jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prashant Govindarajan | Mathieu Reymond | Antoine Clavaud | Mariano Phielipp | Santiago Miret | Sarath Chandar
Source: ArXiv.org (preprint, 2025-09-27)
Field: Materials Science — Materials Chemistry

CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning In silico design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional theory (DFT) calculations. While recent works showcase the strong potential of machine learning to accelerate the material design process, they mostly consist of generative approaches that do not use direct DFT signals as feedback to improve training and generation mainly due to DFT's high computational cost. To aid the adoption of direct DFT signals in the materials design loop through online reinforcement learning (RL), we propose CrystalGym, an open-source RL environment for crystalline material discovery. Using CrystalGym, we benchmark common value- and policy-based reinforcement learning algorithms for designing various crystals conditioned on target properties. Concretely, we optimize for challenging properties like the band gap, bulk modulus, and density, which are directly calculated from DFT in the environment. While none of the algorithms we benchmark solve all CrystalGym tasks, our extensive experiments and ablations show different sample efficiencies and ease of convergence to optimality for different algorithms and environment settings. Additionally, we include a case study on the scope of fine-tuning large language models with reinforcement learning for improving DFT-based rewards. Our goal is for CrystalGym to serve as a test bed for reinforcement learning researchers and material scientists to address these real-world design problems with practical applications. We therefore introduce a novel class of challenges for reinforcement learning methods dealing with time-consuming reward signals, paving the way for future interdisciplinary research for machine learning motivated by real-world applications. Reinforcement learning ; Benchmark (surveying) ; Convergence (economics) ; Class (philosophy) ; Active learning (machine learning) ; Scope (computer science) ; Online machine learning Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Aloïs Castellano | Romuald Béjaud | P. Richard | Olivier Nadeau | Clément Duval | Grégory Geneste | Gabriel Antonius | J. Bouchet | Antoine Levitt | Gabriel Stoltz | François Bottin
Source: Computer Physics Communications (preprint, 2025-07-17)
Field: Materials Science — Materials Chemistry

Machine learning assisted canonical sampling (Mlacs) Sampling (signal processing) ; Computer science ; Artificial intelligence ; Non canonical ; Mathematics ; Machine learning ; Telecommunications Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Liangxu Xie (3824200) | Yingdi Jin (1914595) | Lei Xu (78676) | Shan Chang (287210) | Xiaojun Xu (153741)
Source: Figshare (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Fusing Domain Knowledgewith a Fine-Tuned Large LanguageModel for Enhanced Molecular Property Prediction Although large language models (LLMs) have flourished in various scientific applications, their applications in the specific task of molecular property prediction have not reached a satisfactory level, even for the specific chemistry LLMs. This work addresses a highly crucial and significant challenge existing in the field of drug discovery: accurately predicting the molecular properties by effectively leveraging LLMs enhanced with profound domain knowledge. We propose a Knowledge-Fused Large Language Model for dual-Modality (KFLM2) learning for molecular property prediction. The aim is to utilize the capabilities of advanced LLMs, strengthened with specialized knowledge in the field of drug discovery. We identified DeepSeek-R1-Distill-Qwen-1.5B as the optimal base model from three DeepSeek-R1 distilled LLMs and one chemistry LLM named ChemDFM, by fine-tuning with the ZINC and ChEMBL datasets. We obtained the SMILES embeddings from the fine-tuned model and subsequently integrated the embeddings with the molecular graph to leverage complementary information for predicting molecular properties. Finally, we trained the hybrid neural network on the combined dual modality inputs and predicted the molecular properties. Through benchmarking on regression and classification tasks, our proposed method can obtain higher prediction performance for nine out of ten datasets in the downstream regression and classification tasks. Visualization of the output of hidden layers indicates that the combination of the embedding with the molecular graph can offer complementary information to further improve the prediction accuracy compared with either the LLM embedding or the molecular graph inputs. Larger models do not inherently guarantee superior performance; instead, their effectiveness hinges on our ability to leverage relevant knowledge from both pretraining and fine-tuning. Implementing LLMs with domain knowledge would be a rational approach to making precise predictions that could potentially revolutionize the process of drug development and discovery. Leverage (statistics) ; Embedding ; Property (philosophy) ; Artificial neural network ; Knowledge base ; Molecular graph ; Field (mathematics) ; Graph ; Convolutional neural network ; Conditional random field Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Daniella Pereira dos Santos | Jefferson R. Dias-Silva | Luiz Henrique Keng Queiroz Júnior | Heibbe C. B. de Oliveira
Source: Journal of Chemical Information and Modeling (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

ThermoPred: AI-Enhanced Quantum Chemistry Data Set and ML Toolkit for Thermochemical Properties of API-Like Compounds and Their Degradants In this work, we present an open-access quantum-chemistry database of more than 14,500 API-like molecules and their degradation products, all optimized at the M06-2<i>X</i>/6-31G(d) compound model. The data set delivers a comprehensive suite of thermochemical and quantum descriptors─including Gibbs free energy, enthalpy, electronic energy, vibrational frequencies and Cartesian geometries─tailored for large-scale modeling. Leveraging these data, we trained and validated three machine-learning models (XGBoost, Random Forest and Multi-Layer Perceptron) to enable rapid, accurate prediction of Gibbs free energy and enthalpy. These models are bundled in ThermoPred, an open-source Python package that offers a scalable, computationally efficient alternative to traditional quantum-chemical calculations. All data sets, models and source code are freely available to support reproducibility and foster community-driven development. Python (programming language) ; Gibbs free energy ; Quantum chemical ; Set (abstract data type) ; Suite ; Quantum chemistry ; Data set ; Molecule ; Spurious relationship Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Deysiane Alves Lima Santos | Anerise de Barros | Gabriel Cesar Pereira | Fernando A. Sigoli | Ítalo Odone Mazali
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate Quantification of Binary Pesticide Mixtures Using Shape-Induced Enhanced Raman Scattering (SIERS) and Machine Learning Raman scattering ; Raman spectroscopy ; Substrate (aquarium) ; Binary number ; Scalability ; Projection (relational algebra) ; Polystyrene ; Analytical Chemistry (journal) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jingxing Gao (1835497) | Yachao Dong (6690434) | Tian Qiu (156003) | Wen Sun (273647) | Jian Du (360004)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

DFT-ML-Based PropertyPrediction of Transition MetalComplex Photosensitizers for Photodynamic Therapy Photodynamic therapy (PDT) is a noninvasive clinical treatment for cancers using photosensitizers and light. While most research has focused on organic molecules, such as porphyrins as photosensitizers, there is emerging interest in the utilization of transition metal complexes (TMCs). Photosensitizer synthesis and the following performance test are time- and resource-consuming, so presynthetic screening of photosensitizers for their property would be critical. In this work, a hybrid mechanistic and data-driven model is proposed for the quantitative structure–property relationship (QSPR) of photosensitizers; important excited-state quantum chemistry descriptors (e.g., excitation energy) are first calculated based on density functional theory (DFT), and these descriptors, together with other molecular descriptors, are used to build single and hybrid machine learning (ML) models for the prediction of the singlet oxygen quantum yield of hexacoordinate TMC photosensitizers (Ru-, Ir-, and Re-complex). The support vector regression model and kernel ridge regression model are shown to provide good predictions on test (<i>R</i><sup>2</sup> > 0.9) and external test sets (<i>R</i><sup>2</sup> > 0.7) in single-ML models, while the delta-learning model and the Mixture-of-Experts model can further improve the generalization ability (<i>R</i><sup>2</sup> up to 0.87 on the external test set) and show strong universality. SHAP analysis further confirms the reasonable choice of the mechanistic descriptors in the QSPR model. To our knowledge, this constitutes the first integrated DFT-ML framework specifically designed for the unique challenges of small data sets in TMC photosensitizer research. Singlet oxygen ; Photosensitizer ; Photodynamic therapy ; Quantitative structure–activity relationship ; Density functional theory ; Training set ; Quantum chemical ; Transition metal ; Quantum ; Test set Machine Learning in Materials Science ; Photodynamic Therapy Research Studies ; Computational Drug Discovery Methods
Authors: Muhammad U. Afzal | Shahid S. Siddiqi
Source: Punjab University Journal of Mathematics (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

A Concise Bond-Distance Summation Descriptor for Effective Melting Point Prediction of Boronic Acids Predicting the melting points of boronic acids is crucial for guiding synthetic strategies and understanding their physicochemical behaviors. 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 established 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, Random Forest, XGBoost, LightGBM, and Support Vector Machine. Despite having far fewer features than the high-dimensional Mordred and Morgan representations, our 20-length descriptor achieves competitive results, particularly when paired with XGBoost, which consistently exhibits superior performance in terms of Mean Absolute Error (MAE) and R2 score. These findings underscore the potential of a concise, interpretable descriptor for effective melting point prediction, paving the way for the future integration of this scheme into broader cheminformatics applications. Cheminformatics ; Point (geometry) ; Melting point ; Support vector machine ; Feature (linguistics) ; Random forest ; Coulomb ; Scheme (mathematics) Machine Learning in Materials Science ; Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry
Authors: Tarik Akan | Recep Şahıngöz
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Models for High-Temperature Magnetoresistivity Predictions of Ni81fe19 Monolayers Materials science ; Monolayer ; Condensed matter physics ; Nanotechnology ; Physics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Vittorio Mottini | Liuxi Xing | Charlie Meilinger | S.I. Inamdar | Xiang Chen | Jiaqi Wang | Yi Xing | Jack Darbonne | Zikang Tang | Yunnuo Zhang | Christopher H. Contag | Mi Zhang | Jinxing Li
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-15)
Field: Materials Science — Polymers and Plastics

Highly Adaptive Conductive Polymer Electronics Enhance Neural Data and Learning Accuracy Abstract Human skin, the body’s largest organ, plays a vital role in sensing and transmitting neuronal, mechanical, and biochemical signals, making it an essential non-invasive interface for health monitoring, rehabilitation, and human-machine interaction. However, aging-related changes, including thinning, increased wrinkling, dryness, and altered collagen structure, significantly impact electrical impedance, conductance, and contact stability, challenging the fidelity and consistency of bioelectronic signal acquisition. Here, we address this gap by developing “AdapSkin,” an age-adaptive, skin-mimicking, bio-adhesive, and stretchable polymeric electronic skin interface that seamlessly conforms to diverse skin properties, enabling high-fidelity and high-density electrophysiological recording. The soft electrodes of AdapSkin are composed of an aqueously processed, homogeneously mixed organic nanocomposite with a conductive polymer percolation network, forming a gel-like interface that reduces modulus and enhances skin-electrode contact. The materials platform achieves extraordinary softness and electrical stretchability of up to 1200% through a double-network composite structure. AdapSkin significantly minimizes age-induced variations in interfacial impedance and signal-to-noise ratio (SNR), improving signal consistency for neuromuscular assessment, prosthetic control, and rehabilitation applications. Scalable fabrication enables the creation of large-area electrode arrays, which reduces motion artifacts, improves sEMG mapping reliability, and ensures long-term signal stability across various age groups. Machine learning analysis further demonstrates AdapSkin’s superior accuracy in gesture classification for elderly users, highlighting its potential to enhance prosthetic control, assistive robotics, and rehabilitation for individuals with sarcopenia and neuromuscular decline. By improving signal quality and adaptability in aging populations, AdapSkin advances fair bioelectronic interfaces, fostering more equitable and effective healthcare technologies for age-related conditions. SIGNAL (programming language) ; Interface (matter) ; Electrical conductor ; Exoskeleton ; Consistency (knowledge bases) ; Electronics ; Wearable technology ; Electrical impedance ; Conductive polymer Conducting polymers and applications ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: H. Q. Lu | Xinhu Sun | Guozhong Li | Zhi Qian
Source: Industrial & Engineering Chemistry Research (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

An Interpretable Predictive Model for High-Gravity NO <sub> <i>x</i> </sub> Oxidation-Absorption Driven by Physics-Informed Neural Networks High-gravity technology offers a frontier solution for efficient NOx removal, yet its intricate internal mass transfer-reaction coupling poses significant modeling challenges, particularly for “black-box” machine learning methods that lack physical consistency. To this end, we introduce a Physics-Informed Neural Network (PINN) with a hard parameter-sharing architecture to simulate the oxidation-absorption of NOx by an alkaline H2O2 solution in a high-gravity field. By learning common physical laws through a shared base network and handling multispecies dynamics via dedicated branches, this model markedly improves convergence speed and generalization. The results demonstrate that the model achieves high-precision predictions of NOx absorption efficiency (R2 = 0.931, RMSE = 4.871%) and exhibits excellent robustness, even with sparse or noisy data. Furthermore, concentration field predictions combined with SHAP analysis reveal the underlying mechanisms: rotational speed is the dominant factor governing process efficiency by intensifying fluid shear and interfacial renewal to promote the reaction between NO and the hydroperoxide anion (−OOH), with its importance followed by H2O2 concentration, pH, and liquid flow rate. This study not only delivers an interpretable tool for predicting and optimizing the high-gravity De-NOx process but also establishes a new intelligent modeling paradigm for elucidating other complex multiphase environmental systems such as advanced oxidation processes or gas purification processes. Artificial neural network ; NOx ; Process (computing) ; Convergence (economics) ; Field (mathematics) ; Coupling (piping) ; Deep learning Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Atmospheric chemistry and aerosols
Authors: Junhao Xue | Jun Liu | Kai Chen
Source: Computational and Structural Biotechnology Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Context-informed few-shot molecular property prediction via heterogeneous meta-learning Molecular property prediction is essential in diversified applications, as it helps identify molecules with the desired characteristics. However, the task often suffers from limited data, making the few-shot learning challenging. We introduce a Context-informed Few-shot Molecular Property Prediction via a Heterogeneous Meta-Learning approach, which employs graph neural networks combined with self-attention encoders to effectively extract and integrate both property-specific and property-shared molecular features, respectively. Based on the property-shared molecular features, we further infer molecular relations by using an adaptive relational learning module. The final molecular embedding is improved by aligning with the property label in the property-specific classifier. Furthermore, we employ a heterogeneous meta-learning strategy that updates parameters of the property-specific features within individual tasks in the inner loop and jointly updates all parameters in the outer loop. This enhances the model's ability to effectively capture both general and contextual information, leading to a substantial improvement in predictive accuracy. The model's performance was rigorously evaluated across various real molecular datasets, showcasing its superiority over current methods, especially in challenging few-shot learning scenarios. Context (archaeology) ; Property (philosophy) ; Shot (pellet) ; Computer science ; Data science ; Chemistry ; Biology ; Epistemology Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Topic Modeling
Authors: Frank
T. Mtetwa (22455266) | Neil F. Giles (1821934) | W.Vincent Wilding (22455269) | Thomas A. Knotts (3113916)
Source: Figshare (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Prioritizing DataQuality in Machine Learning forThermophysical Property Prediction: A Case Study on Normal BoilingPoints of Organic Compounds Accurate prediction of the normal boiling point (NBP) of organic compounds is essential for the design and optimization of chemical processes. This is especially true for modern practice since experimental measurements of many compounds of contemporary importance are difficult to perform due to cost, purity, and stability issues. Traditional methods, such as group contribution (GC) and quantitative structure–property relationship (QSPR) approaches, are limited by inflexibility and diminishing accuracy in these situations. Machine learning (ML) approaches have shown promise but are plagued by two weaknesses for the nonspecialist: ease of use (especially compared to GC and QSPR) and proven accuracy for compounds outside the training set. The large effort required to use an ML model of others, let alone develop a new model, is the source of the first problem. The second often arises due to the data-intensive nature of ML. In particular, the origin and reliability of the data used to train a model has to date been of secondary importance compared to finding the optimal ML techniques to use for a given task. This study addresses both these issues. First, we explore the effectiveness of ML models trained on two data sets: a smaller, rigorously curated experimental set from the DIPPR 801 database and a larger, publicly available but uncurated data set. Four distinct molecular featurization methods were evaluated, including RDKit descriptors, Joback groups, and two graph-based representations using graph neural networks (GNNs). Models were implemented using LightGBM and PyTorch, and performance was assessed through a 10-fold cross-validation across four evaluation frameworks. Despite conventional expectations that larger data sets yield superior models, results consistently showed that the curated data set of fewer but certifiably accurate values outperformed the uncurated one in terms of accuracy, bias reduction, and generalization. These findings emphasize the importance of data quality over quantity and advocate for a data-centric approach in ML applications for chemical property prediction. A user-friendly web application is also provided to facilitate access to the best-trained model for the nonspecialist, where all that is needed is a SMILES formula (a single and simple text string that is widely available) for the molecule. Stability (learning theory) ; Property (philosophy) ; Reliability (semiconductor) ; Artificial neural network ; Graph ; Set (abstract data type) ; Boiling point ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Halil Burak Mutu
Source: Savunma Bilimleri Dergisi (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Ballistic Performance Analysis of Silicon Carbide Ceramic Body Armor Using Finite Element Method and Machine Learning Algorithms This study presents a machine learning-based approach for predicting the residual velocity of projectiles impacting silicon carbide (SiC) ceramic body armor plates of varying thicknesses. Explicit dynamic simulations were performed using the ANSYS finite element software to model the ballistic response of the armor under high-velocity impact. Key input parameters included projectile type, bullet muzzle velocity, ceramic thickness, and mesh size. The output parameter of interest was the residual velocity of the projectile after impact. Simulation data were used to train and evaluate three different machine learning models: Linear Regression, ElasticNet, and Multilayer Perceptron (MLP). The predictive performance of each model was assessed using the coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE) metrics across both training and testing datasets. Among the tested algorithms, the MLP model achieved the highest accuracy and lowest error values, demonstrating superior capability in capturing the complex nonlinear relationships governing ballistic impact phenomena.The findings indicate that machine learning techniques, when trained with high-fidelity simulation data, can serve as efficient predictive tools for estimating residual velocity in ballistic protection applications. This approach can significantly reduce the need for extensive physical testing and computationally expensive simulations during the preliminary design phase of protective armor systems, thereby accelerating the material selection and optimization process. Projectile ; Residual ; Armour ; Finite element method ; Perceptron ; Multilayer perceptron ; Silicon carbide ; Ballistic impact High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology