jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nina A. Henke | Leo Luber | Ioannis Kouroudis | Jonathan Paul | Alexander Schuhbeck | Lukas M. Rescher | Tizian Lorenzen | Veronika Mayer | Knut Müller‐Caspary | Bert Nickel | Alessio Gagliardi | Alexander S. Urban
Source: Advanced Materials (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Synthesizer: Chemistry‐Aware Machine Learning for Precision Control of Nanocrystal Growth Precise and reproducible control over nanocrystal synthesis is essential for tailoring optical properties, yet remains a long-standing challenge in halide perovskites. A broadly adoptable machine learning-guided framework, the Synthesizer, is introduced that combines Gaussian Process regression and Bayesian optimization with chemistry-aware molecular encodings and systematic feature engineering. Rather than new algorithms, the advance lies in translating interpretable machine learning tools into a practical, benchtop platform for nanocrystal optimization under ambient conditions. Using CsPbBr<sub>3</sub> as a model system, nm-level precision in photoluminescence peak tuning (430 nm to 520 nm) is achieved, along with benchmark narrow linewidths down to 70 meV via lateral confinement control, and robust photoluminescence quantum yield optimization linked to surface trap density. Mapping the two-dimensional parameter space (Cs/PbBr<sub>2</sub> and antisolvent/PbBr<sub>2</sub> ratios) across multiple antisolvents enables predictive optimization and identifies the antisolvent/PbBr<sub>2</sub> ratio as a previously underappreciated mechanistic parameter, offering a quantitative basis for antisolvent-accelerated nanocrystal growth. Transfer tests across distinct chemical spaces, including alcohols and cyclopentanone, confirm generalizability to unseen molecules, while application to CsPbI<sub>3</sub> demonstrates extension to new material systems. These results establish an adoption-ready platform for data-efficient, uncertainty-aware synthesis design, providing reproducible pathways to accelerate materials discovery beyond halide perovskites. Nanocrystal ; Bayesian optimization ; Photoluminescence ; Quantum dot ; Gaussian process ; Benchmark (surveying) ; Generalizability theory ; Process (computing) ; Feature (linguistics) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Milena Petković | Lucas Vieira | Natasha Dropka
Source: Progress in Crystal Growth and Characterization of Materials (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Machine learning in crystal growth: A review of methods, data, and applications Crystal (programming language) ; Context (archaeology) ; Crystal growth ; Characterization (materials science) ; Cluster analysis ; Process (computing) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Electronic and Structural Properties of Oxides
Authors: Valentin Gradisteanu | Elliot W. Chan | Lester O. Hedges | Meritxell Malagarriga | Rolf David | Miguel de la Puente | Damien Laage | Iñaki Tuñón | Marc W. van der Kamp | Kirill Zinovjev
Source: ChemRxiv (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Simulating enzyme catalysis with electrostatically embedded machine learning potentials To simulate enzyme reactions, multiscale quantum mechanics/molecular mechanics (QM/MM) approaches are well established and popular. However, accurately and efficiently estimating enzyme activity is a challenge, because in general, precise methods are too computationally expensive. Here, we demonstrate that enzyme catalysis can be captured by coupling efficient machine-learned potentials (MLPs) for a reaction to the wider enzyme environment using electrostatic machine-learning embedding (EMLE). Our EMLE model is first applied to the natural Diels-Alderase AbyU, showing that it correctly differentiates the catalytic action on different enzyme-substrate conformations. Then, we show that training a reaction-specific EMLE model allows us to accurately capture the enzyme catalytic effects of the conversion of chorismate to prephenate, a reaction with a highly polarizable and charged transition state. In both cases, in contrast to mechanical embedding approaches, EMLE embedding allows accurate and efficient predictions of enzyme activity, agreeing with high-level QM/MM reference calculations. This approach facilitates the use of gas phase-trained MLPs in MLP/molecular mechanics (ML/MM) simulations and should thus be highly beneficial for computational activity screening of enzyme biocatalysts. Catalysis ; Computer science ; Chemistry ; Biochemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Jin An | Tailai Chen | Hossein Pouri | Tianlong Liu | Jin Zhang
Source: Polymer (article, 2025-06-13)
Field: Materials Science — Polymers and Plastics

Machine learning-assisted development of conductive polymers Electrical conductor ; Conductive polymer ; Polymer ; Polymer science ; Materials science ; Nanotechnology ; Polymer chemistry ; Composite material Conducting polymers and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Bin Chen | Yuxuan Zhang | Haochen Shi | Yujie Zeng | Huinan Yang
Source: Journal of Analytical and Applied Pyrolysis (article, 2025-08-30)
Field: Chemistry — Analytical Chemistry

Complex reaction processes in Kerogen pyrolysis unraveled by deep learning-based molecular dynamics simulation Kerogen ; Pyrolysis ; Molecular dynamics ; Chemistry ; Biochemical engineering ; Geology ; Organic chemistry ; Computational chemistry ; Engineering ; Source rock ; Paleontology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Protein Structure and Dynamics
Authors: Anurupa Maiti | Sutanu Nandi | Biplop Jyoti Hazarika | B Pramanik | Amit Biswas | Anup Bhunia
Source: The Journal of Physical Chemistry C (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Materials Classification to Boost Catalyst Design for Electrochemical Oxidation Machine learning (ML) is revolutionizing materials science with electrocatalysis emerging as a particularly promising area. While numerous noble and non-noble materials have been explored for chlorine evolution reactions (CER), identifying robust and readily accessible electrocatalysts for broader electrochemical oxidation remains a significant challenge. In this study, we leverage ML to address this gap and identify such materials. We examine the complex relationships between the Fermi level and conduction band position of cobalt-based oxides, alongside various uncommon descriptors such as formation energy, energy above the hull, density, number of magnetic sites, and total magnetization. The data underwent careful cleaning and feature engineering using different ML processes to ensure accuracy. Models were trained on 70% of the data and tested on the remaining 30%. Using a Random Forest classifier, we analyzed electrochemical data and identified Co3O4 as a cost-effective and scalable material for electrochemical oxidation. This machine-learning-driven approach revealed that Co3O4 is more susceptible to oxidation, leading to high reaction efficiency in synthetic applications such as arene chlorination and epoxide conversion. Catalysis ; Electrochemistry ; Computer science ; Materials science ; Chemical engineering ; Artificial intelligence ; Chemistry ; Engineering ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Isaiah O. Betinol | Aleksandra Demchenko | Jolene P. Reid
Source: ACS Catalysis (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space Machine learning (ML) models are increasingly being employed in asymmetric catalysis to predict reaction outcomes and optimize enantioselective processes. Despite the trend of expanding data set sizes to improve model performance, asymmetric catalysis presents unique challenges, including the difficulty of acquiring high-quality experimental data and the often-limited availability of structurally diverse examples. Consequently, rational data set design requires the practitioner to choose whether to collect data that maximizes diversity in the training set or data that maximizes representation around a target prediction. A key challenge in these studies is understanding the role of local reaction space─specifically, how much predictive accuracy is driven by nearest neighbors (structurally and electronically similar data points) and the next-nearest neighbors? This study investigates the predictive power of ML models trained with varying levels of local representation in the reaction space. We provide a framework, a radius-based random forest (RaRF) algorithm, to systematically probe the effects of including diverse reactions dissimilar to a target prediction. We show that when the training set is representative of the target reaction, the gains from increasing data set diversity are modest─typically less than 0.1 kcal/mol in predictive error─and increasing to only 0.5 kcal/mol for extrapolative tests, highlighting the need for targeted data set design. Furthermore, these findings hold even for complex architectures and features. Finally, we demonstrate that a targeted, neighborhood-oriented strategy greatly accelerates the identification of predictive models compared to diversity-driven approaches. Perspective (graphical) ; Catalysis ; Space (punctuation) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shuyuan Li | Chenyu Huang | Yunjiang Zhang | Jing Li | Shaorui Sun
Source: Catalysis Science & Technology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and text mining approaches to design selective catalyst reduction synthesis routes The workflow for designing SCR synthesis routes employs machine learning and text mining techniques, including data extraction, feature encoding and filtering, model training and interpretation, and predicting synthesis routes. Catalysis ; Reduction (mathematics) ; Computer science ; Selective catalytic reduction ; Artificial intelligence ; Combinatorial chemistry ; Chemistry ; Machine learning ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Kubra Tiras | Burcu Oral | Nazlinur Koparipek Arslan | Sıla Alemdar | Ramazan Yıldırım | Alper Uzun
Source: Journal of Catalysis (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Using machine learning to guide the synthesis of supported palladium catalysts with desired palladium dispersion Palladium ; Chemistry ; Catalysis ; Dispersion (optics) ; Organic chemistry ; Optics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Research Data Management Practices
Authors: Shifang Sun | Fucheng Tian | Chenhao Zhao | Mengyu Xie | Wenyi Li | Wancheng Yu | Kunpeng Cui | Liangbin Li
Source: The Journal of Chemical Physics (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Directed message passing neural networks enhanced graph convolutional learning for accurate polymer density prediction Polymer density is a critical factor influencing material performance and industrial applications, and it can be tailored by modifying the chemical structure of repeating units. Traditional polymer density characterization methods rely heavily on domain expertise; however, the vast chemical space comprising over one million potential polymer structures makes conventional experimental screening inefficient and costly. In this study, we proposed a machine learning framework for polymer density prediction, rigorously evaluating four models: neural networks (NNs), random forest (RF), XGBoost, and graph convolutional neural networks (GCNNs). Using a curated dataset of 1432 homopolymers from the PoLyInfo database, our comparative analysis indicated that the GCNN model enhanced by a directed message passing neural network (D-MPNN) for feature extraction achieves superior predictive accuracy (mean absolute error, MAE = 0.0497 g/cm3; coefficient of determination, R2 = 0.8097) relative to the other models. Experimental validation on six polymers demonstrated strong agreement between measured densities and GCNN predictions with relative errors not exceeding 4.8%. In-depth error analysis combined with SHapley additive exPlanations (SHAP) and subgroup evaluations reveals fundamental relationships between specific functional groups and polymer density, improving the model's interpretability. Furthermore, t-SNE visualization and kernel density estimation (KDE) analysis revealed that the D-MPNN framework effectively captures critical chemical space features governing density, explaining the GCNN's superior performance. This work establishes a reliable and scalable computational framework for high-throughput polymer screening, providing scientific insights into structure-property relationships while positioning density prediction as a prerequisite for incorporating density-dependent properties into polymer informatics and accelerating the discovery of novel polymer materials. Convolutional neural network ; Artificial neural network ; Scalability ; Chemical space ; Visualization ; Graph ; Kernel (algebra) ; Polymer ; Random forest Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Atsufumi Hirohata | Hiroki Koizumi | Tufan Roy | Masahito Tsujikawa | Shigemi Mizukami | Kenji Nawa | Masafumi Shirai
Source: npj Spintronics (review, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine learning for the development of new materials for a magnetic tunnel junction In materials science, we have been increasing the number of constituent elements in an alloy and compounds to improve their properties. For example, in magnetism and spintronics, ternary alloys, such as NdFeB and CoFeB have been developed and widely used in permanent magnets and memories/sensors, respectively. It has now been considered to be a time to add more elements to further explore their horizon. For such a complicated development, a manual systematic study is no longer practical, leading to the utilisation of machine learning to predict a candidate. These candidates can then be additionally screened by ab initio calculations before experimental confirmation, which can be performed routinely. Additional use of quantum annealing may also broaden the adoptability of machine learning on the materials development. In this perspective, we plan to offer a standardised process for such a development with some requirements for improvement. Tunnel magnetoresistance ; Materials science ; Engineering physics ; Engineering ; Nanotechnology Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Magnetic properties of thin films
Authors: B. S. Kang | Vignesh C. Bhethanabotla | Amin Tavakoli | Maurice D. Hanisch | William A. Goddard | Anima Anandkumar
Source: ArXiv.org (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems Despite the success of deep learning methods in quantum chemistry, their representational capacity is most often confined to neutral, closed-shell molecules. However, real-world chemical systems often exhibit complex characteristics, including varying charges, spins, and environments. We introduce OrbitAll, a geometry- and physics-informed deep learning framework that can represent all molecular systems with electronic structure information. OrbitAll utilizes spin-polarized orbital features from the underlying quantum mechanical method, and combines it with graph neural networks satisfying SE(3)-equivariance. The resulting framework can represent and process any molecular system with arbitrary charges, spins, and environmental effects. OrbitAll demonstrates superior performance and generalization on predicting charged, open-shell, and solvated molecules, while also robustly extrapolating to molecules significantly larger than the training data by leveraging a physics-informed architecture. OrbitAll achieves chemical accuracy using 10 times fewer training data than competing AI models, with a speedup of approximately $10^3$ - $10^4$ compared to density functional theory. Deep learning ; Generalization ; Representation (politics) ; Quantum chemical ; Quantum ; Process (computing) ; Graph ; Artificial neural network Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Various Chemistry Research Topics
Authors: Raffaele Cheula | Mie Andersen
Source: ACS Catalysis (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Transition States Energies from Machine Learning: An Application to Reverse Water–Gas Shift on Single-Atom Alloys Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods and ab initio methods such as density functional theory (DFT). Here, we propose a machine learning (ML) model for predicting TS energies based on Gaussian process regression with the Wasserstein Weisfeiler-Lehman graph kernel (WWL-GPR). Applying the model to predict adsorption and TS energies for the reverse water–gas shift (RWGS) reaction on single-atom alloy (SAA) catalysts, we show that it can significantly improve the accuracy compared to traditional approaches based on scaling relations or ML models without a graph representation. Further benefiting from the low cost of model training, we train an ensemble of WWL-GPR models to obtain uncertainties through subsampling of the training data and show how these uncertainties propagate to turnover frequency (TOF) predictions through the construction of an ensemble of microkinetic models. Comparing the errors in model-based vs DFT-based TOF predictions, we show that the WWL-GPR model reduces errors by almost an order of magnitude compared to scaling relations. This demonstrates the critical impact of accurate energy predictions on catalytic activity estimation. Finally, we apply our model to screen other materials, identifying promising catalysts for RWGS. This work highlights the power of combining advanced ML techniques with DFT and microkinetic modeling for screening catalysts for complex reactions like RWGS, providing a robust framework for future catalyst design. Water-gas shift reaction ; Atom (system on chip) ; Catalysis ; Transition state ; Transition metal ; Materials science ; Chemical physics ; Atomic physics ; Chemistry ; Nanotechnology ; Physics ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Han Xu | Xuexian Yu | Jun Liu | Xiang Gao
Source: Computational Materials Science (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Scaling law-informed machine learning for predicting thermal and electrical properties of polymers: A physics-based approach Scaling law ; Thermal ; Scaling ; Polymer ; Statistical physics ; Physics ; Theoretical physics ; Nanotechnology ; Materials science ; Thermodynamics ; Mathematics ; Nuclear magnetic resonance Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Force Microscopy Techniques and Applications
Authors: Sun‐Mou Lai | Zhitao Li | Jiajun Wang
Source: Macromolecular Reaction Engineering (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Prediction and Explainable Analysis of Molecular Weight Distribution of Polystyrene Based on Machine Learning and SHAP Abstract Molecular weight distribution (MWD) is crucial for the product performance of polymers. In order to explore how process conditions affect molecules with different chain lengths, this study conducts a large number of polystyrene process simulations based on polymerization kinetics and validates them through the pilot plant data to generate a reliable dataset. Machine learning methods are employed to predict average molecular weights and conversion rates. Compared to extreme gradient boosting (XGBoost) and support vector regression (SVR), the fully connected neural network (FCNN) shows the best performance. Furthermore, an improved FCNN model with feature extractor and residual structure is developed to predict MWD accurately. The polymer molecules are divided into 10 bins based on chain length, and the influence of process conditions is revealed through SHapley Additive exPlanations (SHAP). Notably, reducing the feed mass fraction of ethylbenzene and increasing the charging coefficient of the second pre‐polymerization reactor will lead to an increase of low molecular weight polymers. Raising the temperature of the second pre‐polymerization reactor will promote a decrease in the proportion of small molecule polymers and ultra‐large molecule polymers, thereby narrowing MWD. In addition, process conditions for polystyrene with specific target MWD can be effectively predicted by machine learning. Polystyrene ; Molar mass distribution ; Distribution (mathematics) ; Polymer science ; Computer science ; Materials science ; Chemical engineering ; Polymer chemistry ; Mathematics ; Engineering ; Composite material ; Polymer Machine Learning in Materials Science
Authors: Weiqi Chen | Zhiyue Xu | Kang Wang | Lei Gao | Aisheng Song | Tianbao Ma
Source: npj Computational Materials (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects. A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications. Multiphases of hydrogenated carbon materials, from crystal to amorphous, with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions. Here, we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning, and construct a general-purpose pre-trained machine learning potential (MLP) for hydrogen-carbon systems. The pre-trained MLP is further efficiently transferred to three target spaces of deposition, friction and fracture with scale reliability. This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity. Nanoscopic scale ; Amorphous solid ; Materials science ; Amorphous carbon ; Carbon fibers ; Hydrogen ; Nanotechnology ; Crystal (programming language) ; Engineering physics ; Computer science ; Engineering ; Crystallography ; Chemistry ; Composite material ; Composite number Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography