jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiyuan Zhao | Veerupaksh Singla | Hsuan‐Hao Hsu | Brett M. Savoie
Source: Journal of Chemical Theory and Computation (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Graphically Defined Model Reactions Are Extensible, Accurate, and Systematically Improvable Achieving fast and accurate reaction prediction is central to a suite of chemical applications. Nevertheless, classic approaches based on templates or simple models are typically fast but with limited scope or accuracy, while the emerging machine learning-based models are limited in their transferability due to the lack of large reaction databases. Here, we address these limitations by formalizing the model reaction concept based on fixed-depth condensed reaction graphs that are shown to achieve a cost and accuracy balance that is applicable to many problems. The model reaction concept can be utilized to provide reliable predictions of activation energies and transition state geometries for a large range of organic reactions. In addition, using an alkane pyrolysis system as a benchmarking example, we show that the accuracy of the activation energy prediction can be further improved by adding correction terms based on the empirical Brønsted-Evans-Polanyi (BEP) relationship. These successful applications demonstrate that the model reaction can serve as a general tool to reduce the cost associated with ab initio transition state searches. Computer science ; Benchmarking ; Scope (computer science) ; Transferability ; Ab initio ; Range (aeronautics) ; Suite ; Theoretical computer science ; Computational science ; Machine learning ; Chemistry ; Programming language ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: А. Р. Мухутдинов | M.G. Efimov
Source: Nonlinear World (article, 2025-02-02)
Field: Materials Science — Polymers and Plastics

Validation of a universal computational express method for developing a neural network model to evaluate the properties of PVC composites The article presents a method for developing a neural network model for assessing the properties of polyvinyl chloride (PVC) composites using a universal express computational method. The universal express computational method for developing a neural network model for assessing the properties of PVC composites was tested with a relative prediction error of no more than 9%. A neural network model was created that can predict the operational parameters of PVC composites, such as density and plasticity, based on specified input characteristics. It is known that the mechanical properties of PVC composites are difficult to predict due to nonlinear relationships between the components, which makes this study particularly relevant. The research methodology included the development of a knowledge base, the creation of a uniform universal neural network model with general regression in the NeuroShell development environment and its subsequent testing. The initial data for the neural network model included six input characteristics, such as the content of virgin and recycled PVC, calcium carbonate, di-2-ethylhexyl phthalate, chlorinated paraffin wax and CaCO3 particle size. The neural network model was tested on twelve training examples and showed a relative prediction error of no more than 7,8%. The results of the study show that the developed ANN is able to accurately predict the properties of PVC composites, which is confirmed by a low relative error. The prospects of using neural network modeling to solve such problems lies in the fact that the proposed express method allows you to effectively create neural network models with minimal time and resource costs. The article is a significant contribution to the field of computer modeling of materials, offering a new approach to assessing the properties of complex composite materials using modern information technologies. Artificial neural network ; Composite material ; Computer science ; Materials science ; Artificial intelligence Polymer Science and PVC
Authors: Bin Feng | Jiying Zhang | Xuexian Zhang | Z. Liu | Li Yu
Source: ArXiv.org (preprint, 2025-09-02)
Field: Materials Science — Materials Chemistry

BioMD: All-atom Generative Model for Biomolecular Dynamics Simulation Molecular dynamics (MD) simulations are essential tools in computational chemistry and drug discovery, offering crucial insights into dynamic molecular behavior. However, their utility is significantly limited by substantial computational costs, which severely restrict accessible timescales for many biologically relevant processes. Despite the encouraging performance of existing machine learning (ML) methods, they struggle to generate extended biomolecular system trajectories, primarily due to the lack of MD datasets and the large computational demands of modeling long historical trajectories. Here, we introduce BioMD, the first all-atom generative model to simulate long-timescale protein-ligand dynamics using a hierarchical framework of forecasting and interpolation. We demonstrate the effectiveness and versatility of BioMD on the DD-13M (ligand unbinding) and MISATO datasets. For both datasets, BioMD generates highly realistic conformations, showing high physical plausibility and low reconstruction errors. Besides, BioMD successfully generates ligand unbinding paths for 97.1% of the protein-ligand systems within ten attempts, demonstrating its ability to explore critical unbinding pathways. Collectively, these results establish BioMD as a tool for simulating complex biomolecular processes, offering broad applicability for computational chemistry and drug discovery. Computational model ; Generative grammar ; Molecular dynamics ; Complex system ; Dynamics (music) ; Generative model ; Modelling biological systems Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shiluo Huang | Yining Jin | Weiqi Jin | Ming Ying
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry. Transformer ; Infrared spectroscopy ; Infrared ; Spectral line ; Analytical Chemistry (journal) ; Chemistry ; Computer science ; Engineering ; Environmental chemistry ; Physics ; Electrical engineering ; Organic chemistry ; Optics ; Voltage ; Astronomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: A.D. LIFANOV | Е. В. Лифанова
Source: Herald of Technological University (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

CONSTRUCTION OF A MODEL FOR PREDICTING THE ENTHALPY OF SUBLIMATION OF ORGANIC COMPOUNDS USING MACHINE LEARNING METHODS AND FRAGMENTS DESCRIPTORS Currently, a large amount of experimental data has been accumulated in chemistry. In this regard, there is a need to improve computational methods for storing and processing experimental data. In this approach, organic compounds are represented as a set of descriptors that characterize the features of the chemical structure of molecules. In practice, the transition to the gas phase is thermodynamically possible not only for liquids, but also for substances in the solid state. For a large number of organic compounds, their transition from a solid state to a gaseous state is possible, bypassing the liquid phase. This process is called sublimation. The quantitative characteristic of such a transition is the enthalpy of sublimation, which is denoted as Hsub. This parameter is an important thermodynamic characteristic and is undoubtedly of practical interest. Data on octane numbers for organic compounds were taken from literature data. Information on the enthalpy of sublimation for 845 organic substances was included in the database for this work. To simplify the analysis of the representation of organic compounds, we used 208 RDKit descriptors, as they are among the best descriptors for predicting the properties of chemical compounds. These descriptors are created based on the shared keys of the substructure. In addition, the models were calculated using Morgan's molecular fingerprints, also known as circular prints with a radius of 2. Within the framework of this work, ridge regression, the random forest algorithm, the kNN nearest neighbor method, the support vector machine (SVM) method, and artificial neural networks were implemented. For the training sample, the obtained random forest classification model showed an error-free classification, the prediction error for it is 0. The statistical characteristics of the constructed ridge regression model for the sample have the following values: R2 =0.88 and the prediction error RMSE=13.05 kDj/mol. Sublimation (psychology) ; Enthalpy ; Thermodynamics ; Chemistry ; Artificial intelligence ; Computer science ; Physics ; Psychology Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Finkler, Jonas Alexander | Lin, Yan; id_orcid 0000-0002-2320-9777 | Du, Tao | Hu, Jilin; id_orcid 0000-0002-7739-7769 | Smedskjær, Morten Mattrup
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Diffusion Generative Models for the Inverse Design of Disordered Materials Designing new materials with tailored properties is one of the ultimate goals of materials science. Traditionally, new materials are discovered through trial-and-error based experiments, guided by expert knowledge. However, recently a new approach, called inverse design, has become possible in which machine learning models are used to directly predict a new materials structure and composition given its target properties.<br/><br/>To enable inverse design of disordered materials such as glasses, we developed the AMDEN (Amorphous Materials DEnoising Network) framework. Our implementation is based on an equivariant graph neural network architecture and can be conditioned to generate material samples with specific properties. We developed an extensive data set of over 9000 oxide glasses containing up to three different glass formers and seven modifiers, for which we computed properties including elastic moduli, heat capacities and thermal expansion coefficients to benchmark our method.<br/><br/>Our results show that the model can generate high quality glass samples with a valid short-range order. By conditioning the model, samples with specific properties can be generated, even extending beyond the scope of the training data, indicating excellent extrapolation capabilities. Both composition and structure are modified by the model to obtain the desired properties, offering an exciting opportunity to explore property-structure relationships in disordered materials. Despite the model’s excellent performance in terms of structure and properties, challenges arise when it comes to generating low-energy, relaxed structures. To address this, we extended the traditional denoising scheme by integrating a Monte Carlo based relaxation procedure. Extrapolation ; Inverse ; Benchmark (surveying) ; Monte Carlo method ; Artificial neural network ; Set (abstract data type) ; Bundle ; Relaxation (psychology) ; Inverse problem Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Material Dynamics and Properties
Authors: Sanghyeok Ryou | Jihyun Lim | M. S. Jang | Kitae Eom | Sunwoo Lee | Hyungwoo Lee
Source: Advanced Science (article, 2025-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Approach to Characterize Ferromagnetic La <sub>0.7</sub> Sr <sub>0.3</sub> MnO <sub>3</sub> Thin Films via Featurization of Surface Morphology Ferromagnetic perovskite oxides, particularly La<sub>0.7​</sub>Sr<sub>0.3</sub>MnO<sub>3</sub> (LSMO), show significant promise for spintronics and electromagnetic applications due to their unique half-metallicity and colossal magnetoresistance properties. These properties are known to arise from Mn-O-Mn double-exchange interactions, which are directly related to microscopic lattice structures. However, since the microscopic structure in LSMO is highly sensitive to various material parameters, such as thickness, lattice strain, oxygen deficiency, and cation stoichiometry, understanding the intricate relationship between the microscopic structures and the resulting physical properties of LSMO remains challenging. Herein, a machine learning approach is introduced to characterize ferromagnetic LSMO thin films by featurization of their surface morphology. Using an ensemble machine learning method, the non-linear correlations between surface morphology and the electronic, magnetic properties of LSMO thin films are captured and modeled. Based on these estimated correlations, LSMO thin films are classified into five representative types, each characterized by distinctive properties and surface morphologies. These results imply that surface morphology can reveal hidden information about the strongly correlated properties of ferromagnetic LSMO thin films. Consequently, the machine learning-based approach provides an efficient method for understanding the correlated material properties of ferromagnetic oxides and related materials through surface morphology analysis. Ferromagnetism ; Materials science ; Spintronics ; Thin film ; Condensed matter physics ; Colossal magnetoresistance ; Magnetoresistance ; Morphology (biology) ; Ferromagnetic material properties ; Nanotechnology ; Magnetization ; Chemical physics ; Machine learning ; Computer science ; Chemistry ; Physics ; Magnetic field ; Geology Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Danyu Xiong
Source:  (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

Deep Learning-Based Image Recognition of Meat Freshness Assessing meat freshness rapidly and reliably is critical for food safety and public health. Traditional inspection methods—whether manual or chemical—suffer from subjectivity, high cost, and limited real-time applicability. In this work, we present an automated visual inspection framework for meat freshness based on a lightweight Convolutional Neural Network (CNN). Building on a constructed dataset, MeatDataset, which categorizes samples into three freshness levels—Fresh, Half-Fresh, and Spoiled—we develop a compact CNN architecture named SimpleVGG. The model incorporates data augmentation and normalization strategies to enhance robustness and generalization under limited data conditions. Experimental results demonstrate that SimpleVGG achieves 96.2% accuracy on the validation set, outperforming traditional image-processing baselines (85–90%) and human inspectors (90–95%) while maintaining a real-time inference speed of 3 ms per image. These results confirm that deep learning provides an effective and scalable solution for intelligent food-quality monitoring, paving the way for automated and non-destructive freshness assessment in industrial environments. Convolutional neural network ; Robustness (evolution) ; Normalization (sociology) ; Deep learning ; Visual inspection ; Automation ; Inference ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: S. K. Nevhal | Fan Yang | S. A. Meguid | S. I. Kundalwal
Source: Nano Micro Mechanics Review (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Numerical modeling of flexoelectricity: Status, opportunities and challenges Flexoelectricity — the induction of electric polarization in response to a mechanical strain gradient — is a universal electromechanical phenomenon that has gained increasing attention for its relevance in nanoscale sensors, actuators, and energy harvesting systems. Unlike piezoelectricity, flexoelectricity is not constrained by material symmetry and thus occurs in all dielectric materials, making it particularly significant at reduced dimensions where strain gradients are amplified. This review presents a comprehensive examination of flexoelectric behavior across one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) material systems, including nanostructures, polymers, ceramics, and functional composites. We highlight recent advances in theoretical frameworks and computational methodologies that have deepened the understanding of flexoelectric coupling mechanisms. Atomistic simulations using Density Functional Theory (DFT) and Molecular Dynamics (MD) have provided crucial insights into polarization response and electronic structure under strain gradients. Finite Element Methods (FEM) enable modeling of flexoelectric effects at the continuum level, linking microscopic and macroscopic responses. Emerging machine learning (ML) techniques and high-throughput screening have further accelerated the discovery and optimization of flexoelectric materials, offering data-driven strategies to predict flexoelectric coefficients and design application-specific materials. The review also discusses key theoretical developments, including tensorial representations, direct and converse effects, and symmetry considerations. By integrating insights from multiscale modeling, computational simulation, and data science, this paper offers a holistic perspective on the current state and future directions of flexoelectricity research. It serves as a critical resource for advancing the modeling and design of next-generation materials and devices based on flexoelectric principles. Flexoelectricity ; Finite element method ; Polarization (electrochemistry) ; Converse ; Dielectric ; Computational model ; Coupling (piping) ; Multiscale modeling Nonlocal and gradient elasticity in micro/nano structures ; Vibration and Dynamic Analysis ; Piezoelectric Actuators and Control
Authors: Fabian P. Krüger | Nicklas Österbacka | Mikhail Kabeshov | Ola Engkvist | Igor V. Tetko
Source: ChemRxiv (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

MolEncoder: Towards Optimal Masked Language Modeling for Molecules Predicting molecular properties is a key challenge in drug discovery. Machine learning models, especially those based on transformer architectures, are increasingly used to make these predictions from chemical structures. Inspired by recent progress in natural language processing, many studies have adopted encoder-only transformer architectures similar to BERT (Bidirectional Encoder Representations from Transformers) for this task. These models are pretrained using masked language modeling, where parts of the input are hidden and the model learns to recover them before fine-tuning on downstream tasks. In this work, we systematically investigate whether core assumptions from natural language processing, which are commonly adopted in molecular BERT-based models, actually hold when applied to molecules represented using the Simplified Molecular Input Line Entry System (SMILES). Specifically, we examine how masking ratio, pretraining dataset size, and model size affect performance in molecular property prediction. We find that higher masking ratios than commonly used significantly improve performance. In contrast, increasing model or pretraining dataset size quickly leads to diminishing returns, offering no consistent benefit while incurring significantly higher computational cost. Based on these insights, we develop MolEncoder, a BERT-based model that outperforms existing approaches on drug discovery tasks while being more computationally efficient. Our results highlight key differences between molecular pretraining and natural language processing, showing that they require different design choices. This enables more efficient model development and lowers barriers for researchers with limited computational resources. We release MolEncoder publicly to support future work and hope our findings help make molecular representation learning more accessible and cost-effective in drug discovery. Computer science ; Linguistics ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Sarah K. Yorke | Zhenze Yang | Aviad Levin | Asim K. Ray | Jeremy Owusu Boamah | Tuomas P. J. Knowles | Markus J. Buehler
Source: PubMed (preprint, 2025-05-13)
Field: Materials Science — Biomaterials

High-throughput Screening of the Mechanical Properties of Peptide Assemblies. Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a unique set of material properties. The functionalities of these materials hold expectional interest for applications in biology, medicine, photonics, nanotechnology and the food industry. In specific, the ability to exploit peptides as viable and sustainable mechanical materials requires sequence design that enables superior performance, notably a high Young's modulus. As the peptide sequence space is vast, however, even a slight increase in sequence length leads to an exponential increase in the number of potential peptide sequences to be characterized. Here, we combine coarse-grained molecular dynamics simulations, atomic force microscopy experiments and machine learning models to correlate the sequence length and composition with the mechanical properties of self-assembled peptides. We calculate the Young's modulus for all possible amino acid sequences of di- and tripeptides using high-throughput coarse-grained methods, and validate these calculations through in-situ mechanical characterization. For pentapeptides, we select and calculate properties for a subset of sequences to train a machine learning model, which allows us to predict the modulus for other sequences. The combined workflow not only identifies promising peptide candidates with exceptional mechanical performances, but also extends current understanding of the sequence-to-function relationships for peptide materials, for specific applications. Throughput ; High-throughput screening ; Peptide ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Biochemistry ; Telecommunications Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Antimicrobial Peptides and Activities