jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eddah K. Sure | Xing Wu | Quan QIAN
Source: Journal of Cheminformatics (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Capsule graph networks for accurate and interpretable crystalline materials property prediction Accurate and interpretable modeling of crystalline materials is essential for understanding the structure-property relationships in materials critical in accelerating materials discovery. While recent graph neural networks (GNNs) have achieved high predictive accuracy, they often struggle to provide physical interpretability and fail to explicitly model the hierarchical and symmetrical nature of crystals. In this work, we introduce Capsule Graph Networks with E(3)-Equivariance (CGN-e3), a novel deep learning framework that integrates equivariant message passing with capsule networks to capture both geometric symmetries and hierarchical motif structures. CGN-e3 leverages E(3)-equivariant message passing to learn physically consistent features and organize them into capsule representations that can disentangle local motifs, such as polyhedral environments, and connects them to global properties. We validate the effectiveness of our framework on bandgap and formation energy prediction, as well as material classification using Materials Project and Matbench datasets. Our model achieves a MAE of 0.054 eV/atom and 0.379 eV on formation energy and bandgap prediction, respectively, outperforming CGCNN and matching the performance of MEGNet on the same dataset, while also providing insightful interpretations of the learned capsule representations.Scientific contribution: We present the first integration of E(3)-equivariant graph neural networks with capsule networks for modeling crystalline materials. This unified architecture captures both the fundamental physical symmetries of 3D space; rotation, translation, reflection and the intrinsic hierarchical part-whole relationships e.g., atoms to polyhedra to extended motifs found in crystal structures. The framework provides an unsupervised pathway for interpretable motif discovery. The dynamic routing-by-agreement mechanism identifies and aggregates structurally significant local environments such as the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>T</mml:mi> <mml:mi>i</mml:mi> <mml:msub><mml:mi>O</mml:mi> <mml:mn>6</mml:mn></mml:msub> </mml:mrow> </mml:math> octahedra into higher-order graph-level capsules. This process yields human-intelligible insights by explicitly quantifying the contribution of specific structural motifs to target material properties, moving beyond "black-box" predictions. We validate our framework on key property prediction tasks and provide capsule-level interpretation of the results. Interpretability ; Graph ; Homogeneous space ; Artificial neural network ; Deep learning ; Topology (electrical circuits) ; Message passing ; Property (philosophy) ; Architecture Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: Reilly Osadchey | Kwangho Nam | Qiang Cui
Source: The Journal of Physical Chemistry B (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Toward Improving Multiple Time Step QM/MM Simulations with Δ-Machine Learning Semiempirical methods are popular for QM/MM simulations of chemical reactions in the condensed phase due to their immense speedup compared to higher-level <i>ab initio</i> or density functional theory (DFT) methods but can be much less accurate depending on the system of interest. Multiple time step (MTS) methods can improve the accuracy of semiempirical QM/MM simulations by using higher-level calculations at less frequent outer time steps, yet accurate results and efficient sampling require the low- and high-level methods to be sufficiently similar. In this work, we show the limitations of standard semiempirical methods (e.g., AM1) for MTS applications. In a condensed-phase reaction of proton transfer from water to methyl phosphate, for which DFT (B3LYP) is used as the high-level method, we can only reach an outer time step of 4, even with the stochastic isokinetic thermostat. We then explore the value of employing Δ-machine learning to enhance the efficiency of MTS QM/MM simulations. As an initial step, we train neural network potentials and Δ-learning corrections to the AM1 method for the corresponding gas-phase reaction. We show that Δ-corrections outperform ML potentials and that the amount of training data has a major impact on the accuracy and transferability of the learned correction. In a gas-phase MTS simulation with appropriate machine-learned Δ-corrections, we can reach an outer integration frequency of 25 for a nearly exact result compared to B3LYP and 30 if we accept an error of 0.3 kcal·mol<sup>-1</sup> for the free energy profile. The study validates and provides guidance to Δ-learning based MTS simulations, setting the stage for future development and realistic applications in the condensed phase. Speedup ; Transferability ; Sampling (signal processing) ; Artificial neural network ; Phase (matter) ; Energy (signal processing) ; Importance sampling ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Shashank Pathrudkar | S. Taylor | Abhishek Keripale | Abhijeet S. Gangan | Ponkrshnan Thiagarajan | Shivang Agarwal | Jaime Marian | Susanta Ghosh | Amartya Banerjee
Source: npj Computational Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Electronic structure prediction of medium and high entropy alloys across composition space Abstract We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space. Ternary operation ; Electronic structure ; Bayesian probability ; High entropy alloys ; Space (punctuation) ; Entropy (arrow of time) ; Bayesian inference ; Data space Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Jack Yang | Ziqi Yin | Sean Li
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accounting for the vibrational contribution to the configurational entropy in disordered solids with machine learned forcefields: a case study of garnet electrolyte Li <sub>7</sub> La <sub>3</sub> Zr <sub>2</sub> O <sub>12</sub> Accounting for lattice vibrations to accurately determine the phase stabilities of site-disordered solids is a long-standing challenge in computational material designs, due to the high computational cost associated with sampling the vast configurational space to obtain the converged thermodynamic quantities. One example is the garnet electrolyte Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub>, the high-temperature and high-ion-mobility cubic phase of which is disordered in its Li<sup>+</sup> site occupations, such that both the vibrational and configurational entropic contributions to its phase stability cannot be ignored. Understanding the subtle interplay between vibrational and configurational entropies in this material will therefore play a critical role in the rational manipulation of dopants and defects to stabilise cubic Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> at room temperature for practical applications. Here, by developing machine learned forcefields based on an equivariant message-passing neural network SO3KRATES, we follow a strict statistical thermodynamic protocol to quantify the phase stability of cubic Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> through structural optimisations, as well as molecular dynamics simulations at 300 and 1500 K, for a total of 70 120 configurations of cubic Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub>. Although this only covers a tiny fraction of the configurational space (∼7 × 10<sup>34</sup> configurations in total), we are able to deterministically show that the vibrational contributions to the total configurational free energy at 1500 K are significant (on the order of 1 eV per atom) in correctly ordering the stability of the cubic Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> over its tetragonal counterpart, thanks to the high data efficiency, accuracy, stability and good transferability of the transformer-based equivariant network architecture behind SO3KRATES. Therefore, our work opens up new avenues to accelerate the accurate computational designs of disordered solids, such as solid electrolytes, for technologically important applications. Electrolyte ; Entropy (arrow of time) ; Configuration entropy ; Thermodynamics ; Materials science ; Statistical physics ; Chemistry ; Condensed matter physics ; Chemical physics ; Physical chemistry ; Physics Solid-state spectroscopy and crystallography
Authors: Waqar Ahmed | Fei Wang | Kaneez Fatima | Caixin Zhang | Hui‐Xiong Deng
Source: Advances in Physics X (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

TMDCs and related vdW-heterostructures: a gap analysis of hybrid synthesis and the realization of optimal properties for practical applications, current challenges, and future prospects Two-dimensional (2D) materials like transition metal dichalcogenide (TMDC) nanosheets and their heterostructures have attracted considerable attention owing to their diverse properties. The growth of defect-free and scalable TMDCs is a major concern nowadays to utilize their real potential in a wide range of applications like nano-electronics, photonics, sensing, energy storage, optoelectronics, catalysis, and biomedicine. To overcome this issue, there is a dire need to understand the gaps and limitations in their fabrication techniques. This paper focuses on synthesis gaps and categorizes applications of TMDCs based on their ideal properties by summarizing the roadmap for their fabrication and screening, experimentally and theoretically, with special reference to density functional theory (DFT) based calculations and some basics of machine learning that support the numerical simulation in many domains. Finally, the difficulties and obstacles that arise while applying TMDCs in the real-world industry are also debated. Greater dedication is required to overcome the obstacles to fully exploit the potential of TMDCs. Exploit ; Realization (probability) ; Scalability ; Fabrication ; Ideal (ethics) ; Current (fluid) ; Gap analysis (conservation) 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Wenxia Xu | Zhen‐Hai Fu | Bo Tang | Jun Xu | Guodong Li
Source: Analytica Chimica Acta (article, 2025-09-09)
Field: Chemistry — Analytical Chemistry

A chemometric approach for FTIR-Raman-LIBS tri-modal spectral fusion: Transformer-based accurate identification of recyclable polymers Robustness (evolution) ; Scalability ; Genetic algorithm ; Deep learning ; Sorting ; Fusion ; Hyperparameter ; Sensor fusion Spectroscopy and Chemometric Analyses ; Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Liu, Gang | Alosious, Sobin | Mahajan, Subhamoy | Inae, Eric | Zhu, Yihan | Liu, Yuhan | Zhang, Renzheng | Xu, Jiaxin | Howard, Addison | Li, Ying | Luo, Tengfei | Jiang, Meng
Source: arXiv (Cornell University) (preprint, 2025-12-09)
Field: Materials Science — Materials Chemistry

Open Polymer Challenge: Post-Competition Report Machine learning (ML) offers a powerful path toward discovering sustainable polymer materials, but progress has been limited by the lack of large, high-quality, and openly accessible polymer datasets. The Open Polymer Challenge (OPC) addresses this gap by releasing the first community-developed benchmark for polymer informatics, featuring a dataset with 10K polymers and 5 properties: thermal conductivity, radius of gyration, density, fractional free volume, and glass transition temperature. The challenge centers on multi-task polymer property prediction, a core step in virtual screening pipelines for materials discovery. Participants developed models under realistic constraints that include small data, label imbalance, and heterogeneous simulation sources, using techniques such as feature-based augmentation, transfer learning, self-supervised pretraining, and targeted ensemble strategies. The competition also revealed important lessons about data preparation, distribution shifts, and cross-group simulation consistency, informing best practices for future large-scale polymer datasets. The resulting models, analysis, and released data create a new foundation for molecular AI in polymer science and are expected to accelerate the development of sustainable and energy-efficient materials. Along with the competition, we release the test dataset at https://www.kaggle.com/datasets/alexliu99/neurips-open-polymer-prediction-2025-test-data. We also release the data generation pipeline at https://github.com/sobinalosious/ADEPT, which simulates more than 25 properties, including thermal conductivity, radius of gyration, and density. Polymer ; Pipeline (software) ; Benchmark (surveying) ; Pipeline transport ; Scale (ratio) ; Core (optical fiber) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Zhuo Chen | Yuejin Yuan | Wenyang Ding | Shouhang Li | Meng An | Gang Zhang
Source: ArXiv.org (preprint, 2025-02-08)
Field: Materials Science — Materials Chemistry

Hyperparameter Optimization and Force Error Correction of Neuroevolution Potential for Predicting Thermal Conductivity of Wurtzite GaN As a representative of wide-bandgap semiconductors, wurtzite gallium nitride (GaN) has been widely utilized in high-power devices due to high breakdown voltage and low specific on resistance. Accurate prediction of wurtzite GaN thermal conductivity is a prerequisite for designing effective thermal management systems of electronic applications. Machine learning driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters. However, these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principle calculation, posing a critical challenge for their broader application. In this study, we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity, 259 W/m-K at room temperatue, achieving excellent agreement with reported experimental measurements. The hyperparameters of neuroevolution potential (NEP) were optimized based on systematic analysis of reproduced energy and force, structural feature, computational efficiency. Furthermore, a force prediction error correction method was implemented, effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit. This study provides valuable insights and hold significant implication for advancing efficient thermal management technologies in wide bandgap semiconductor devices. Hyperparameter ; Neuroevolution ; Wurtzite crystal structure ; Thermal conductivity ; Computer science ; Materials science ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Artificial neural network ; Zinc ; Composite material Thermal properties of materials ; Heat Transfer and Optimization ; GaN-based semiconductor devices and materials
Authors: Amirhossein D. Naghdi | Grzegorz Kaszuba | Stefanos Papanikolaou | Andrzej Jaszkiewicz | Piotr Sankowski
Source: Machine Learning Science and Technology (article, 2025-05-30)
Field: Materials Science — Materials Chemistry

Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data Abstract In this work, we utilize computationally efficient molecular dynamics simulations to create a machine learning pipeline for discovery of crystalline multi-component alloys. We employ high-quality interatomic potentials to create a dataset of NiFeCr structures and apply crystal diffusion variational autoencoder to maximize their mechanical properties, i.e. bulk modulus. As part of the experiment, we utilize local search coupled with classical interatomic potentials to explore the local structure space and show that utilization of this procedure greatly improves optimization capability of the neural model. We also expand the model with an extra submodule, which attains 42% improvement on modeling the crystalline phase of the structures. Ultimately, we verify the global stability of the created structures with quantum mechanical calculation methods. Autoencoder ; Pipeline (software) ; Computer science ; Entropy (arrow of time) ; Data mining ; Artificial intelligence ; Statistical physics ; Artificial neural network ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Zexi Zhang | Chengda Zhou | Yufei Chen | Yu Gu | Mao Chen
Source: Angewandte Chemie International Edition (article, 2025-10-28)
Field: Chemistry — Organic Chemistry

Copolymer Sequence Regulation Enabled by Reactivity Ratio Fingerprints via Machine Learning Sequence control is critical for tuning polymer properties in high-end applications, where reactivity ratios serve as key parameters for analyzing and regulating sequences. Nevertheless, traditional determination methods exhibit low experimental efficiency and are typically confined to two-component copolymerization. Here, we develop a machine learning platform, which leverages a novel design of "reactivity ratio fingerprints" (rFPs) to determine reactivity ratios in binary and ternary copolymerizations. Deep learning models trained on millions of rFPs enable highly efficient (millisecond-level) determination from sparse experimental data (random monomer structures, arbitrary reaction design). This approach demonstrates outstanding versatility to analyze reactivity ratios under diverse conditions (e.g., temperature, solvent). Notably, rFP-guided reaction design promotes on-demand sequence tailoring, compatible with a wide range of binary and ternary monomer combinations. Kinetic investigations and glass transition characterizations support the formation of varied sequence structures, facilitating the identification of binary and ternary azeotropic copolymerizations. This work not only unveils an attractive strategy for determining reactivity ratios but also offers a generalizable framework for sequencing complicated chain structures toward property engineering. Sequence (biology) ; Ternary operation ; Reactivity (psychology) ; Binary number ; Identification (biology) ; Copolymer ; Monomer ; Range (aeronautics) Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis
Authors: Qian Yu | Guang Hao Sun | Wei Luo
Source: ACS Materials Letters (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Prediction of Amorphization Enthalpy in Ionic Compounds Amorphous ionic materials hold promise for advanced energy storage, electrocatalysis, and optical devices, yet systematically evaluating their propensity to form amorphous phases remains underdeveloped. Here, we present a machine learning framework for efficiently predicting amorphization enthalpy, a universal measure of the thermodynamic cost of converting crystalline ionic compounds to amorphous phases. By combining a standardized DFT-based melt-and-quench protocol with machine learning, we build a training set of 407 compounds and identify key descriptors linked to the amorphization enthalpy. A random forest regressor highlights relevant features but shows a limited predictive power. To overcome this, we implement e3nn, one of the state-of-art graph neural networks (GNN), and adopt a transfer-learning approach. Applying this GNN to screen 12,123 ionic compounds reveals that nitrides and sulfides typically resist amorphization, while alkali- or halogen-rich and multi-cation compositions favor it. Our data-driven approach offers a practical guide for discovering novel amorphous ionic materials, accelerating experimental development across diverse applications. Enthalpy ; Ionic bonding ; Thermodynamics ; Materials science ; Chemistry ; Computer science ; Physics ; Ion ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices
Authors: Shrinjay Sharma | Ping Yang | Yachan Liu | Kevin Rossi | Peng Bai | Marcello Rigutto | Erik Zuidema | Umang Agarwal | R. Baur | Sofı́a Calero | David Dubbeldam | Thijs J. H. Vlugt
Source: The Journal of Physical Chemistry C (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization Shape-selective adsorption in zeolites plays a pivotal role in catalytic hydroisomerization of long-chain alkanes, a key process in producing sustainable aviation fuels from Fischer-Tropsch products. Accurately predicting adsorption behavior for the large number of alkane isomers in different zeolite frameworks is computationally intensive. To address this, we have developed a machine learning framework that rapidly and accurately predicts Henry coefficients of linear (C<sub>1</sub>-C<sub>30</sub>) and branched (C<sub>4</sub>-C<sub>20</sub>) alkanes in one-dimensional zeolites. Using descriptors based on chain length, branching patterns, and molecular graphs, we evaluate multiple ML models, including Random Forest, XGBoost, CatBoost, TabPFN, and D-MPNN in MTT-, MTW-, MRE-, and AFI-type zeolites. TabPFN and D-MPNN offer the highest predictive accuracy. Active learning further boosts model performance by efficiently selecting diverse and structurally informative isomers. We also uncover activity cliffs, where small changes in molecular structure lead to sharp variations in adsorption, and demonstrate that targeted oversampling of these cases improves model robustness. Finally, we combine the ML-predicted Henry coefficients with gas-phase thermodynamics to compute reaction equilibrium distributions for C<sub>16</sub> hydroisomerization. This integrated, data-driven approach enables efficient screening and design of shape-selective zeolite catalysts, thereby reducing the need for costly simulations. Zeolite ; Adsorption ; Alkane ; Process (computing) ; Branching (polymer chemistry) ; Key (lock) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions