jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aljoscha Felix Baumann | Daniel Mutter | Daniel F. Urban | Christian Elsässer
Source: ACS Applied Energy Materials (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

High-Throughput-Screening Workflow for Predicting Volume Changes by Ion Intercalation in Battery Materials Mechanical stresses and strains developing locally within the microstructure of active ion-battery-electrode materials during charge–discharge cycles can compromise their long-term stability. In this context, crystalline compounds exhibiting low volume changes are of particular interest. Atomistic simulations can be employed to quantify the volume change of the crystal structure upon intercalation and deintercalation of ions and to elucidate the local mechanisms underlying the global structural response. While density functional theory (DFT) offers a robust and accurate framework for such calculations, its computational cost limits its applicability for large-scale screening of diverse intercalation structures and sites. In this work, we present a workflow designed to prioritize candidate materials for subsequent detailed characterization. The workflow calculates the volume change upon intercalation using atomic-level features and a machine-learning model for bond-length prediction. The bond-length predictions are based on the assumption that bonds between the same ionic species in similar local coordination environments exhibit comparable lengths across different crystallographic structures. The model was trained on a DFT-generated data set, which inherently defines the chemical space in which reliable predictions can be expected. We demonstrate the workflow’s utility by screening approximately 1,175,000 transition-metal oxides and fluorides, followed by DFT validation of the most promising candidates. The proposed workflow enables filtering of large candidate sets and accelerates the potential discovery of low volume change intercalation materials for batteries. Workflow ; Intercalation (chemistry) ; Volume (thermodynamics) ; Ionic bonding ; Ion ; Density functional theory Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advancements in Battery Materials
Authors: Khac Gia Hoang Nguyen | Hoang Long Dang | Minh Tri Nguyen
Source: Research Square (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Soft Specialization via Mixture-of-Experts Enables Accurate Prediction of Superconducting Tc from Crystal Structures Robustness (evolution) ; Artificial neural network ; Superconductivity ; Intuition ; Graph ; Crystal structure ; Cuprate ; Interpretability Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks
Authors: Aljoscha Felix Baumann | Daniel Mutter | Daniel F. Urban | Christian Elsässer
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

High-Throughput-Screening Workflow for Predicting Volume Changes by Ion Intercalation in Battery Materials Mechanical stresses and strains developing locally within the microstructure of active ion-battery-electrode materials during charge-discharge cycles can compromise their long-term stability. In this context, crystalline compounds exhibiting low volume changes are of particular interest. Atomistic simulations can be employed to quantify the volume change of the crystal structure upon intercalation and deintercalation of ions and to elucidate the local mechanisms underlying the global structural response. While density functional theory (DFT) offers a robust and accurate framework for such calculations, its computational cost limits its applicability for large-scale screening of diverse intercalation structures and sites. In this work, we present a workflow designed to prioritize candidate materials for subsequent detailed characterization. The workflow calculates the volume change upon intercalation using atomic-level features and a machine-learning model for bond-length prediction. The bond-length predictions are based on the assumption that bonds between the same ionic species in similar local coordination environments exhibit comparable lengths across different crystallographic structures. The model was trained on a DFT-generated dataset, which inherently defines the chemical space in which reliable predictions can be expected. We demonstrate the workflow's utility by screening approximately 1,175,000 transition-metal oxides and fluorides, followed by DFT validation of the most promising candidates. The proposed workflow enables filtering of large candidate sets and accelerates the potential discovery of low volume change intercalation materials for batteries. Intercalation (chemistry) ; Workflow ; Volume (thermodynamics) ; Ionic bonding ; Ion ; Microstructure Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Zihe Cao | Huan Wang | Changli Shi | Yi Yang | Xiaolu Chen | Wenming Zhang | Zhangqi Yu | Qiang Lu | Bing Zhang
Source: Physica Scripta (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Investigating the effect of structural variations on the band gap of titanium dioxide using machine learning Abstract Predicting the band gap of titanium dioxide (TiO 2 ) is crucial for its photocatalytic applications, yet first-principles calculations of band gap properties are often computationally expensive. Machine learning offers an efficient alternative, but predictive accuracy strongly depends on how structural features are represented. In this work, we investigate the structure–property relationships governing the TiO 2 band gap by comparing different feature construction strategies. Traditional structure descriptors based on local atomic environments show limitations in effectively encoding complex structural correlations. In contrast, graph neural network (GNN) models more effectively learn structure–property relationships and achieve higher prediction accuracy. Further deconstruction of the model reveals that its predictive performance relies on a synergistic integration of chemical identity, local geometry, and non-local structural information. Feature visualization further shows that the model learns task-adaptive representations. These findings highlight the necessity of incorporating non-local structural information for predicting global properties and provide new insights into the machine learning driven materials design. Band gap ; Titanium dioxide ; Artificial neural network ; Feature (linguistics) ; Visualization ; Electronic band structure Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Khac Gia Hoang Nguyen | Hoang Long Dang | Minh Tri Nguyen
Source: ChemRxiv (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Soft Specialization via Mixture-of-Experts Enables Accurate Prediction of Superconducting Tc from Crystal Structures Predicting superconducting critical temperature (T c) from crystal structure is challenging due to diverse material families with distinct physical mechanisms and structural complexities like siteoccupation disorder. We introduce Superconductor Mixture of Experts (SuMoE), a Mixture-of-Experts (MoE) architecture using multiple Graph Neural Network experts with learned routing, achieving MAE of 0.671 logK on the SuperCon3D dataset, significantly outperforming both a state-of-the-art single generalist Graph Neural Network (0.709 logK) and isolated family-specific specialists (0.683 logK). While traditional intuition suggests models should specialize in rigid material families, our analysis reveals a more sophisticated regime of soft specialization. We find that experts specialize in underlying physical mechanisms rather than arbitrary labels, for instance, a single expert dominates predictions for both Cuprates and Heavy Fermion systems, effectively capturing the physics of strongly correlated electrons across chemically distinct families. Crucially, the model couples this specialized knowledge with a diffuse routing mechanism that synthesizes insights from multiple experts, ensuring robustness through redundancy. Our work demonstrates that for complex structure-property relationships, the most effective paradigm is not specialization but integrative prediction, where the model learns to synthesize complementary insights to form a single, robust prediction. Robustness (evolution) ; Artificial neural network ; Intuition ; Superconductivity ; Graph ; Interpretability ; Physical system ; Crystal structure Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Physics of Superconductivity and Magnetism
Authors: Roohollah Hafizi | H. Gittins | Christopher R. Taylor | Graeme M Day
Source: ChemRxiv (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Towards Foundation Models Trained from Crystal Structure Prediction of the Organic Molecular Solid State Machine-learning interatomic potentials (MLIPs) promise near ab initio accuracy at a fraction of the computational cost, unlocking atomistic simulations at scales previously inaccessible. Yet, their accuracy and generalizability remain critically limited by the quality and diversity of training data. Here, we introduce crystal structure prediction (CSP) as a systematic and scalable approach to generate diverse, physically relevant datasets tailored for MLIP development. CSP workflows inherently explore broad regions of molecular crystal energy landscapes, making them an untapped resource for high-coverage training data. Exploiting this, we generate a dataset of 40 million unique crystal structures for 1,600+ rigid and flexible organic molecules via low discrepancy, quasi-random sampling across all frequently observed space group symmetries. From this, we curate a highfidelity reference dataset, CSP-25, of 2.2 million crystal structures with density functional theory-level energies, forces, and stresses. Leveraging this dataset, we train three message passing, graph neural network foundation models, using the MACE architecture, of increasing complexity, spanning a range of cost-accuracy trade-offs. These models are rigorously evaluated on challenging downstream tasks, including unconstrained geometry optimization and energy ranking across entire crystal structure landscapes for over 1,600 molecules, providing a stringent test of both accuracy and transferability. Our results show that CSP-driven datasets enable robust, high-fidelity MLIPs that capture both local forces and global energetic ordering in molecular crystals. This work establishes CSP as a powerful paradigm for next-generation MLIP development and underscores the critical role of data diversity in achieving predictive atomistic models. Crystal structure prediction ; Scalability ; Bridging (networking) ; Workflow ; Ab initio ; Outlier ; Crystal (programming language) ; Artificial neural network ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Dipayan Sanpui | Anirban Chandra | Henry Chan | Sukriti Manna | S. Sankaranarayanan
Source: Engineering Reports (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing ABSTRACT We present an accurate estimation of the dimensions of additively manufactured components by adopting a probabilistic perspective. Our study utilizes a previously gathered experimental dataset, encompassing five crucial design features for 405 parts produced in nine production runs. These runs involved two machines, three polymer materials, and two‐part design configurations. To illustrate design information and manufacturing conditions, we employ data models that integrate both continuous and categorical factors. For predicting Difference from Target (DFT) values, we employ two machine learning approaches: deterministic models that offer point estimates and probabilistic models generating probability distributions. The deterministic models, trained on 80% of the data using Support Vector Regression (SVR), exhibit high accuracy, with the SVR model demonstrating precision close to the process repeatability. To address systematic deviations, we introduce probabilistic machine learning methodologies, namely Gaussian Process Regression (GPR) and Probabilistic Bayesian Neural Networks (BNNs). While the GPR model shows high accuracy in predicting feature geometry dimensions, the BNNs aim to capture both aleatoric and epistemic uncertainties. We explore two approaches within BNNs, with the second approach providing a more comprehensive understanding of uncertainties but showing lower accuracy in predicting feature geometry dimensions. Emphasizing the importance of quantifying epistemic uncertainty in machine learning models, we highlight its role in robust decision‐making, risk assessment, and model improvement. We discuss the trade‐offs between BNNs and GPR, considering factors such as interpretability and computational efficiency. The choice between these models depends on analytical needs, striking a balance between predictive accuracy, interpretability, and computational constraints. In summary, our analysis of an additive manufacturing dataset through the lens of a Probabilistic Bayesian Neural Network (BNN) and the simultaneous quantification of both epistemic and aleatoric uncertainties provides a robust foundation for advancing manufacturing design. Interpretability ; Uncertainty quantification ; Probabilistic logic ; Categorical variable ; Kriging ; Gaussian process ; Support vector machine ; Feature (linguistics) ; Bayesian probability ; Process (computing) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Lorenzo Piersante | Anirudh Raju Natarajan
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for solid-state precipitation Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline the parameterization of MLIPs for modeling precipitation in multi-component alloys. We developed an algorithm that enumerates symmetrically distinct transformation pathways connecting chemical decorations on different parent crystal structures. Additionally, we introduce the weighted Kendall-$τ$ coefficient and its semi-grand canonical generalization as metrics for quantifying MLIP accuracy in predicting low-temperature thermodynamics. We apply these approaches to parameterize an MLIP for a dilute Mg-Nd alloy. The resulting potential reproduces the complex early-stage precipitation behavior observed in experiment. Large-scale atomistic simulations reveal competition between order-disorder and structural transformations. Furthermore, these results suggest a continuous transition between high-symmetry hcp and bcc crystal structures during aging heat treatments. Generalization ; Precipitation ; Transformation (genetics) ; Interatomic potential ; Phase (matter) ; Phase transition Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications
Authors: Lorenzo Piersante | Anirudh Raju Natarajan
Source: ArXiv.org (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for solid-state precipitation Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline the parameterization of MLIPs for modeling precipitation in multi-component alloys. We developed an algorithm that enumerates symmetrically distinct transformation pathways connecting chemical decorations on different parent crystal structures. Additionally, we introduce the weighted Kendall-$τ$ coefficient and its semi-grand canonical generalization as metrics for quantifying MLIP accuracy in predicting low-temperature thermodynamics. We apply these approaches to parameterize an MLIP for a dilute Mg-Nd alloy. The resulting potential reproduces the complex early-stage precipitation behavior observed in experiment. Large-scale atomistic simulations reveal competition between order-disorder and structural transformations. Furthermore, these results suggest a continuous transition between high-symmetry hcp and bcc crystal structures during aging heat treatments. Generalization ; Precipitation ; Transformation (genetics) ; Interatomic potential ; Phase (matter) ; Phase transition Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications
Authors: Haseeb Ahmad
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

SparsePhysNet: Physics-Informed Sparse Attention Transformers for Sample-Efficient Molecular Property Prediction Predicting molecular properties accurately from limited labeled data remains a central bottleneck in computational drug discovery and materials science. Existing graph neural networks and molecular transformers achieve strong performance on large benchmark datasets but degrade significantly in low-data regimes, precisely where experimental annotation is most expensive. We introduce SparsePhysNet, a molecular property prediction framework that incorporates two novel inductive biases derived from first-principles chemistry: (1) a Physics-Informed Positional Encoding (PIPE) that initializes atom representations from atomic orbital theory, electronegativity, and covalent radii, and (2) a Chemical Valence Sparse Attention (CVSA) mask that restricts self-attention to chemically meaningful atom pairs, reducing complexity from O(n²) to near-linear in typical drug-like molecules. We evaluate SparsePhysNet on four MoleculeNet benchmarks under scaffold split: ESOL (1,128 samples), FreeSolv (642 samples), Lipophilicity (4,200 samples), and BBBP (2,039 samples). Against baseline models including Random Forest on Morgan fingerprints and Graph Isomorphism Networks, SparsePhysNet achieves RMSE of 1.095 ± 0.096 on ESOL, 3.037 ± 0.068 on FreeSolv, and ROC-AUC of 0.880 ± 0.016 on BBBP — outperforming the RF-FP and GIN baselines across all regression benchmarks. An ablation study confirms that both PIPE and CVSA contribute independently to performance, with the physics encoding providing the largest benefit in low-data regimes. Code and model weights are available at https://huggingface.co/sibi-ai Molecular graph ; Bottleneck ; Decoding methods ; Encoding (memory) ; Benchmark (surveying) ; Graph ; Autoencoder ; Regression ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Govinakere Mallegowda Dhanush | Vivek Hamse Kameshwar | Yarabahally R. Girish
Source: Next Materials (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Understanding and mitigating the toxicity of MXenes in emerging applications MXenes, a versatile class of two-dimensional transition-metal carbides, nitrides, and carbonitrides, exhibit exceptional properties for applications in energy, catalysis, and biomedicine. However, their rapid advancement has outpaced understanding of their biological and environmental safety. Recent findings reveal that MXene cytotoxicity is dose-, composition-, and surface-dependent rather than inherent. Ti-based MXenes show conditional biocompatibility, while V- and Cr-based variants present higher risks due to ion release. Variations in synthesis routes, flake size, and surface terminations contribute to inconsistent toxicological data. Surface engineering, green synthesis, and polymeric functionalization have emerged as effective routes to mitigate toxicity. Yet, the lack of standardized testing frameworks limits reliable structure toxicity correlations. Integration of machine learning and nano-informatics offers a path to predictive toxicology and safe-by-design strategies. Embedding toxicity assessment early in development will be vital for regulatory acceptance. Ultimately, harmonizing performance with safety will ensure MXenes’ sustainable translation into biomedical and technological applications. MXenes ; Sustainability ; Toxicity ; Risk assessment ; Sustainable development MXene and MAX Phase Materials ; Physical Unclonable Functions (PUFs) and Hardware Security ; Energetic Materials and Combustion
Authors: Pengfei Suo | Xingxing Wu | Hongzhen Tian | Lin-wang Wang
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Towards Scalable and Efficient Machine-Learning Force Fields: The MatPL package and Its Advancements on Neuroevolution Potentials The development of accurate and efficient machine-learning interatomic potentials is a transformative technology in computational materials science and molecular dynamics simulations. Here, we introduce the Materials Potential Library (MatPL), a comprehensive software framework integrating multiple machine-learning potential architectures, with a particular emphasis on advancing the neuroevolution potential (NEP) framework. MatPL incorporates gradient-based optimizers to improve training stability and accuracy. A key innovation is its highly optimized, GPU-native interface for LAMMPS, seamlessly integrated with LAMMPS’s robust Message Passing Interface (MPI) infrastructure. This design transcends the limitations of single-node GPU execution, enabling efficient cross-node scalability without compromising the computational efficiency intrinsic to NEP. MatPL is a high-performance and versatile platform for next-generation atomistic modeling, balancing between high accuracy, training efficiency, and large-scale simulation capability. Scalability ; Key (lock) ; Interface (matter) ; Software ; Neuroevolution ; Stability (learning theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly