jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: GuoFa Shen | Yutao Liu | Tinghong Gao
Source: Materials Chemistry and Physics (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Accelerated prediction of elastic constants in cubic crystals via interpretable machine learning: From metallic alloys to broader material systems Unary operation ; Physical metallurgy ; Hyperparameter ; Generalizability theory ; Cubic crystal system ; Aggregate (composite) ; Alloy ; Feature (linguistics) ; Constant (computer programming) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Boron and Carbon Nanomaterials Research
Authors: Woojin Shin | Kyoungmin Min
Source: Journal of Energy Storage (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential enables interface-level insights into cathode/solid electrolyte adhesion in sodium-ion batteries Adhesion ; Interatomic potential ; Electrolyte ; Molecular dynamics ; Ultimate tensile strength ; Surface energy ; Cathode ; Lattice (music) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Xiang Huang | Xizhao Xie | Congcong Luo | Bing Yao | Yongqiang Chen | Weigang Ma
Source: International Journal of Heat and Mass Transfer (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Accelerating design of synthesizable thermally conductive polymers using atomic interaction-embedded deep learning Deep learning ; Pipeline (software) ; Thermal conductivity ; Convolutional neural network ; Workflow ; Electrical conductor ; Chemical space ; Polymer Machine Learning in Materials Science ; Thermal properties of materials ; Conducting polymers and applications
Authors: Eleftherios Christofi | Hilal Reda | Vagelis Harmandaris
Source: The Journal of Chemical Physics (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Physics-aware deep learning models for predicting the heterogeneous mechanical properties of polymeric nanostructured materials Developing innovative materials with superior properties has been a major engineering challenge. Rationally designed polymer nanocomposites represent an emerging field that offers materials with enhanced mechanical properties and added functionalities. Here, we propose a computational methodology for predicting the heterogeneous mechanical behavior of model polymer nanocomposites that combines deep learning and detailed atomistic simulations. Atomistic simulations, while crucial for unraveling the mechanical behavior of composite materials, often focus on global mechanical properties by applying macroscopic strain and calculating the overall stress response. However, exploring the distributions of mechanical properties in heterogeneous polymer systems requires the computation of stress and strain fields for each atom within the simulation box. Our approach is based on a hierarchical data-driven computational framework that involves a nano/micro/macro coupling approach to predict the mechanical properties of polymer nanocomposites. We introduce a physics-aware deep learning method to predict the distribution of mechanical properties of model nanocomposites by directly computing stress and strain at the atomic level. Incorporating a set of physics-based constraints in the loss function encourages the deep learning model to follow the physical symmetries of the underlying system and to accurately predict the values of local stress and strain data in any arbitrarily chosen domain of the model systems. The proposed framework is transferable over systems with different volume fractions of nanofiller while being computationally efficient and effectively agnostic to the underlying microscopic model. Deep learning ; Multiscale modeling ; Computational model ; Polymer nanocomposite ; Field (mathematics) ; Stress (linguistics) ; Coupling (piping) ; Stress–strain curve ; Function (biology) Machine Learning in Materials Science ; Composite Material Mechanics ; Block Copolymer Self-Assembly
Authors: Renato Frazzato Viana | Juarez L. F. Da Silva | Luís Gustavo Dias | Ronaldo Cristiano Prati
Source: ACS Omega (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Pairwise Neural Networks for Ranking Molecular Structures Based on Properties The rapid discovery and design of new molecules drive innovation in science and technology, advancing energy storage, catalysis, and drug development. Traditionally, exploring chemical space involves costly quantum-chemical calculations or slow experimental screening, which limits the speed of identifying promising candidates. Machine learning has emerged as a groundbreaking approach to accelerate molecular discovery by predicting key properties directly from molecular structures. Moreover, in many cases, if we can rank molecular structures, it is not necessary to know the exact value of a molecular property. In other words, a ranker model can be useful for molecular screening. In this work, we develop a deep learning model to rank molecular structures using a siamese network approach and pairwise learning to learn the ranking. According to different properties of the QM7x and QO2Mol data sets, the results show that the performance of the learn-to-rank Siamese architecture outperforms standard pointwise regression for predicting absolute energetic properties, such as total and orbital energies, while traditional pointwise regression remains effective for derived (e.g., HOMO-LUMO gap) or nonenergy properties (e.g., dipole moment). To further validate the robustness of the proposed framework, we extended our evaluation to include the Uni-Mol molecular representation model. Experiments with Uni-Mol <i>V</i>1 and <i>V</i>2 across various model sizes (84 M to 1.1 B parameters) confirm that the pairwise learning-to-rank objective consistently outperforms standard pointwise regression, even when using highly expressive pretrained Transformer backbones. Pointwise ; Pairwise comparison ; Robustness (evolution) ; Artificial neural network ; Ranking (information retrieval) ; Representation (politics) ; Deep learning ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Nuria H. Espejo | Pablo Llombart | Andrés González de Castilla | Jorge Ramirez | Jorge R. Espinosa | Adiran Garaizar
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Thermodynamic Descriptors from Molecular Dynamics as Machine Learning Features for Extrapolable Property Prediction The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&D, where navigating uncharted chemical space to find next-generation materials or drugs is paramount. These models, reliant on structural descriptors or graph neural networks (GNNs), often fail when predicting properties for molecules with novel chemotypes. Here, we introduce a physics-augmented ML framework that overcomes this limitation. Our approach replaces conventional structural inputs with thermodynamic properties such as cohesive energy, heat of vaporization, and density, derived directly from molecular dynamics (MD) simulations. While performing comparably to structure-based models on known organic compounds, our method uniquely maintains low error when extrapolating to dissimilar chemical spaces. Crucially, it accurately predicts boiling points for entire chemical classes absent from the training set, including inorganic compounds, salts, and molecules with elements such as Si, B, and Te. By learning from the intermolecular forces that govern phase transitions, our framework provides a more fundamental and generalizable strategy for molecular property prediction, enabling chemical exploration beyond established structural domains. Property (philosophy) ; Bottleneck ; Benchmark (surveying) ; Chemical space ; Molecular dynamics ; Encoding (memory) ; Molecular descriptor ; Key (lock) ; Feature (linguistics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Chunyu Kong | Zhenrui Xiao | Yufei Pan
Source: Journal of Physics Conference Series (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties of magnesium-aluminum alloys based on machine learning Abstract Magnesium-aluminum alloys are widely used in aerospace and automotive industries due to their excellent specific strength. This study proposes a machine learning method to predict the mechanical properties of these alloys. Using a dataset of chemical composition, processing parameters, and microstructure, three algorithms (random forest, support vector machine, and neural network) were applied. The random forest model achieved the best performance with R 2 = 0.94 for tensile strength prediction and a mean absolute error of 12.3 MPa. Feature analysis identified aluminum content, solution treatment temperature, and grain size as key factors. This method provides an efficient tool for alloy design and process optimization, significantly reducing development time. Random forest ; Artificial neural network ; Aerospace ; Automotive industry ; Support vector machine ; Process (computing) ; Feature (linguistics) ; Ultimate tensile strength Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties
Authors: Yang Liu | Shiyu He | Fei Xiao | Quan Zhou | Ying Zhou | Zhu Li | Leiji Li | Yi Zeng | Xuejun Jin
Source: Journal of Materials Research and Technology (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Accelerated discovery of MRI-compatible β-Ti alloys via materials database integration and machine learning Data-driven machine learning approaches facilitate the rapid screening and evaluation of the electromagnetic properties of magnetic resonance imaging (MRI) compatible alloys across a broad compositional space. In this work, we establish a closed-loop interpretable machine learning workflow, combined with a dynamically updated materials database, to enable the rapid evaluation of the magnetic susceptibility and electrical conductivity of MRI-compatible β-Ti alloys. A cross-system materials database of MRI-compatible alloys was constructed by integrating electromagnetic data from Ti-, Zr-, Cu-, and Au-based alloys. Alloy features that may reflect the compatibility between parent and product phases, lattice distortion, and the free energy in the alloy are considered in the model. The established models were then used to evaluate the magnetic susceptibility and electrical conductivity of several new and known alloys, with subsequent optimization based on the dynamic database. Furthermore, the Shapley Additive exPlanations (SHAP) method was employed to interpret the models, revealing that shear modulus mismatch and mixing entropy are the most critical features in the prediction of magnetic susceptibility and electrical conductivity, respectively. These results provide a data-driven strategy for the evaluation and fast screening of MRI-compatible β-Ti alloys, with a deep understanding of MRI-compatibility from the perspective of physicochemical mechanisms. Data integration ; Materials processing Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes
Authors: Huicong Chen | Mingqiang Li | Zheyuan Ji | Yu Zou
Source: arXiv (Cornell University) (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Identification and Prediction of Photoplasticity in Semiconductors Using Feature Engineering and Machine learning Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the interplay of coupled multiphysics factors. Here, we conduct high-throughput nanoindentation measurements to compile a dataset of paired hardness values in dark and light conditions. Then, we engineer physics-informed descriptors spanning electrical, mechanical, and optical properties, and identify the ten most informative features, including bandgap, breakdown field, and refractive index, to enable an interpretable machine learning framework that yields transferable design rules for light-tunable semiconductor mechanics. By identifying and predicting photoplasticity in semiconductors, this work provides a practical pathway for extracting mechanism-linked, transferable guidelines to engineer light-responsive mechanical behavior in semiconductor materials and devices. Multiphysics ; Identification (biology) ; Semiconductor industry ; Semiconductor ; Feature (linguistics) ; Feature engineering ; Semiconductor device fabrication ; Work (physics) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Nanowire Synthesis and Applications
Authors: Jasper De Landsheere | Anton Zamyatin | Johannes Karwounopoulos | Esther Heid
Source: Journal of Chemical Information and Modeling (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models Modeling of chemical reactions is essential for understanding kinetic mechanisms and predicting possible outcomes of reacting systems. Quantum mechanical calculations are accurate but often prohibitively expensive. Deep learning has emerged as a faster alternative, but progress is slowed by a fragmented software ecosystem that hinders reuse, fair comparison, and reproducibility. We present ChemTorch, an open-source framework that streamlines model development, experimentation, hyperparameter tuning, and benchmarking through modular pipelines, standardized configuration, and built-in data splitters for in- and out-of-distribution evaluation. We envision ChemTorch as a foundation for community-driven method development and reproducible benchmarking in chemical reaction modeling. As a first step toward unified benchmarks, we compare four representative modalities for barrier-height prediction on the RDB7 data set, including fingerprint-, sequence-, graph-, and 3D-based approaches. Our results highlight clear advantages of structurally informed models and sharp performance drops under out-of-distribution conditions, highlighting the importance of rigorous benchmarking. Benchmarking ; Deep learning ; Modular design ; Property (philosophy) ; Hyperparameter ; Quantum chemical ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Evgeny Moerman | Adil Kabylda | Almaz Khabibrakhmanov | Alexandre Tkatchenko
Source: arXiv (Cornell University) (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

MBD-ML: Many-body dispersion from machine learning for molecules and materials Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must also be accurately included in machine-learned force fields. The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions, requiring only atomic $C_6$ coefficients and polarizabilities as input. We present MBD-ML, a pretrained message passing neural network that predicts these atomic properties directly from atomic structures. Through seamless integration with libMBD, our method enables the immediate calculation of MBD-inclusive total energies, forces, and stress tensors. By eliminating the need for intermediate electronic structure calculations, MBD-ML offers a practical and streamlined tool that simplifies the incorporation of state-of-the-art vdW interactions into any electronic structure code, as well as empirical and machine-learned force fields. van der Waals force ; Artificial neural network ; Dispersion (optics) ; London dispersion force ; Molecule ; Electronic structure Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Kirill V. Karpov | Ivan S. Pikulin | A. P. Mitrofanov | Anatoliy A. Chasovskikh | A. Yu. Konovalov | Grigory V. Bokov
Source: Journal of Chemical Information and Modeling (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Symmetry-Sensitive Analysis of Molecular Graph Neural Network Models Graph convolutional neural networks (GCNNs) have emerged as powerful tools for predicting molecular properties in chemistry. However, their black-box nature poses challenges for interpretability, hindering their widespread adoption. In this work, we propose a symmetry-sensitive method for interpreting GCNN models, aiming to provide explanations that align with chemical intuition while maintaining computational efficiency. We introduce the MolgraphX explainer method, tailored to highlight the importance of specific molecular substructures in predictions. We demonstrate the effectiveness of our approach using multiple data sets of small organic molecules with different properties. Our method offers insights into the underlying chemical mechanisms, bridging the gap between formal accuracy and chemical intuition. Through extensive experimentation, we validate the efficacy and efficiency of our proposed method, offering chemists a valuable tool for understanding and interpreting GCNN predictions for molecular chemistry applications. Bridging (networking) ; Intuition ; Convolutional neural network ; Artificial neural network ; Graph ; Molecular graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Tae Wook Yang | Seung Hyun Jo | Min Chul Suh
Source: Journal of Cheminformatics (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Privileged structure-based molecular fingerprints for organic electronic materials: towards intuitive machine learning interpretation Molecular descriptors are central to the performance and interpretability of QSPR models, yet most existing fingerprints for organic electronics lack chemical relevance or interpretability. Here, we present the Organic Electronic Fingerprint (OEFP), a structure-based representation tailored for OLED and OPV materials. OEFP was constructed from a manually curated OLED dataset and publicly available OPV and chromophore datasets to ensure structural diversity. Synthetically accessible substructures were identified using fragmentation and ring decomposition methods, which capture the conjugated π-bonds crucial for organic electronic materials, and subsequently encoded as individual bits. In case studies of OPV HOMO energy prediction, OEFP achieved up to 13.7% lower MAE and 1.6% higher R<sup>2</sup> compared to domain-mismatched structure-based fingerprint under random splits. Under scaffold-based splits designed to assess generalization, OEFP demonstrated substantially improved generalization, reducing MAE by 50-70% and increasing R<sup>2</sup> by over 150% relative to the same baseline and achieved performance comparable to ECFP baselines evaluated at a similar fingerprint length when using count-based representations. SHAP analysis further enabled intuitive substructure-level interpretation, while OEFP's chemically meaningful and synthetically relevant design supports rational molecular generation. Together, these results establish OEFP as an effective and interpretable molecular representation for machine learning applications in organic electronics. Interpretability ; Fingerprint (computing) ; Representation (politics) ; Cheminformatics ; Relevance (law) ; Molecular descriptor ; Deep learning ; Interpretation (philosophy) Synthesis and Properties of Aromatic Compounds ; Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes
Authors: C. Eagan | M. Copus | E. Iacocca
Source: ArXiv.org (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Deep learning statistical defect models on magnetic material dynamic and static properties The modeling of realistic magnetic materials requires the inclusion of defects. Based on the pseudospectral Landau-Lifshitz description of magnetisation dynamics, we propose a statistical model that takes into account defects, specifically vacancies. This statistical model can be integrated with deep learning techniques that correlate defect thresholds with relevant physical observables. We develop a convolutional neural network and a physics-informed neural network combined with theory of functional connections to predict the dispersion relation given defect parameters and physical constraints. A two-branch convolutional neural network is developed to predict domain-wall widths depending on defects threshold, taking into account the spatial profile and domain-wall width separately to achieve a prediction. The proposed physics-informed approaches leverage deep-learning and achieve statistical predictions measured in physical units. This is a stepping stone towards the discovery of new materials and the determination of minimal defect thresholds required for desired dynamics, states, or topological textures. Leverage (statistics) ; Convolutional neural network ; Artificial neural network ; Statistical learning ; Deep learning ; Statistical model ; Relation (database) ; Multiscale modeling ; Magnetization Machine Learning in Materials Science ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Henry R. N. B. Enninful
Source: ChemRxiv (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Auditing Domain Alignment in Latent Representations for Cross-Domain Materials Machine Learning Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by such models are rarely examined directly, allowing domain misalignment to remain hidden until model transfer fails. Here, we present a practical, representation-level framework for auditing domain alignment in latent spaces learned by multi-task neural networks for materials property prediction. Using porous carbon materials and metal–organic frameworks as a representative cross-domain case study, we analyze a shared latent space trained across multiple target properties and regularization strategies. Global and local dimensionality-reduction visualizations are combined with quantitative measures of domain separability and latent–target correlation structure to assess alignment quality. We find that domain-alignment objectives induce measurable geometric reorganization and partial large-scale mixing in latent space, while domain identity remains locally recoverable and physically interpretable monotonic associations between latent dimensions and target properties are preserved. These diagnostics reveal structural differences in learned representations that are not apparent from conventional performance metrics alone and enable model comparison based on latent-space geometry rather than predictive accuracy. The proposed auditing framework is model-agnostic and can be readily integrated into existing materials machine learning workflows, providing a practical tool for improving robustness and interpretability in cross-domain materials modeling. Interpretability ; Robustness (evolution) ; Domain (mathematical analysis) ; Regularization (linguistics) ; Audit ; Artificial neural network ; Property (philosophy) ; Domain adaptation Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Graph Neural Networks