14607894) | Robert M. Stolz (6171680) | Zheng Meng (9293) | Joseph Y. M. Chan (22615391) | Elissa O. Shehayeb (11863914) | Colin T. Morrell (14607900) | Gbenga Fabusola (22378814) | Nikolaus Elsaesser (22615394) | Cory M. Simon (1669942) | Katherine A. Mirica (1473688)
Source: Figshare (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Conductive CovalentOrganic Frameworks as ChemiresistiveSensor Arrays for the Detection and Differentiation of Gasotransmitters This paper describes a chemiresistive sensor array using four structurally analogous, but chemically distinct, conductive covalent organic frameworks (COFs) (M-COF-DC-8, M = Fe, Co, Ni, and Cu) capable of detecting and differentiating four important gaseous analytes: nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H<sub>2</sub>S), and ammonia (NH<sub>3</sub>). The COFs were synthesized from the condensation of 2,3,9,10,16,17,23,24-octaamino-metallophthalocyanine precursors with pyrenetetraone linkers resulting in chemically robust and electrically conductive materials. Chemiresistive sensing experiments, together with machine learning to parse the response pattern of the sensor array, show that the M-COF-DC-8 (M = Fe, Co, Ni, Cu) materials can detect and differentiate this suite of oxidizing and reducing gases at parts-per-million concentrations, with theoretical limits of detection (LOD) in the parts-per-billion range in dry N<sub>2</sub>. Importantly, the COF array containing M-COF-DC-8 (M = Co, Ni, Cu) retains its ability to detect and differentiate these analytes in air and humidity under low power consumption. Spectroscopic investigations reveal that the synthetic control over the identity of the metallophthalocyanine core efficiently tunes material–analyte interactions and, therefore, emergent device performance. The use of highly tunable COFs as the active material in sensor arrays enables low-power, sensitive, and real-time gas detection with future applications in healthcare and personal protection. Electrical conductor ; Analyte ; Covalent bond ; Oxidizing agent ; Covalent organic framework ; Chemiresistor ; Hydrogen sulfide ; Carbon monoxide Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Pelin Sezgin (21662562) | Feride Neva Yüngül (22608199) | Beste Naz Karaca (22608202) | Hasan Can Gulbalkan (14846398) | Seda Keskin (536519)
Source: Figshare (article, 2025-11-12)
Field: Chemistry — Inorganic Chemistry

MolecularModeling-Based Machine Learning for AccuratePrediction of Gas Diffusivity and Permeability in Metal–OrganicFrameworks Gas diffusion determines the performance of metal–organic frameworks (MOFs) in various practical applications, including membrane-based separations, yet its experimental measurement is challenging. We presented an efficient computational framework that integrates high-fidelity molecular dynamics (MD) simulations with machine learning (ML) to predict the diffusivities of CO<sub>2</sub>, N<sub>2</sub>, O<sub>2</sub>, CH<sub>4</sub>, and H<sub>2</sub> in >18,000 synthesized and hypothetical MOFs. ML models trained on MD data accurately predicted gas diffusivities of any given MOF within minutes using only easily accessible structural and guest-related properties. We provided an interactive, user-friendly web interface for predicting diffusivities of MOFs to facilitate material selection. Leveraging ML-predicted diffusivities, we evaluated membrane-based gas separation performances of all MOFs for seven industrially important separations: CO<sub>2</sub>/N<sub>2</sub>, CO<sub>2</sub>/CH<sub>4</sub>, N<sub>2</sub>/CH<sub>4</sub>, H<sub>2</sub>/CO<sub>2</sub>, H<sub>2</sub>/CH<sub>4</sub>, H<sub>2</sub>/N<sub>2</sub>, and O<sub>2</sub>/N<sub>2</sub>. The best MOF membranes offering high selectivity and permeability were identified and analyzed by using molecular fingerprinting to reveal the critical chemical properties for designing next-generation MOFs. Thermal diffusivity ; Permeability (electromagnetism) ; Molecular dynamics ; Experimental data ; Gas separation ; Diffusion ; Membrane ; Interface (matter) Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Catalysts for Methane Reforming