jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aaron D. Kaplan
Source: Structural Dynamics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Accelerating Crystal Structure Prediction with Machine Learning Forcefields Long-standing methods in materials simulation can now generally predict crystalline structure for near-/stable materials with high accuracy, and independently of local materials chemistry. However, these methods, particularly density functional theory, are electronic structure-based and scale unfavorably with material complexity. This has hindered study of, e.g., configurationally disordered and glassy materials. More, electronic structure calculations are typically run at zero temperature. To bridge the gap between non-universal atomistic simulation and electronic structure modelling, universal machine learning forcefields (MLFFs) have recently attained a level of accuracy suitable for rapid prediction of crystal structure with near electronic structure accuracy. In my talk, I'll discuss the materials databases such as the Materials Project [1] which are used to train these potentials. I will also discuss how the Materials Project's thermodynamic information, including competition between polymorphs, is crucial for determining ground-state crystal structure. I'll highlight recent advances in training universal MLFFs with purpose-built datasets like MatPES [2] which aim to make these models more efficient while retaining their level of predictive accuracy. I'll showcase a range of applications of MLFFs, including benchmarks for ground-state structure prediction, determining positions of hydrogen atoms, representing disordered crystals from ICSD, and extensions to non-crystalline / glassy-like materials through a simulated melt-quench process. Crystal structure prediction ; Electronic structure ; Crystal structure ; Bridge (graph theory) ; Crystal (programming language) ; Scale (ratio) ; Range (aeronautics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: L. Kempen | Marius Juul Nielsen | Mie Andersen
Source: arXiv (Cornell University) (preprint, 2025-04-23)
Field: Materials Science — Materials Chemistry

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure--activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the excellent catalytic performance of inverse catalysts observed in experiments. Nanoclusters ; Inverse ; Scaling ; Workflow ; Density functional theory ; Speedup ; Inverse problem Machine Learning in Materials Science ; Topic Modeling ; Complex Network Analysis Techniques
Authors: Beyza Yılmaz (21364639) | Halide Beyza Ceylan (21364642) | Gizem Yaz (21364645) | Ramazan Yıldırım (6452681)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

PerformanceAssessment of Catalyst Materials for CO<sub>2</sub> Hydrogenationto Methanol Using Explainable Machine Learning An extensive dataset containing 1547 data points from 84 published papers was constructed and analyzed using machine learning tools to assess the performance of catalyst materials (active metal and support). Random forest (RF) models were developed for the prediction of CO<sub>2</sub> conversion and methanol selectivity; the SHAP (SHapley Additive exPlanations) analysis, accompanying the RF models, was used to determine the contributions of descriptors, including the catalyst materials, to the conversion and selectivity predictions. Association rule mining analysis (ARM) was also utilized to determine the effects of individual catalyst material and active metal–support combinations on methanol selectivity and to improve the explainability of the results further. RF models for both CO<sub>2</sub> conversion and methanol selectivity were quite successful; the RMSE of training and testing were 2.81 (<i>R</i><sup>2</sup> = 0.87) and 3.74 (<i>R</i><sup>2</sup> = 0.74), respectively, for CO<sub>2</sub> conversion, and they were 7.31 (<i>R</i><sup>2</sup> = 0.94) and 12.74 (<i>R</i><sup>2</sup> = 0.80) for methanol selectivity. SHAP analysis indicated that the reaction temperature, the support type, the active metal type, and the catalyst preparation methods are the most significant descriptors for both CO<sub>2</sub> conversion and methanol selectivity; the temperature affects the conversion positively, while its effect on methanol selectivity is negative. ARM analysis for the catalyst material and preparation methods revealed that the use of Ga<sub>3</sub>Ni<sub>5</sub>, Ga, Ir, Ru, and Y improves methanol selectivity, while Nb<sub>2</sub>O<sub>5</sub>, CuBr<sub>2</sub>, In<sub>2</sub>O<sub>3</sub>–ZrO<sub>2</sub>, and ZnO–ZrO<sub>2</sub> are the best performing supports. The use of evaporation-induced self-assembly method and precipitation was found to be better to improve methanol selectivity. The ARM results also indicate that Cu–Nb<sub>2</sub>O<sub>5</sub>, Ga–ZnO–ZrO<sub>2</sub>, Ru–In<sub>2</sub>O<sub>3</sub>, and Y–ZrO<sub>2</sub> pairs promote high selectivity, while preparing Cu-based catalysts by precipitation and Ru-based catalyst with deposition-precipitation methods appears to be beneficial. The use of bimetallic Cu-InO<sub>2</sub>, Y–In<sub>2</sub>O<sub>3</sub>, La–In<sub>2</sub>O<sub>3</sub>, and Zn–ZrO<sub>2</sub> catalysts seems to be also beneficial. Methanol ; Catalysis ; Selectivity ; Precipitation ; Random forest Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Dmytro Tymoshchuk | Oleh Yasniy
Source: Measuring and computing devices in technological processes (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

MODELLING OF HYSTERESIS BEHAVIOUR OF NICKEL-TITANIUM SHAPE MEMORY ALLOY USING ARTIFICIAL NEURAL NETWORK Shape memory alloys (SMAs) are a class of materials that have the ability to return to their previous shape when exposed to temperature or mechanical stress. The main functional properties of these alloys, the shape memory effect (SME) and superelasticity (SE), make them indispensable in various industries. The SMA superelasticity is the ability of a material to return to its original shape after loading and unloading due to transformations between austenite and martensite. These phase transitions are accompanied by hysteresis, which can be observed in the stress-strain diagram. In this study, the hysteresis behavior of SMA, particularly nickel-titanium alloy (NiTi or Nitinol), was modeled using artificial neural networks. The use of neural networks in the study made it possible to obtain accurate material strain predictions and reduce the number of actual experiments. The results showed the high accuracy of the prediction model, which indicates the prospects of using artificial neural networks in the study of SMA characteristics. Shape-memory alloy ; Pseudoelasticity ; SMA* ; Nickel titanium ; Materials science ; Hysteresis ; Artificial neural network ; Austenite ; Martensite ; Stress (linguistics) ; Titanium alloy ; Alloy ; Metallurgy ; Composite material ; Microstructure ; Artificial intelligence ; Computer science ; Algorithm ; Condensed matter physics Shape Memory Alloy Transformations
Authors: Maliheh Shaban Tameh
Source: Journal of Chemical Information and Modeling (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm<sup>-1</sup>/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely on costly inorganic materials, limiting their broader deployment. Here, we present a machine learning (ML)-driven framework for identifying highly LWIR-transparent hydrocarbons as candidate monomers for sulfur-organic hybrid polymers. Infrared (IR) spectra are predicted by using a communicative message-passing neural network (CMPNN), and the effect of spectral broadening (γ = 4-10) is systematically analyzed to guide flexible, use-driven γ selection. LWIR window transparency (wT) is estimated from spectra and directly predicted using regression models trained on CMPNN-derived fingerprints. These interpretable models enable the extraction of structure-property relationships, revealing chemically meaningful descriptors and substructural motifs linked to high transparency and supporting the formulation of design rules for IR-transparent materials. The approach demonstrates strong predictive accuracy and generalizability across training, test, and external data sets. Notably, several ML-prioritized motifs match experimentally validated comonomers used in IR polymer fabrication, and the model further proposes new, chemically plausible scaffolds. By bypassing the cost of density functional theory (DFT), the framework enables scalable, data-driven screening and provides chemically grounded design insights for next-generation LWIR-transparent materials. Generalizability theory ; Transparency (behavior) ; Limiting ; Artificial neural network ; Infrared ; Near-infrared spectroscopy ; Thermal ; Density functional theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Stella Christodoulou | Elizabeth Santos | Romuald Poteau | Laurent Maron
Source: ChemistryEurope (article, 2025-09-13)
Field: Materials Science — Materials Chemistry

Styrene Polymerization with Group III Metallocenes: A Generative and Interpretable Catalyst Design Strategy Combining Density Functional Theory and Machine Learning The rational design of group III metallocene catalysts for styrene polymerization remains a complex challenge due to intertwined steric and electronic effects that govern monomer coordination and insertion. A generative and interpretable machine learning (ML) framework is presented, grounded in density functional theory (DFT) calculations, to predict and optimize the coordination energy (Δ H coord ) and activation barrier (Δ H act ) of the first styrene insertion step across 32 scandium and yttrium metallocenes. Key catalyst features—including Sterimol parameters, Tolman cone angle, metal‐centroid distance, buried volume, and natural metal charge—are extracted and used as descriptors in various regression models. The best‐performing ML models, notably support vector regressor‐bagging and random forest regressors, achieve sub‐2 kcal mol −1 mean absolute error for Δ H coord and 2.1 kcal mol −1 for Δ H act , exceeding the accuracy of DFT itself in some cases. SHapley Additive exPlanations analyses reveal steric parameters and metal acidity as primary drivers of catalyst performance. Using descriptor‐space optimization and generative mapping to ligand architectures, candidate ligands predicted to achieve target insertion barriers are identified, with validated DFT agreement. This integrative strategy highlights a path toward data‐driven, interpretable, and computationally efficient discovery of tailored polymerization catalysts. Through deliberate construction of both the descriptor space and the underlying chemical space, the approach remains effective in data‐light regimes, establishing a generalizable strategy for inverse design across homogeneous catalytic reactions. Density functional theory ; Polymerization ; Catalysis ; Steric effects ; Generative grammar ; Support vector machine ; Hybrid functional ; Molecular descriptor Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Radioactive element chemistry and processing
Authors: Zibo Wu (11335327) | Bohan Zhang (1597939) | Wei-Hai Fang (1268982) | Zhendong Li (357916)
Source: Figshare (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Hybrid Tensor Networkand Neural Network Quantum Statesfor Quantum Chemistry Neural network quantum states (NQS) have emerged as a powerful and flexible framework for addressing quantum many-body problems. While successful for model Hamiltonians, their application to molecular systems remains challenging for several reasons. In this work, we introduce three innovations to overcome some of the key limitations. (1) We develop a bounded-degree graph recurrent neural network (BDG–RNN) ansatz, which hybridizes the tensor network and neural network states and is more suitable to molecular electronic structure problems. As matrix product states (MPS) can be embedded into this ansatz, good initialization is possible for complex systems. (2) We introduce neural network correlators (NNCs) to further enhance expressivity and improve accuracy, without dramatically modifying the underlying variational Monte Carlo (VMC) optimization framework. Specifically, we introduce two types of restricted Boltzmann machine (RBM)-inspired correlators, namely, cos-RBM and Ising-RBM, which unlike previous correlators, such as Jastrow and real RBM, can adjust the sign structure of the wave function. (3) We introduce a semistochastic algorithm for local energy evaluation, which significantly reduces computational cost while maintaining high accuracy. Combining these advances, we demonstrate that our approaches can achieve chemical accuracy in challenging systems, including the one-dimensional hydrogen chain H<sub>50</sub>, the iron–sulfur cluster [Fe<sub>2</sub>S<sub>2</sub>(SCH<sub>3</sub>)<sub>4</sub>]<sup>2–</sup>, and a three-dimensional 3 × 3 × 2 hydrogen cluster H<sub>18</sub>. These methods are implemented in an open-source package, PyNQS (https://github.com/Quantum-Chemistry-Group-BNU/PyNQS), to advance NQS methodologies for quantum chemistry. Artificial neural network ; Quantum computer ; Boltzmann machine ; Quantum ; Initialization ; Coupled cluster ; Tensor (intrinsic definition) ; Topology (electrical circuits) ; Quantum network Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Jielan Li | Zekun Chen | Qian Wang | Han Yang | Ziheng Lu | Guanzhi Li | Shuizhou Chen | Yu Zhu | Xixian Liu | Jasper Tan | Meibo Tang | Yichi Zhou | Claudio Zeni | Andrew T. Fowler | Daniel Zügner | Robert Pinsler | Matthew K. Horton | Tian Xie | Tie‐Yan Liu | Haiguang Liu | Tao Qin | Bing Lv | Davide Donadio | Hongxia Hao
Source: ArXiv.org (preprint, 2025-03-14)
Field: Materials Science — Materials Chemistry

Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains unknown. Using deep learning accelerated crystal structure prediction and first-principles calculation, we systematically explore the thermal conductivity landscape of inorganic crystals. We brute-force over half a million ordered crystalline structures, encompassing an extensive coverage of local energy minima in binary compounds with up to four atoms per primitive cell. We confirm diamond sets the upper bound of thermal conductivity within our search space, very likely also among all stable crystalline solids at ambient conditions. We also identify over 20 novel crystals surpassing silicon in thermal conductivity, validated by density functional theory. These include a semiconductor TaN with ultrahigh thermal conductivity (~900 $\mathrm{W\cdot m^{-1}\cdot K^{-1}}$), and metallic compounds such as MnV that exhibit high lattice and electronic thermal conductivity simultaneously, a distinctive feature not observed before. These results as well as the deep learning-driven screening method, redefine the landscape of thermal transport and establish a large open-access database for future materials discovery. Thermal conductivity ; Thermal ; Semiconductor ; Diamond ; Heat transfer ; Maxima and minima ; Crystal (programming language) ; Thermal conduction Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Mori, Hodaka | Tonogai, Shunsuke | Miyazaki, Yu | Hayashi, Akihide | Takayanagi, Masayoshi
Source: arXiv (Cornell University) (preprint, 2025-11-28)
Field: Materials Science — Materials Chemistry

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and rarity of chemical events. Conventional reactive force fields such as ReaxFF require system-specific parametrization, while universal machine learning interatomic potentials (uMLIPs) exhibit limited sampling efficiency. This paper introduces a novel simulation framework integrating a uMLIP with a time-dependent bond-boost scheme. The bias potential increases monotonically with time, and the use of a unified parameter set across reaction classes enables consistent acceleration without system-specific tuning. For radical polymerization of vinyl monomers, the proposed framework reproduces characteristic trends, such as linear molecular-weight growth with conversion, initiator-concentration scaling, and relative monomer reactivity trends. For step-growth polycondensation of nylon-6,6, it captures the characteristic sharp increase in molecular weight at high conversion rates, consistent with experimental behavior. For epoxy curing at a copper substrate, it reveals interfacial ring-opening and cross-linking events, consistent with spectroscopic evidence of Cu-O-C bond formation. Overall, coupling uMLIPs with time-dependent bond boost enables practical and transferable simulations of polymerization and curing processes. The proposed framework reliably resolves mechanistic pathways and relative reactivity, offering molecular-level insights into polymer growth and interfacial adhesion. Polymerization ; Curing (chemistry) ; Polymer ; Monomer ; Epoxy ; ReaxFF ; Chemical bond Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Mori, Hodaka | Tonogai, Shunsuke | Miyazaki, Yu | Hayashi, Akihide | Takayanagi, Masayoshi
Source: ArXiv.org (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design Although polymerization and curing reactions govern the performance of advanced materials, their simulation remains challenging owing to the need for accurate, transferable potentials and rarity of chemical events. Conventional reactive force fields such as ReaxFF require system-specific parametrization, while universal machine learning interatomic potentials (uMLIPs) exhibit limited sampling efficiency. This paper introduces a novel simulation framework integrating a uMLIP with a time-dependent bond-boost scheme. The bias potential increases monotonically with time, and the use of a unified parameter set across reaction classes enables consistent acceleration without system-specific tuning. For radical polymerization of vinyl monomers, the proposed framework reproduces characteristic trends, such as linear molecular-weight growth with conversion, initiator-concentration scaling, and relative monomer reactivity trends. For step-growth polycondensation of nylon-6,6, it captures the characteristic sharp increase in molecular weight at high conversion rates, consistent with experimental behavior. For epoxy curing at a copper substrate, it reveals interfacial ring-opening and cross-linking events, consistent with spectroscopic evidence of Cu-O-C bond formation. Overall, coupling uMLIPs with time-dependent bond boost enables practical and transferable simulations of polymerization and curing processes. The proposed framework reliably resolves mechanistic pathways and relative reactivity, offering molecular-level insights into polymer growth and interfacial adhesion. Polymerization ; Curing (chemistry) ; Polymer ; Monomer ; Epoxy ; ReaxFF ; Chemical bond Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Chuan-Yung Chang | Yi-Ling Wu | Jhih-Jheng Ke | Hui-Hsu Tsai
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accurate and Generalizable Graph Deep Learning for Accelerating Predictive Discovery of Efficient Organic Photovoltaics with Expandable Molecular Fragments Chemical space ; Organic solar cell ; Deep learning ; Ternary operation ; Virtual screening ; Photovoltaic system ; Photovoltaics ; Graph Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Synthesis and Properties of Aromatic Compounds
Authors: Michelle J. S. Spencer | Olexandr Isayev | David A. Winkler | Sherif Abdulkader Tawfik
Source: UNC Libraries (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Predicting Thermal Properties of Crystals Using Machine Learning Calculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science. This problem is solved using complementary machine learning methods that rapidly and reliably recapitulate entropy, specific heat, effective polycrystalline dielectric function, and a non‐vibrational property (band gap) for materials calculated by accurate but lengthy QM methods. The materials are described mathematically using property‐labeled materials fragment descriptors. The machine learning models predict the QM properties with root mean square errors of 0.31 meV per atom per K for entropy, 0.18 meV per atom per K for specific heat, 4.41 for the trace of the dielectric tensor, and 0.5 eV for band gap. These models are sufficiently accurate to allow rapid screening of large numbers of crystal structures to accelerate material discovery. Dielectric ; Thermal ; Atom (system on chip) ; Crystal (programming language) ; TRACE (psycholinguistics) ; Property (philosophy) ; Crystallite Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal Expansion and Ionic Conductivity