jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sreekanth Kunchapu | Kevin Maik Jablonka
Source: npj Computational Materials (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

PolyMetriX: an ecosystem for digital polymer chemistry Abstract Digital polymer chemistry leverages computational methods to design and optimize polymer materials. While there have been advances in using machine learning to accelerate the design of polymers, the field is hampered by the lack of standards, which precludes comparability and makes it difficult to build on top of prior work. To address this gap, we introduce PolyMetriX, an open-source Python library designed to facilitate the entire polymer informatics workflow—from obtaining data to training models. PolyMetriX provides curated polymer property datasets, and novel featurization techniques that extract hierarchical structural information at the full polymer, backbone, and sidechain levels. Additionally, it incorporates polymer-specific data splitting strategies to ensure robust model generalization. PolyMetriX enhances the predictive performance of models while improving reproducibility in digital polymer chemistry. Comparability ; Polymer ; Python (programming language) ; Field (mathematics) ; Informatics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Polymer Synthesis and Characterization
Authors: Ben Gao | Penghui Li | Di Zhang | Qian Tan | Wanquan Liu | Xunzhi Wang | Junxian Li | Shufei Zhang | Dongzhan Zhou | Yuqiang Li | Guoyin Yin
Source: ChemRxiv (preprint, 2025-09-09)
Field: Materials Science — Materials Chemistry

LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions Modular design ; Scalability ; Artificial neural network ; Transformative learning ; Architecture ; Predictive modelling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Yan Gao | Xinshuo Wang | Huiyu Yuan | Haiyan Xu
Source: The Journal of Chemical Physics (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Thermal conductivity of the layered titanate K0.8Li0.27Ti1.73O4 explored by a deep learning interatomic potential The theoretical prediction of thermal conductivity in many layered oxides remains challenging, primarily due to their structural complexity and low symmetry. The traditional Boltzmann transport equation method is highly accurate but limited by the low-order phonon scattering model, which makes it difficult to resolve the high-order scattering effects of low symmetry layered materials. The classical molecular dynamics calculation is efficient but lacks accuracy due to the missing multi-component potential function. In this study, we develop a strategy to predict the thermal conductivity of K0.8Li0.27Ti1.73O4 (KLTO), a model of layered oxides by machine-learning using a deep neural network model to acquire the interatomic potential of KLTO. The deep learning potential (DLP) is in excellent agreement with density functional theory in predicting atomic force, energy, and elastic properties. In addition, the calculated out-of-plane thermal conductivity values based on the DLP (0.37 W m-1 K-1) are close to experimental results (0.28 W m-1 K-1). This machine-learning framework for constructing interatomic potentials can be extended to other layered materials, offering a promising approach for advancing the theoretical study of such systems. Thermal conductivity ; Interatomic potential ; Phonon ; Boltzmann equation ; Scattering ; Symmetry (geometry) ; Molecular dynamics ; Materials science ; Phonon scattering ; Condensed matter physics ; Density functional theory ; Statistical physics ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Thermal properties of materials
Authors: Eduardo Amorim | Thamer Horbylon Nascimento | Ana Karoline Silva Mendanha Valdo | Juliana Paula Félix | Luciana Cardoso | Renan Vinícius Aranha | Fabrízzio Soares
Source:  (article, 2025-07-08)
Field: Chemistry — Physical and Theoretical Chemistry

Enhancing Chemistry Education: Evaluating Methods for Classifying Hand-Drawn Molecules and Generating 3D Visualizations This work presents a comparison between techniques for recognizing inorganic molecular structures from hand-drawn sketches. The approach used combines Convolutional Neural Networks (CNN) and the Histogram of Oriented Gradients (HOG) method to classify and recognize these sketches. For the tests, we used images drawn by high school students. The comparison of the results obtained with the CNN and HOG techniques was carried out in detail. Additionally, we developed an application capable of generating three-dimensional visualizations of the molecular structures, allowing their representation in a virtual environment. This 3D visualization provides a more intuitive understanding of the molecular structures. This work highlights the effectiveness of machine learning technologies in education, by offering an accessible and interactive educational tool that complements traditional chemistry teaching. In this way, it provides students with a deeper understanding of the physical and chemical properties of inorganic molecules, enriching the learning process and making it more engaging. Computer science ; Visualization ; Chemistry education ; Data science ; Human–computer interaction ; Mathematics education ; Artificial intelligence ; Physics ; Psychology Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Mouna Sbai Idrissi | Ahmed El Hamdaoui | Tarik Chafiq | Saïd Ouaskit
Source: Journal of Non-Crystalline Solids (article, 2025-08-07)
Field: Materials Science — Ceramics and Composites

