jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sajid Mannan | Rupert J. Myers | Rohit Batra | Rocio Mercado | Lothar Wondraczek | N. M. Anoop Krishnan
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Sustainable Materials Discovery in the Era of Artificial Intelligence Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current AI workflows optimize performance first, deferring sustainability to post synthesis assessment. This creates inefficiency by the time environmental burdens are quantified, resources have been invested in potentially unsustainable solutions. The disconnect between atomic scale design and lifecycle assessment (LCA) reflects fundamental challenges, data scarcity across heterogeneous sources, scale gaps from atoms to industrial systems, uncertainty in synthesis pathways, and the absence of frameworks that co-optimize performance with environmental impact. We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment. The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning glass, cement, semiconductor photoresists, and polymers demonstrate both necessity and feasibility while identifying material-specific integration challenges. Realizing ML-LCA demands coordinated advances in data infrastructure, ex-ante assessment methodologies, multi-objective optimization, and regulatory alignment enabling the discovery of materials that are sustainable by design rather than by chance. Sustainability ; Workflow ; Scale (ratio) ; Upstream (networking) ; Big data ; Bridging (networking) ; Scarcity ; Knowledge extraction Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Catalysis and Oxidation Reactions
Authors: Merbubu Sydykova | Zh. Mambetova | Zh. Ubaidyldaeva
Source: Bulletin of Science and Practice (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Fundamentals of Working with TensorFlow: Syntax and Basic Operations The article is devoted to the fundamentals of working with the TensorFlow library, one of the most popular and functional platforms for creating and training machine learning and deep learning models. The paper provides a detailed examination of the key concepts underlying the computational architecture of TensorFlow, including the principles of forming and processing tensors, their properties, data types, dimensions, and ways of representing them within the computational graph. Special attention is given to basic tensor operations such as arithmetic computations, aggregation functions, matrix multiplication, and the broadcasting mechanism, which plays an important role in optimizing computations and building models. The article further explains the differences between variables and constants in TensorFlow, their roles in the process of training neural networks, and the methods used to modify or update tensor values. Emphasis is placed on how TensorFlow’s data structures enable efficient automatic differentiation and optimization of model parameters. The material is accompanied by practical exercises and code examples designed for beginning researchers and students learning machine learning technologies. The presented examples demonstrate the creation of simple computational models, the use of core TensorFlow operations, and the connection between theoretical principles and their programmatic implementation. The article can serve as an introductory guide for those taking their first steps in studying TensorFlow and seeking to develop confident practical skills with this library. Process (computing) ; Syntax ; Code (set theory) ; Simple (philosophy) ; Tensor (intrinsic definition) ; Computation ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Graph Neural Networks
Authors: Kati Asikainen | M. Alatalo | Marko Huttula | Assa Aravindh Sasikala Devi
Source: arXiv (Cornell University) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO$_2$ Monolayer Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can be achieved, even with a limited number of training examples, provided the dataset is compact and and grounded in physically relevant quantities. By combining density functional theory calculations with a machine-learning framework, we construct accurate descriptor-based models to predict the formation energies of doped lepidocrocite TiO$_2$ monolayers. The predictive accuracy of machine-learning models was first evaluated for single-dopant Pt configurations, demonstrating that the selected structural and chemical descriptors reliably capture the key factors governing dopant stability. Chemical transferability is then examined by extending the dataset to include Ag-doped configurations. Predictive accuracy improved systematically as additional Ag-doped data points were included in the training, while the performance of Pt remains robust. These results highlight the potential of small and well-curated datasets combined with physically informed descriptors to enable not only accurate but also chemically transferable machine-learning-driven screening in doped TiO$_2$ monolayer. Predictive power ; Dopant ; Transferability ; Predictive modelling ; Key (lock) ; Experimental data ; Density functional theory ; Training set Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Tianhao Su | Shunbo Hu | Yue Wu | Runhai Oyang | Xitao Wang | Musen Li | Jeffrey R. Reimers | Tong-Yi Zhang
Source: ArXiv.org (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

DeePAW: A universal machine learning model for orbital-free ab initio calculations Developing universal machine learning models for ab initio calculations is the frontier of materials cutting edge research in the new era of artificial intelligence. Here, we present the Deep Augment Way model (DeePAW) that is a universal machine learning (ML) model for orbital-free (OF) ab initio calculations, based on the density functional theory (DFT). DeePAW is currently the best OFDFT ML model according to the three criterions, 1) covering the largest number of elements, 2) having the widest application capability to diverse crystal structures, and 3) achieving the highest prediction accuracy without further fine-tuning. These scientific merits and innovations of DeePAW are stemmed from the novel SE(3)-equivariant double massage passing neuron networks. Besides predicting electron density distributions, DeePAW predicts formation energies of crystals as well and therefore paves an efficient avenue for multiscale materials modeling beyond conventional electronic structure calculation methods. Ab initio ; Density functional theory ; Electronic structure ; Ab initio quantum chemistry methods ; Frontier ; Enhanced Data Rates for GSM Evolution ; Deep learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Mariachiara Grande | Tommaso Gallingani | Romolo Laurita | Matteo Gherardi
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation Modular design ; Process (computing) ; Adaptability ; Automation ; Representation (politics) ; Key (lock) ; Identification (biology) ; Industry 4.0 Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Additive Manufacturing Materials and Processes
Authors: Keita Kobayashi | Hiroki Nakamura | Mitsuhiro Itakura
Source: ArXiv.org (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at low temperatures, where spin-orbit coupling produces strong coupling between spin and lattice degrees of freedom. Direct DFT simulations of magnetic phase transitions at finite temperatures are computationally prohibitive. Here, we develop a spin neural network potential (SpinNNP) that explicitly incorporates spin degrees of freedom together with spin-orbit coupling to describe the magnetic states of UO2.Reference datasets were generated using magnetic constrained DFT+U calculations with spin-orbit coupling, covering a wide range of non-collinear spin configurations. The SpinNNP accurately reproduces DFT energies, atomic forces, spin forces, and lattice constants. Machine learning molecular dynamics simulations with spin dynamics successfully capture the antiferromagnetic-paramagnetic transition. Although the predicted magnetic ground state differs from experiment due to known limitations of the underlying DFT description, the transition temperature obtained is of the correct order of magnitude compared with experiment. These results demonstrate that machine-learning potentials can enable large-scale spin-lattice simulations of actinide oxides and provide a practical route toward predictive modeling of complex magnetic materials. Coupling (piping) ; Phase transition ; Inductive coupling ; Spin (aerodynamics) ; Ground state ; Lattice (music) ; Spin engineering ; Uranium dioxide ; Range (aeronautics) Nuclear Materials and Properties ; Rare-earth and actinide compounds ; Machine Learning in Materials Science
Authors: Anton Stefan Razvan
Source: OSF Preprints (OSF Preprints) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A spatially resolved high-dimensional AFM approach-curve dataset for machine-learning based material analysis Atomic force microscopy ; Material properties ; Calibration ; Noise (video) ; Microscopy Machine Learning in Materials Science ; Composite Material Mechanics ; Microstructure and mechanical properties
Authors: A. S. Lihitkar
Source: Research Hub (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

BENCHMARKING TREE-BASED MACHINE LEARNING MODELS FOR FORMATION ENERGY PREDICTION IN PEROVSKITES Machine learning (ML) has become an important tool for accelerating materials discovery by enabling rapid prediction of material properties from existing datasets. Among various material classes, perovskites have attracted significant attention due to their applications in photovoltaics, catalysis, and electronic devices. In this work, four tree-based machine learning models are benchmarked for the prediction of formation energies of perovskite materials. These are Random Forest, Gradient Boosting, XGBoost, and LightGBM. The Matbench perovskite dataset, consisting of 18,928 crystal structures with density functional theory (DFT)-calculated formation energies, was used. Composition-based features were generated using Magpie descriptors, while structure-based features were obtained from density and symmetry properties. Models were trained using an 80/20 train– test split and evaluated with five-fold cross-validation. Among the tested models, XGBoost achieved the best performance with a test mean absolute error (MAE) of 0.227 eV/atom and an R² value of 0.79. The results indicate that boosting-based tree models outperform bagging-based approaches and demonstrate the importance of structural descriptors for accurate prediction of perovskite formation energies. Perovskite (structure) ; Benchmarking ; Random forest ; Energy (signal processing) ; Decision tree ; Predictive modelling ; Ensemble learning ; Crystal (programming language) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Łukasz Janisiów | Marek Kochańczyk | Bartosz Michał Zieliński | Tomasz Danel
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Enhancing Chemical Explainability Through Counterfactual Masking Molecular property prediction is a crucial task that guides the design of new compounds, including drugs and materials. While explainable artificial intelligence methods aim to scrutinize model predictions by identifying influential molecular substructures, many existing approaches rely on masking strategies that remove either atoms or atom-level features to assess importance via fidelity metrics. These methods, however, often fail to adhere to the underlying molecular distribution and thus yield unintuitive explanations. In this work, we propose counterfactual masking, a novel framework that replaces masked substructures with chemically reasonable fragments sampled from generative models trained to complete molecular graphs. Rather than evaluating masked predictions against implausible zeroed-out baselines, we assess them relative to counterfactual molecules drawn from the data distribution. Our method offers two key benefits: (1) molecular realism that underpins robust and distribution-consistent explanations, and (2) meaningful counterfactuals that directly indicate how structural modifications may affect predicted properties. We demonstrate that counterfactual masking is well-suited for benchmarking model explainers and yields more actionable insights across multiple datasets and property prediction tasks. Our approach bridges the gap between explainability and molecular design, offering a principled and generative path toward explainable machine learning in chemistry. Counterfactual thinking ; Counterfactual conditional ; Masking (illustration) ; Property (philosophy) ; Task (project management) ; Generative model ; Generative grammar ; Data-driven Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Maryam Nurhuda | Tiong Wei Teh | Daniel M. Packwood | Niels Hansen
Source: Advanced Theory and Simulations (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization ABSTRACT Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture, however, the vast chemical and structural space of possible COFs makes conventional molecular simulation‐based screening of adsorption properties such as Henry coefficients computationally infeasible. Here, we systematically investigate the performance of several physically motivated descriptors, the sine matrix, Ewald sum matrix, smooth overlap of atomic positions (SOAP), many‐body tensor representation (MBTR), and a custom Lennard Jones‐based descriptor for predicting Henry coefficients of small gas molecules (both polar and nonpolar) in COFs using machine learning. To account for realistic variability, we construct datasets including both chemically functionalized frameworks and interlayer displaced stacking configurations, and train relatively simple models (random forests, neural networks, and gaussian process regression) on these data. By comparing predictive performance across descriptor–model combinations, we demonstrate how various physical representations capture the key factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results highlight the critical role of descriptor choice and provide physically interpretable guidance for designing efficient machine learning models, providing a foundation for scalable high throughput computational screening of COFs for gas separation applications. van der Waals force ; Stacking ; Chemical space ; Gaussian process ; Polar ; Representation (politics) ; Artificial neural network ; Tensor (intrinsic definition) ; Chemical polarity Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Yendoubouam Lalle | Yuri Kominami | Arno Germond
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Python Workflow to characterize Biological Materials and Food Processes by Hyperspectral Image Analysis coupled with Machine Learning.” Hyperspectral imaging ; Python (programming language) ; Workflow ; Traceability ; Phenomics ; Troubleshooting ; R package ; Biological data Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Biopolymer Synthesis and Applications
Authors: Luca Schaufelberger | Aline Hartgers | Kjell Jorner
Source: ArXiv.org (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis. However, reliably sampling the conformer ensembles of ring systems remains challenging. Here, we introduce PuckerFlow, a generative machine learning model that performs flow matching on the Cremer-Pople space, a low-dimensional internal coordinate system capturing the relevant degrees of freedom of rings. Our approach enables generation of valid closed rings by design and demonstrates strong performance in generating conformers that are both diverse and precise. We show that PuckerFlow outperforms other conformer generation methods on nearly all quantitative metrics and illustrate the potential of PuckerFlow for ring systems relevant to chemical applications, particularly in catalysis and drug discovery. This work enables efficient and reliable conformer generation of cyclic structures, paving the way towards modeling structure-property relationships and the property-guided generation of rings across a wide range of applications in chemistry and biology. Conformational isomerism ; Ring (chemistry) ; Matching (statistics) ; Flexibility (engineering) ; Function (biology) ; Degrees of freedom (physics and chemistry) ; Flow (mathematics) ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Luca Schaufelberger | Aline Hartgers | Kjell Jorner
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Generating Cyclic Conformers with Flow Matching in Cremer-Pople Coordinates Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis. However, reliably sampling the conformer ensembles of ring systems remains challenging. Here, we introduce PuckerFlow, a generative machine learning model that performs flow matching on the Cremer-Pople space, a low-dimensional internal coordinate system capturing the relevant degrees of freedom of rings. Our approach enables generation of valid closed rings by design and demonstrates strong performance in generating conformers that are both diverse and precise. We show that PuckerFlow outperforms other conformer generation methods on nearly all quantitative metrics and illustrate the potential of PuckerFlow for ring systems relevant to chemical applications, particularly in catalysis and drug discovery. This work enables efficient and reliable conformer generation of cyclic structures, paving the way towards modeling structure-property relationships and the property-guided generation of rings across a wide range of applications in chemistry and biology. Conformational isomerism ; Ring (chemistry) ; Matching (statistics) ; Flexibility (engineering) ; Function (biology) ; Degrees of freedom (physics and chemistry) ; Flow (mathematics) ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Surajit Das | Raghunathan Ramakrishnan
Source: The Journal of Chemical Physics (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Enhancing NMR shielding predictions of atoms-in-molecules machine learning models with neighborhood-informed representations Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic variants of the Coulomb matrix (aCM) and bag-of-bonds (aBoB) descriptors and extend them using radial basis functions (RBFs) to yield smooth, per-atom representations (aCM-RBF and aBoB-RBF). Local structural information is incorporated by augmenting each atomic descriptor with contributions from the n nearest neighbors, resulting in the family of descriptors, aCM-RBF(n) and aBoB-RBF(n). For 13C shielding prediction on the QM9NMR dataset (831 925 shielding values across 130 831 molecules), aBoB-RBF(4) achieves an out-of-sample mean error of 1.69 ppm, outperforming models reported in previous studies. While explicit three-body descriptors further reduce errors at a higher cost, aBoB-RBF(4) offers the best balance of accuracy and efficiency. Benchmarking on external datasets comprising larger molecules (GDBm, Drug12/Drug40, and pyrimidinone derivatives) confirms the robustness and transferability of aBoB-RBF(4), establishing it as a practical tool for ML-based NMR shielding prediction. Electromagnetic shielding ; Robustness (evolution) ; Benchmarking ; Coulomb ; Transferability ; Support vector machine ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality