jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gustavo Lunardon Quilló | Satyajeet Bhonsale | A. Collas | Jan Van Impe | Christos Xiouras
Source: Crystal Growth & Design (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Hybrid Semi-mechanistic and Machine Learning Solubility Regression Modeling for Crystallization Process Development Solubility regression modeling is foundational for several chemical engineering applications, particularly for crystallization process development. Traditionally, these models rely on parametric semimechanistic approaches such as the Van’t Hoff Jouyban-Acree (VH-JA) cosolvency model. Although these models generally provide narrow prediction intervals, they can exhibit increased bias when dealing with significant solute heat capacities or complex mixture effects. This study explores machine learning, including Random Forests, Support Vector Machines, Gaussian Process Regression, and Neural Networks, as potential alternatives. While most machine learning models offered a lower training error, it was observed that their predictive quality quickly deteriorates further from the training data. Hence, a hybrid approach was explored to leverage the low bias of machine learning and the low variance of the VH-JA model through heterogeneous locally weighted bagging ensembles. Key to the methodology is quantifying, tracking, and minimizing the uncertainty using the ensemble. This approach was illustrated through a case study on the solubility of ketoconazole in binary mixtures of 2-propanol and water. The optimal hybrid ensemble, comprising of 58% stepwise VH-JA models and 42% machine learning models, reduced the training root-mean-squared error and maximum absolute percentage error by ≈30% compared to the full VH-JA, while preserving a comparable prediction interval. Crystallization ; Solubility ; Process (computing) ; Process development ; Process engineering ; Biological system ; Computer science ; Biochemical engineering ; Chemistry ; Materials science ; Organic chemistry ; Engineering Crystallization and Solubility Studies ; Metallurgical Processes and Thermodynamics ; Iron and Steelmaking Processes
Authors: Nicholas A. Jose | Alexei A. Lapkin
Source: Nature Chemical Engineering (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

A roadmap toward closed-loop autonomous experimentation for engineered nanomaterials Commercialization ; Automation ; Nanomaterials ; Key (lock) ; Production (economics) Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Nanotechnology research and applications
Authors: Jianhui Chen | Xiaoxia Chen | Bo Xu | Libin Chen | Chenyu Wang | Cuilian Wen | Xushan Zhao | Musheng Wu | Baisheng Sa | Chuying Ouyang
Source: Energy storage materials (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Revealing the intrinsic structure-property relationship in hard carbon during (de)sodiation: Insights from direct atomic simulations with neural network potentials Materials science ; Artificial neural network ; Property (philosophy) ; Chemical physics ; Carbon fibers ; Nanotechnology ; Physics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Adam Foster | Zeno Schätzle | P. Bernát Szabó | Lixue Cheng | Jonas Köhler | Gino Cassella | Nicholas Gao | Jiawei Li | Frank Noé | Jan Hermann
Source: ArXiv.org (preprint, 2025-06-24)
Field: Chemistry — Spectroscopy

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species. Multireferential methods in particular suffer from large computational cost, which under the normal paradigm has to be paid anew for each system at a full price, ignoring commonalities in electronic structure across molecules. Quantum Monte Carlo with deep neural networks (deep QMC) uniquely offers to exploit such commonalities by pretraining transferable wavefunction models, but all such attempts were so far limited in scope. Here, we bring this new paradigm to fruition with Orbformer, a novel transferable wavefunction model pretrained on 22,000 equilibrium and dissociating structures that can be fine-tuned on unseen molecules reaching an accuracy-cost ratio rivalling classical multireferential methods. On established benchmarks as well as more challenging bond dissociations and Diels-Alder reactions, Orbformer is the only method that consistently converges to chemical accuracy (1 kcal/mol). This work turns the idea of amortizing the cost of solving the Schrödinger equation over many molecules into a practical approach in quantum chemistry. Wave function ; Electronic structure ; Quantum ; Quantum Monte Carlo ; Ab initio ; Quantum chemistry ; Foundation (evidence) ; Chemical bond Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Inorganic and Organometallic Chemistry
Authors: Łukasz Górski | Małgorzata Jakubowska
Source: Applied Sciences (article, 2025-11-13)
Field: Chemistry — Analytical Chemistry

Automatic Baseline Correction of 1D Signals Using a Parameter-Free Deep Convolutional Autoencoder Algorithm Baseline correction techniques are highly applicable in analytical chemistry. Consequently, there is a constant demand for universal and automated baseline correction methods. Our new procedure, based on the Convolutional Autoencoder (ConvAuto) model and combined with an automated implementation algorithm (ApplyModel procedure), meets these expectations. The key advantage of this approach is its ability to handle 1D signals of various lengths and resolutions, which is a common limitation encountered in deep neural network models. The proposed procedure is fully automatic and does not require any parameter optimization. As our experiments show, the ApplyModel procedure can also be easily combined with other baseline correction methods that utilize deep neural networks, such as the ResUNet model, which also extends its practical applicability. The usability of our new approach was tested by implementing it for both simulated and experimental signals, ranging from 200 to 4000 points in length. For complex signals characterized by multiple peaks and a nonlinear background, the ConvAuto model achieved an RMSE of 0.0263, compared to 1.7957 for the ResUNet model. In the determination of Pb(II) in a certified reference material, a recovery of 89.6% was obtained, which was 1% higher than that achieved with the ResUNet model. Autoencoder ; Baseline (sea) ; Convolutional neural network ; Key (lock) ; Constant (computer programming) ; Pattern recognition (psychology) ; Ranging Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: Ronghai Wu | Lei Zeng | Z. Shangguan | Yuxin Zhang | Zichao Peng | Xu‐Qing Wang | Heng Li
Source: International Journal of Plasticity (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Predicting dislocation patterns and discovering the law of similitude: Machine learning based on fully reversed fatigue of FCC metals Misorientation ; Dislocation ; Crystal (programming language) ; Critical resolved shear stress ; Similitude ; Shear (geology) ; Grain boundary Microstructure and mechanical properties ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Dariush Ebrahimibagha | Mallar Ray | Shubhabrata Datta
Source: Journal of Materials Engineering and Performance (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

An Insight into Polyaniline/Carbon Nanotube Thermoelectric Nanocomposite by Genetic Algorithm Polyaniline ; Materials science ; Carbon nanotube ; Nanocomposite ; Thermoelectric effect ; Nanotechnology ; Nanotube ; Composite material ; Polymer Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Conducting polymers and applications
Authors: Jaewan Lee | Chang-Young Park | Hongjun Yang | Sungbin Lim | Lim, Woohyung | Han, Sehui
Source: ArXiv.org (preprint, 2025-02-06)
Field: Materials Science — Materials Chemistry

CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction Recent advancements in graph neural networks (GNNs) have significantly enhanced the prediction of material properties by modeling crystal structures as graphs. However, GNNs often struggle to capture global structural characteristics, such as crystal systems, limiting their predictive performance. To overcome this issue, we propose CAST, a cross-attention-based multimodal model that integrates graph representations with textual descriptions of materials, effectively preserving critical structural and compositional information. Unlike previous approaches, such as CrysMMNet and MultiMat, which rely on aggregated material-level embeddings, CAST leverages cross-attention mechanisms to combine fine-grained graph node-level and text token-level features. Additionally, we introduce a masked node prediction pretraining strategy that further enhances the alignment between node and text embeddings. Our experimental results demonstrate that CAST outperforms existing baseline models across four key material properties-formation energy, band gap, bulk modulus, and shear modulus-with average relative MAE improvements ranging from 10.2% to 35.7%. Analysis of attention maps confirms the importance of pretraining in effectively aligning multimodal representations. This study underscores the potential of multimodal learning frameworks for developing more accurate and globally informed predictive models in materials science. Property (philosophy) ; Fusion ; Computer science ; Artificial intelligence ; Natural language processing ; Linguistics ; Philosophy Machine Learning in Materials Science
Authors: Jacob Toney | Roland St. Michel | Aaron Garrison | Ilia Kevlishvili | Heather J. Kulik
Source: ChemRxiv (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Identifying Dynamic Metal–Ligand Coordination Modes with Ensemble Learning Knowledge of how a ligand coordinates a metal is essential for mechanistic and data-driven studies of transition metal complexes (TMCs), but most analyses assume a single binding interaction for a given metal–ligand pair. This assumption neglects hemilabile ligands defined by dynamic coordination and highly relevant in catalysis. In this work, we curate datasets of hemilabile and nonhemilabile ligands from experimentally characterized structures in the Cambridge Structural Database, analyze trends in observed coordination modes, and introduce four new exhaustive and mutually exclusive classes of hemilability. Using these labeled datasets, we train graph neural networks to classify hemilabile ligands with high accuracy, precision, and recall and develop an ensemble algorithm which predicts primary and alternative chemically plausible coordination modes from SMILES strings in an end-to-end fashion. We demonstrate the utility of our algorithm by generating novel TMCs in predicted coordination modes and calculating the corresponding energy difference due to changes in coordination (i.e., ΔE_c) with density functional theory. Validating our novel TMCs against a ΔE_c benchmark of experimentally observed TMCs reveals strong agreement. We anticipate our open-source workflows will accelerate automated TMC screening by proposing realistic metal–ligand coordination outcomes of synthesized organometallic compounds. Ligand (biochemistry) ; Computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Mandi Fang | Yinqiao Zhang | Zheyong Fan | D. S. K. Tan | Xiaoyong Cao | Chunlei Wei | Nan Xu | Yi He
Source: npj Computational Materials (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint Accurate prediction of molecular polarizability is essential for understanding electrical, optical, and dielectric properties of materials. Traditional quantum mechanical (QM) methods, though precise for small systems, are computationally prohibitive for large-scale systems. In this work, we proposed an efficient approach for calculating molecular polarizability of condensed-phase systems by embedding atomic polarizability constraints into the tensorial neuroevolution potential (TNEP) framework. Using n-heneicosane as a benchmark, a training data set was constructed from molecular clusters truncated from the bulk systems. Atomic polarizabilities derived from semi-empirical QM calculations were integrated as training constraints for its balance of computational efficiency and physical interpretability. The constrained TNEP model demonstrated improved accuracy in predicting molecular polarizabilities for larger clusters and condensed-phase systems, attributed to the model’s refined ability to properly partition molecular polarizabilities into atomic contributions across systems with diverse configurational features. Results highlight the potential of the TNEP model with atomic polarizability constraint as a generalizable strategy to enhance the scalability and transferability of other atom-centered machine learning-based polarizability models, offering a promising solution for simulating large-scale systems with high data efficiency. Polarizability ; Transferability ; Constraint (computer-aided design) ; Phase (matter) ; Computer science ; Chemical physics ; Materials science ; Machine learning ; Physics ; Quantum mechanics ; Engineering ; Mechanical engineering ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Arjun Neyyathala | Edvin Fako | Sandip De | Daria Gashnikova | Florian Maurer | Jan‐Dierk Grunwaldt | Stephan A. Schunk | Schirin Hanf
Source: Small Structures (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts Hydroformylation Catalysts Supported RhxSy systems, particularly Rh17S15/SiO2, outperform metallic Rh catalysts in liquid-phase hydroformylation reactions. Machine learning, density functional theory, and structural analysis show that the incorporation of sulfur enhances the surface catalytic properties by improving substrate affinity. Consequently, Rh-rich terminations in Rh17S15 create highly reactive, spatially isolated Rh sites with tailored geometric and electronic properties, driving the superior catalytic performance. More details can be found in article number 2400260 by Sandip De, Schirin Hanf, and co-workers. Hydroformylation ; Rhodium ; Catalysis ; Metal ; Chemistry ; Promoter ; Organic chemistry ; Biochemistry ; Gene Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Guodong Zou | Hengyuan Zhang | Sen Li | Y. Zhang | Z.Q. Fang | Jinyu Li | Zhigang Xu | Tianyue Zhang | Yangyang Wang | Carlos Fernández | Qiuming Peng
Source: Journal of Energy Chemistry (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

MBenes on the rise: progress, prospects, and multifunctional applications Scalability ; Emerging technologies ; Key (lock) ; Work (physics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Cecile Valsecchi | Jose A. Arjona-Medina | Natalia Dyubankova | Ramil Nugmanov
Source: Journal of Cheminformatics (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks. Benchmarking ; Computer science ; Transformer ; Graph ; Conformational isomerism ; Context (archaeology) ; Data mining ; Machine learning ; Chemistry ; Theoretical computer science ; Engineering ; Molecule ; Electrical engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Connor Edwards | Jack D. Evans
Source: Advanced Theory and Simulations (article, 2025-10-29)
Field: Chemistry — Inorganic Chemistry

Exploring Foundational Machine Learned Potentials for Treating the High Temperature Dynamics of Metal‐Organic Frameworks Abstract Metal‐organic framework (MOF) derived materials, formed through high temperature processes, show great potential as catalysts, but structure‐property relationships between initial MOFs and final catalysts remain poorly understood due to characterization challenges. Current simulation methods like ab initio molecular dynamics are too computationally intensive for pyrolysis studies. Neural network approaches for learning interatomic potentials from density functional theory (DFT) offer a solution. Pyrolysis of CALF‐20 and ZIF‐8 is explored using machine learned potentials (MLPs) that can simulate high‐temperature decomposition with near‐DFT accuracy. Random sampling and two biased sampling techniques are tested to sample zinc coordination number and bond length phase space. Biased methods significantly outperformed random sampling, successfully recreating DFT simulation environments. Using this model, nanosecond‐scale quenches of both MOFs are simulated at high temperatures, revealing atomistic details of gas formation, zinc coordination changes, and linker decomposition. This demonstrates the potential for MLPs simulating complex high‐temperature MOF processes to understand reactivity and predict features for new catalytic materials. Molecular dynamics ; Ab initio ; Sampling (signal processing) ; Artificial neural network ; Decomposition ; Interatomic potential ; Phase (matter) ; Density functional theory ; Dynamics (music) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Weiyi Xia | Wei-Shen Tee | P. C. Canfield | F. A. Garcia | R. A. Ribeiro | Yongbin Lee | Liqin Ke | Rebecca Flint | Cai Zhuang Wang
Source: Physical Review Materials (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs The discovery of novel quantum materials within ternary phase spaces containing antagonistic pairs such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mo>(</a:mo><a:mi mathvariant="normal">X</a:mi><a:mo>=</a:mo><a:mi>Bi</a:mi><a:mo>,</a:mo><a:mo> </a:mo><a:mi>Pb</a:mi><a:mo>,</a:mo><a:mo> </a:mo><a:mi>In</a:mi><a:mo>,</a:mo><a:mo> </a:mo><a:mtext>and</a:mtext><a:mo> </a:mo><a:mi>Ag</a:mi><a:mo>)</a:mo></a:mrow></a:math> ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance of Ce-based intermetallic compounds in technological applications. Materials science ; Ternary operation ; Crystallography ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Jie Chen | Pengfei Ou | Yuxin Chang | Hengrui Zhang | Xiaoyan Li | Edward H. Sargent | Wei Chen
Source: Journal of Mechanical Design (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Materials Discovery Using Uncertainty-Aware Constrained Bayesian Optimization With Representation Learning of High-Dimensional Inputs Abstract High-dimensional structure and composition spaces pose a fundamental challenge in materials discovery due to the lack of efficient approaches for navigating the vast and complex design space. Although machine learning (ML) has aided materials discovery, most existing ML models lack the ability to quantify epistemic uncertainty arising from limited data. Developing this capability is particularly challenging for tasks involving high-dimensional design representations, such as atomic structures. In this study, building on the Bayesian optimization (BO) framework, we propose an uncertainty-aware atomistic machine learning model, uncertainty-aware PointNet, which enables automated representation learning directly from high-dimensional design inputs, such as atomic structures, and achieves principled uncertainty quantification through the use of spectral-normalized neural Gaussian process. By utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple design criteria. We demonstrate the effectiveness of our approach in two materials discovery case studies: (1) identifying catalysts for the carbon dioxide reduction reaction and (2) designing transparent conducting materials. The results show that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria material design using constrained BO, leading to a significant reduction of computing power and time (a 10× reduction in required simulation calculations). Beyond the demonstration examples, the developed method can accelerate materials discovery for various other applications with high-dimensional design inputs and expensive physics-based simulations. Bayesian optimization ; Representation (politics) ; Reduction (mathematics) ; Uncertainty quantification ; Bayesian probability ; Feature (linguistics) ; Gaussian process ; Bayesian network ; Uncertainty reduction theory Machine Learning in Materials Science ; Manufacturing Process and Optimization