Structural and mechanical properties of alkali silicate glasses: Insights from molecular dynamics simulations and artificial intelligence Silicate ; Molecular dynamics ; Silicate glass ; Alkali metal ; Materials science ; Dynamics (music) ; Composite material ; Chemistry ; Chemical engineering ; Engineering ; Physics ; Computational chemistry Glass properties and applications ; Material Dynamics and Properties ; Geological and Geochemical Analysis
Authors: Janosh Riebesell | Rhys E. A. Goodall | Philipp Benner | Yuan Chiang | Bowen Deng | Gerbrand Ceder | Mark Asta | Alpha A. Lee | Anubhav Jain | Kristin A. Persson
Source: Nature Machine Intelligence (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Author Correction: A framework to evaluate machine learning crystal stability predictions Stability (learning theory) ; Computer science ; Machine learning Machine Learning in Materials Science
Authors: Negar Bagherieh | Moslem Noori | Dongyang Li | Meisam Nouri
Source: Engineering Reports (article, 2025-05-01)
Field: Materials Science — Biomaterials

Critical Factors Governing the Frictional Coefficient in <scp>Mg</scp> Alloys—Learn From Machine Learning ABSTRACT Data‐driven methods are emerging as a promising approach in discovering the correlation between tribological properties, composition, and mechanical properties of engineering materials. In the present study, the capability of several ML models in predicting the coefficient of friction (COF) of magnesium alloys is studied. To this end, first 1400 data points are extracted from prior studies through an extensive literature review. The collected data is then used to train models for the following two scenarios: (i) COF prediction using composition, processing parameters, and tribological variables; (ii) COF prediction using mechanical properties (hardness, yield strength, ultimate tensile strength, ductility, and elastic modulus), and tribological variables. After preprocessing, the data is partitioned into train and test datasets where the train dataset is used for model training and hyperparameter tuning, K‐fold cross‐validation, and the test dataset is used for evaluating the best trained models. The results indicate that light gradient boosting (LGBM) accurately predicts COF of magnesium alloys using the processing procedure, heat treatment, alloy composition, and tribology variables with an R‐squared value of 0.89. Further, the gradient boosting method (GBM) achieves an R‐squared score of 0.87 for predicting the COF using mechanical properties and tribological variables, showing a promising performance. In addition, a comparative analysis between alloying elements, manufacturing process, heat treatment, mechanical properties, and tribological test variables is performed using feature importance in the trained random forest (RF) models. Our findings highlight the importance of normal load, elastic modulus, and content of Zn in determining the COF in magnesium alloys, which helps improve materials and mechanical system design for effective COF control. Friction coefficient ; Chemistry ; Materials science ; Composite material Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Advanced Welding Techniques Analysis
Authors: Geolita Ihsantia Ning Asih | Ande Fudja Rafryanto | Sri Hartati | Xiaoyi Jiang | Alinda Anggraini | Azis Yudhowijoyo | Jizhou Jiang | Arramel Arramel
Source: Composite Functional Materials (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Recent Advances of Polymer Nanocomposites in Emerging Applications Renewed interest in polymer nanocomposites (PNCs) has garnered significant impact towards next-generation hybrid materials. Owing to the outstanding mechanical, thermal, electrical, and chemical properties of PNCs, the integration of various nanoparticles or other emerging nanofillers into polymer matrices renders their applicability. This review outlines recent progress and a comprehensive overview across multidisciplinary fields of chemistry and physics concepts including surface chemistry and polymer science, emphasizing their enhanced photophysical performance over traditional composites. To examine their promising physicochemical nature, several fabrication techniques are outlined: in situ polymerization, solution blending, melt compounding, and electrospinning. In addition, state-of-the-art characterization tools that cover in situ or operando, including X-ray diffraction, neutron scattering, and various spectroscopic methods, are summarized for nanoscale structures and dynamics interpretation. Driven by stringent requirements for improved interfacial bonding and nanofiller dispersion, recent advancements in computational techniques such as density functional theory (DFT) in combination with machine learning (ML) are introduced to achieve high accuracy in terms of polymer structure predictive design. The multitude of aspects of PNCs embarked on diverse applications spanning from energy sector (fuel cells, solar cells, batteries, and supercapacitors), petroleum engineering (enhanced oil recovery), environmental fields (wastewater treatment via photocatalysis), biomedicine (drug delivery), and in biosensors (high-precision volatile analytes). This review highlights the vast potential of PNCs in addressing technological challenges such as structural complexity and engineering trade-offs. Moreover, several profound future research directions, including scalable fabrication and multifunctional material design are discussed. Characterization (materials science) ; Polymer nanocomposite ; Nanocomposite ; Multidisciplinary approach ; Polymer ; Nanofluidics ; Nanoparticle Polymer Nanocomposites and Properties
Authors: Zayan A. Ali | Julius Mutschler | O. Waldmann
Source: Physical review. B./Physical review. B (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Determination of ligand field parameters of single-molecule magnets from magnetic susceptibility using deep learning based inverse models Lanthanide-based single-molecule magnets (SMMs) have garnered extensive attention in recent decades for their potential applications in magnetic memory and quantum computing. In ligand field theory, the magnetic properties of a lanthanide ion may have to be characterized by up to 27 ligand field parameters. However, common experimental data such as temperature-dependent susceptibility curves measured on powder samples typically lack distinctive features. This leads to a massive over-parametrization, where multiple parameter sets can equally describe the data. In this work the issue is tackled by applying a deep learning architecture which combines a variational autoencoder (VAE) with an invertible neural network (INN). This VAE-INN architecture is shown to be capable of determining multiple ligand field parameter sets by extracting hidden parameters from the magnetic data and mapping them to the space of ligand field parameters. The approach is tested on simulated and experimental susceptibility data in magnetic models involving second-order ligand field parameters and is gradually extended to higher-order parameters until it is found limited by dimensionality issues. Compared with traditional least-squares fitting methods, the proposed approach exhibits enhanced generalization, robustness, and convergence. The present investigation of the VAE-INN architecture can be hoped to pave the way for further exploration of higher-order ligand field parameters. Magnet ; Inverse ; Field (mathematics) ; Magnetic field ; Ligand (biochemistry) ; Physics ; Nuclear magnetic resonance ; Chemistry ; Mathematics ; Quantum mechanics ; Geometry ; Pure mathematics ; Biochemistry ; Receptor Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Angela Slavova
Source: Engineering Sciences (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Neural Networks and Artificial Intelligence The pioneering methods and concepts developed by John Hopfield and Geoffrey Hinton have been instrumental in shaping the field of artificial neural networks (ANNs).In addition, Hinton played a leading role in the efforts to extend the methods to deep and dense ANNs.With their breakthroughs, which stand on the foundations of physical science, they have shown a completely new way for us to use computers to aid and to guide us in tackling many of the challenges in our society.Simply put, thanks to their work, Humanity now has a new item in its toolbox, which we can choose to use for good purposes.Machine learning based on ANNs is currently revolutionizing science, engineering, and daily life.The field is already on its way to enable breakthroughs toward building a sustainable society, e.g., by helping to identify new functional materials.How deep learning by ANNs will be used in the future depends on how we humans choose to use these incredibly potent tools, already present in many aspects of our lives.In this paper, we present a short survey on the work of the two Nobel laureates in the field of machine learning and neural networks.We present recent achievements in the field of machine learning and artificial intelligence.Moreover, we provide new results on machine learning through physics-informed neural networks. Artificial neural network ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Neural Networks and Reservoir Computing
Authors: Bowen Kan | Honghui Shang
Source: Journal of Chemical Theory and Computation (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Accelerating Many-Body Quantum Chemistry via Generative Transformer-Enhanced Configuration Interaction Quantum many-body calculations are fundamentally limited by the exponential growth of the configuration space, making accurate treatment of strongly correlated systems computationally prohibitive. Here we present the Generative Transformer Neural Network Selected Configuration Interaction (GTNN-SCI), a Transformer-based machine learning approach that generatively samples important configurations to accelerate many-body quantum chemistry calculations. By leveraging the Transformer architecture's self-attention mechanism to capture long-range electron correlations, GTNN-SCI achieves up to 10× speedup compared to state-of-the-art neural network methods while maintaining high accuracy. We demonstrate the efficacy of GTNN-SCI by calculating correlation and binding energies for representative molecules including N<sub>2</sub>, H<sub>2</sub>O, and C<sub>2</sub> using both Gaussian (cc-pVDZ) and plane-wave basis sets, achieving faster convergence and lower energies than previously reported neural network-based selected CI techniques. Most significantly, our generative approach identifies higher-order excitations missed by conventional coupling schemes, yielding lower variational energies than established methods including heat-bath CI. This capability enables GTNN-SCI to accurately treat the strongly correlated [Fe<sub>2</sub>S<sub>2</sub>(SCH<sub>3</sub>)<sub>4</sub>]<sup>2-</sup> ([2Fe-2S]) cluster system, achieving ground-state energies within chemical accuracy of DMRG benchmarks, whereas conventional selected CI methods have failed on this system. The GTNN-SCI method thus combines modern deep learning with high-performance electronic structure computation, providing an efficient and precise avenue for solving the electronic Schrödinger equation in challenging molecular systems. Artificial neural network ; Coupled cluster ; Gaussian ; Quantum ; Speedup ; Quantum chemistry ; Full configuration interaction ; Generative grammar ; Kriging Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Jingdan Chen | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2025-06-25)
Field: Materials Science — Materials Chemistry

Radical Reactivity Ratio Predictions for Copolymers with an Interpretable Machine Learning Model Accurate predictions of reactivity ratios (RRs) are crucial for understanding and controlling copolymerization kinetics and the resulting copolymer microstructure. While various methods have been proposed for RR prediction, prior efforts have been limited by a lack of data accessibility, model interpretability, and out-of-distribution performance on new chemical spaces. We address these challenges by assembling a dataset of copolymer RRs extracted from the experimental literature and then developing a machine learning model that demonstrates robustness in predicting RRs for diverse monomers and radical chemistries. SHAP analysis of the machine learning model reveals the significant role of frontier molecular orbital interactions, corroborating earlier RR prediction models emphasizing the bipolar reactivity of radicals in copolymerization. Importantly, this interpretable machine learning model leads to an intuitive argument based on the relative chemical potential and chemical hardness of comonomers that enables predictions of copolymerization regimes based on simple density functional theory. Copolymer ; Reactivity (psychology) ; Computer science ; Artificial intelligence ; Chemistry ; Materials science ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Han Liu | Liantang Li
Source: Advanced Intelligent Discovery (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning Data‐driven modeling of polymer processing–property landscape is key to accelerate polymeric product designs with tailored properties. However, it remains lack of rationales to properly construct a processing–property database balancing the modeling accuracy and the sampling cost, challenged by the landscape dimensionality and complexity. Here, inspired by the practicality of Taguchi sampling strategy, we introduce a Taguchi–Bayesian database framework of sampling strategies and principles, in order to maximize the polymer database informativity at minimum sampling cost. This sampling methodology follows a four‐step practice principle, including a sequential (and alternating) strategy of mesh‐grid‐, orthogonality‐, uniformity‐, and representativeness‐type sampling, which exhibits a sequentially flattened scaling law of minimum samplings with landscape resolution and dimensionality, enabling the state‐of‐the‐art balanced reconstruction efficiency and accuracy for polymer processing–property landscape. Overall, this article establishes a database construction roadmap to navigate sampling in polymer processing–property landscape and generically to various materials database designs. Sampling (signal processing) ; Construct (python library) ; Key (lock) ; Curse of dimensionality ; Scaling ; Product (mathematics) Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Winston Gee | Abigail G. Doyle | Santiago Vargas | Anastassia N. Alexandrova
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes Here we evaluate the robustness and utility of quantum mechanical descriptors for machine learning with transition metal complexes. We utilize <i>ab initio</i> information from the quantum theory of atoms-in-molecules (QTAIM) for 60 k transition metal complexes at multiple levels of theory (LOT), presented here in the tmQM+ dataset, to inform flexible graph neural network (GNN) models. We evaluate these models with several experiments, including training on limited charge and elemental compositions and testing on unseen charges and elements, as well as training on smaller portions of the dataset. Results show that additional quantum chemical information improves performance on unseen regimes and smaller training sets. Furthermore, we leverage the tmQM+ dataset to analyze how QTAIM descriptors vary across different LOT and probe machine learning performance with less computationally expensive LOT. We determine that <i>ab initio</i> descriptors provide benefits across LOT, thereby motivating the use of lower-level DFT descriptors, particularly for predicting expensive or experimental molecular properties. Leverage (statistics) ; Artificial neural network ; Robustness (evolution) ; Quantum ; Graph ; Quantum chemical ; Graph theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Santiago Vargas | Winston Gee | Anastassia N. Alexandrova | Abigail G. Doyle
Source: ChemRxiv (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

Multi-level QTAIM-Enriched Graph Neural Networks for Resolving Properties of Transition Metal Complexes Here we evaluate the robustness and utility of quantum mechanical descriptors for machine learning with transition metal complexes. We utilize ab initio information from the quantum theory of atoms-in-molecules (QTAIM) for 60k transition metal complexes at multiple levels of theory (LOT), presented here in the tmQM+ dataset, to inform flexible graph neural network (GNN) models. We evaluate these models with several experiments, including training on limited charge and elemental compositions 1 and testing on unseen charges and elements, as well as training on smaller portions of the dataset. Results show that additional quantum chemical information improves performance on unseen regimes and smaller training sets. Furthermore, we leverage the tmQM+ dataset to analyze how QTAIM descriptors vary across different LOT and probe machine learning performance with less computationally expensive LOT. We determine that ab initio descriptors provide benefits across LOT, thereby motivating the use of lower-level DFT descriptors, particularly for predicting expensive or experimental molecular properties. Artificial neural network ; Transition (genetics) ; Cognitive science ; Psychology ; Computer science ; Epistemology ; Chemistry ; Artificial intelligence ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry