jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ke Si | Zhipeng Sun | Huaxin Song | Xiangfen Jiang | Xuebin Wang
Source: Physical Chemistry Chemical Physics (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted design and prediction of materials for batteries based on alkali metals Since the commercialization of lithium-ion batteries in the 1990s, batteries based on alkali metals have been promising candidates for energy storage. The performances of these batteries, in terms of cost-efficiency, energy density, safety, and cycle life need continuous improvement. Battery performances are highly dependent on electrode materials, yet the long experimental period, intensive labor, and high cost remain bottlenecks in the improvement of electrode materials. Machine learning (ML), which is being increasingly integrated into materials science, offers transformative potential by reducing the R&D period and cost. ML also demonstrates significant advantages in the performance prediction of various materials, and it can also help reveal the structure-performance relationship of materials. ML-assisted material design and performance prediction thus enable the innovation of advanced materials. Herein, implementation of ML for exploring alkali metal-based batteries is outlined, highlighting various ML algorithms as well as electrode reaction mechanisms. Alkali metal ; Materials science ; Nanotechnology ; Biochemical engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Ziyu Huo | Xiaoyu Xie | Rong Tong
Source: Chemistry - A European Journal (review, 2025-04-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Developing Sustainable Polymers Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial-and-error methods remains inefficient and resource-intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field. Sustainable development ; Computer science ; Resource (disambiguation) ; Field (mathematics) ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: J.P. Shukla | Xiaohui Qu | Zubin Darbari | Marija Iloska | J. Anibal Boscoboinik | Qin Wu
Source: The Journal of Physical Chemistry Letters (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Network Modeling of Transient Kinetics Spectroscopy We demonstrate a data-driven approach to interpreting surface reactions by combining time-resolved gas pulsing infrared spectroscopy with chemical reaction neural networks (CRNNs). Using CO adsorption and desorption on Pd(111) at 460-490 K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow and bridge site adsorption. Despite the similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism in which CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as being the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and a molecular-level understanding of surface reactions. Kinetics ; Desorption ; Adsorption ; Transient (computer programming) ; Spectroscopy ; Chemistry ; Chemical kinetics ; Chemical engineering ; Materials science ; Physical chemistry ; Computer science ; Physics ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: Z. H. Ming | Min Zhang | Shouxin Zhang | Xiaopeng Li | Xiaoshan Yan | Kexin Guan | Yu Li | Yufeng Peng | Jinfeng Li | Heguo Li | Yue Zhao | Zhiwei Qiao
Source: Nanomaterials (article, 2025-01-24)
Field: Chemistry — Inorganic Chemistry

A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal-organic frameworks (MOFs), with their unique structural properties, show significant potential for HD adsorption applications. Due to the extreme hazards of HD, most experimental studies focus on its simulants, but molecular simulation research on these simulants remains limited. Simulation analyses of simulants can uncover structure-performance relationships and enable experimental validation, optimizing methods, and improving material design and performance predictions. This study integrates molecular simulations, machine learning (ML), and molecular fingerprinting (MFs) to identify MOFs with high adsorption performance for the HD simulant diethyl sulfide (DES), followed by in-depth structural analysis and comparison. First, MOFs are categorized into Top, Middle, and Bottom materials based on their adsorption efficiency. Univariate analysis, machine learning, and molecular fingerprinting are then used to identify and compare the distinguishing features and fingerprints of each category. Univariate analysis helps identify the optimal structural ranges of Top and Bottom materials, providing a reference for initial material screening. Machine learning feature importance analysis, combined with SHAP methods, identifies the key features that most significantly influence model predictions across categories, offering valuable insights for future material design. Molecular fingerprint analysis reveals critical fingerprint combinations, showing that adsorption performance is optimized when features such as metal oxides, nitrogen-containing heterocycles, six-membered rings, and C=C double bonds co-exist. The integrated analysis using HTCS, ML, and MFs provides new perspectives for designing high-performance MOFs and demonstrates significant potential for developing materials for the adsorption of CWAs and their simulants. Adsorption ; Fingerprint (computing) ; Computer science ; Materials science ; Biochemical engineering ; Nanotechnology ; Chemistry ; Artificial intelligence ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Energetic Materials and Combustion ; Thermal and Kinetic Analysis
Authors: Yabei Xu | Wei Sun | Xinzhe Chen | Yan Wang | Wentao Ji | Dongping Chen
Source: Chemical Engineering Science (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

Reaction mechanism and kinetic modelling of RDX/AP via a chemical reaction neural network Mechanism (biology) ; Kinetic energy ; Reaction mechanism ; Artificial neural network ; Chemistry ; Chemical reaction ; Biological system ; Thermodynamics ; Computer science ; Organic chemistry ; Artificial intelligence ; Physics ; Catalysis ; Biology ; Classical mechanics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Hendrik Kraß | Ju Huang | Seyed Mohamad Moosavi
Source: npj Computational Materials (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliability and effectiveness in practical, real-world applications remain an open question. Metal-organic frameworks (MOFs) and related nanoporous materials are highly porous crystals with critical relevance in carbon capture, energy storage, and catalysis applications. Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry, structural complexity, including porosity and coordination bonds, and the absence from existing training datasets. Here, we introduce MOFSimBench, a benchmark for evaluating uMLIPs on key materials modeling tasks for nanoporous materials, including structural optimization, molecular dynamics (MD) stability, bulk property prediction, and host-guest interactions. Evaluating 20 models from various architectures, we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks, demonstrating their readiness for deployment in nanoporous materials modeling. Our analysis highlights that data quality plays a more critical role than model architecture in determining performance across all evaluated uMLIPs. We release our modular and extensible benchmarking framework at https://github.com/AI4ChemS/mofsim-bench, providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs. Nanoporous ; Benchmarking ; Benchmark (surveying) ; Scalability ; Modular design ; Multiscale modeling ; Key (lock) ; Molecular dynamics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Huiyang Fan | Zhen Mei | Jianhua Yan | Zheng Bo | Zhu Liu
Source: Materials Genome Engineering Advances (article, 2025-08-10)
Field: Materials Science — Materials Chemistry

Interpretable machine learning framework for designing high ionic conductivity in low‐temperature lithium‐ion battery electrolytes Abstract Ionic conductivity is a critical determinant of electrolyte performance in lithium‐ion batteries, governing functionalities such as rate capability and low‐temperature operability. Conventional optimizations, empirical or simulation‐based, face significant limitations in either resource efficiency or predictive accuracy. To address these challenges, we developed an interpretable machine learning (ML) framework that combines least absolute shrinkage and selection operator (LASSO) regression with SHapley Additive exPlanations analysis to elucidate structure–property relationships in multicomponent electrolytes. This framework proposes a novel descriptor, model‐input‐weighted sum of LASSO features, which quantitatively captures the collective influence of molecular characteristics on ionic conductivity. Our approach achieves state‐of‐the‐art predictive accuracy (RMSE = 1.33 mS cm −1 , = 0.88) while identifying two dominant molecular features: PEOE_VSA1, representing surface charge distribution, and NumAtomStereoCenters, reflecting stereochemical complexity. This led to the design of an optimized ternary electrolyte (1 mol L −1 LiTFSI in MA:THF:DMF, 5:3:2 molar ratio) demonstrating unprecedented conductivity values: 15.74 mS cm −1 at 25°C and 2.69 mS cm −1 at −70°C. These results validate our framework's ability to guide the development of high‐performance electrolytes for low‐temperature applications. This study establishes a robust ML framework for accelerated electrolyte discovery, providing fundamental insights into molecular determinants of ionic conductivity. Electrolyte ; Conductivity ; Ionic conductivity ; Ternary operation ; Materials science ; Lasso (programming language) ; Lithium (medication) ; Computer science ; Algorithm ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Shaofeng Shao | Liangwei Yan | Jiale Li | Yizhou Zhang | Jun Zhang | Hyoun Woo Kim | Sang Sub Kim
Source: Chemical Engineering Journal (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Multimodal deep learning-driven exploration of lanthanide-based perovskite oxide semiconductors for ultra-sensitive detection of 2-butanone Lanthanide ; Perovskite (structure) ; Butanone ; Oxide ; Semiconductor ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Optoelectronics ; Engineering ; Ion ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Electrochemical Analysis and Applications
Authors: Adil Kabylda | J. Thorben Frank | Sergio Suarez Dou | Almaz Khabibrakhmanov | Leonardo Medrano Sandonas | Oliver T. Unke | Stefan Chmiela | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Sci. Adv. 10, eadn4397 (2024)]. This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semi-local interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions. SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the PBE0+MBD level of quantum mechanics, ensuring a comprehensive coverage of covalent and non-covalent interactions. Our approach is characterized by computational and data efficiency, scalability to 200 thousand atoms on a single GPU, and reasonable to high accuracy across the chemical space of organic (bio)molecules. SO3LR is applied to study units of four major biomolecule types, polypeptide folding, and nanosecond dynamics of larger systems such as a protein, a glycoprotein, and a lipid bilayer, all in explicit solvent. Finally, we discuss the future challenges toward truly general molecular simulations by combining MLFFs with traditional atomistic models. Molecular dynamics ; Electrostatics ; Computer science ; Scalability ; Force field (fiction) ; Pairwise comparison ; Biomolecule ; Artificial neural network ; Statistical physics ; Chemical physics ; Biological system ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Dylan M. Anstine | R.I. Zubatyuk | Liliana C. Gallegos | Robert S. Paton | Olaf Wiest | Benjamin Nebgen | Travis E. Jones | Gabriel dos Passos Gomes | Sergei Tretiak | Olexandr Isayev
Source: ChemRxiv (preprint, 2025-03-18)
Field: Materials Science — Materials Chemistry

Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing. We report AIMNet2-Pd, a machine learned interatomic potential that enables rapid, accurate computational studies of palladium-catalyzed cross-coupling reactions. AIMNet2-Pd replaces computationally expensive electronic structure calculations with a neural network-based model that performs geometry optimization, transition state searches, and energy calculations in seconds while maintaining accuracy within 1-2 kcal mol⁻¹ and ~0.1 Å compared to the reference QM calculations. AIMNet2-Pd makes computational high-throughput catalyst screening and mechanistic studies of realistic systems feasible by providing on-demand thermodynamic and kinetic predictions for each step of a catalytic cycle. Importantly, the applicability of the systems extends beyond the monophosphine ligands in Pd(0)/Pd(II) cycles for which it has been trained on to chemically diverse Pd complexes. This demonstrates AIMNet2-Pd's utility to serve as a general-purpose and high-throughput tool for studying catalytic reactions. Coupling (piping) ; Interatomic potential ; Chemical physics ; Chemistry ; Materials science ; Computer science ; Computational chemistry ; Molecular dynamics ; Metallurgy Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions
Authors: Xiaoyong Xu | Xiao Hu | Liquan Wang | Ying Jiang
Source: Chinese Journal of Polymer Science (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Data-efficient Machine Learning for Polymer Informatics Informatics ; Polymer ; Computer science ; Materials informatics ; Materials science ; Artificial intelligence ; Machine learning ; Health informatics ; Engineering ; Engineering informatics ; Composite material ; Electrical engineering ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Eisuke F. Sato | Akio Tani | Tomoyuki Miyao | Shumpei Kunimoto | Shinobu Takizawa | Koichi Mitsudo | Seiji Suga
Source: Chemistry - A European Journal (article, 2025-05-24)
Field: Chemistry — Organic Chemistry

Cyanomethylation of Aldehydes on an Electrochemical Microflow System and Utility of Machine Learning‐Assisted Examination of the Reaction Conditions Cyanomethylation of carbonyl compounds affords a β-hydroxy nitrile, which is very useful in the field of synthetic organic chemistry. Although several electrochemical methods have also been reported to achieve cyanomethylation of carbonyl compounds, dehydration as a major side reaction is a significant issue that needs to be prevented. An electrochemical flow reactor, free from side reactions through rapid removal of the reaction solution from the electrode surface, enables the electrochemical cyanomethylation of aldehydes in acetonitrile without any dehydrated side products. To find the "best" reaction conditions for each starting material, we used machine learning (ML) models. In addition, comparing the ML models constructed using various molecular descriptors contributed to understanding the critical molecular properties to proceed the electrochemical cyanomethylation. Our cyanomethylation using a flow system went to completion with a semi-catalytic amount of electrical input, which is not consistent with the conventional basic mechanism, and DFT calculations suggest a radical mechanism. Nitrile ; Electrochemistry ; Acetonitrile ; Reaction mechanism ; Chemistry ; Catalysis ; Mechanism (biology) ; Electrode ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry Radical Photochemical Reactions ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yao-Kun Lei | Kiyoshi Yagi | Yuji Sugita
Source: Journal of Chemical Theory and Computation (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Efficient Training of Neural Network Potentials for Chemical and Enzymatic Reactions by Continual Learning Machine learning (ML) methods have emerged as an efficient surrogate for high-level electronic structure theory, offering precision and computational efficiency. However, the vast conformational and chemical space remains challenging when constructing a general force field. Training data sets typically cover only a limited region of this space, resulting in poor extrapolation performance. Traditional strategies must address this problem by training models from scratch using old and new data sets. In addition, model transferability is crucial for general force field construction. Existing ML force fields, designed for closed systems with no external environmental potential, exhibit limited transferability to complex condensed phase systems such as enzymatic reactions, resulting in inferior performance and high memory costs. Our ML/MM model, based on the Taylor expansion of the electrostatic operator, showed high transferability between reactions in several simple solvents. This work extends the strategy to enzymatic reactions to explore the transferability between more complex heterogeneous environments. In addition, we also apply continual learning strategies based on memory data sets to enable autonomous and on-the-fly training on a continuous stream of new data. By combining these two methods, we can efficiently construct a force field that can be applied to chemical reactions in various environmental media. Training (meteorology) ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Training set ; Data science ; Chemistry ; Physics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Yusuf Uzunoğlu | Berkay Emi̇n | Yusuf Alaca
Source: ADBA computer science. (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

High-Accuracy Prediction of Mechanical Properties of Ni-Cr-Fe Alloys Using Machine Learning Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy. Materials science ; Metallurgy ; Artificial intelligence ; Machine learning ; Computer science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced materials and composites
Authors: Xin Peng | Raquel Pan | Xin Li | Weimin Zhong | Qian Feng
Source: Chemical Engineering Science (article, 2025-02-17)
Field: Chemistry — Inorganic Chemistry

Molecular descriptor-assisted interpretable machine learning: A scheme for guiding the synthesis of zeolites with target structures Scheme (mathematics) ; Artificial intelligence ; Classification scheme ; Computer science ; Machine learning ; Mathematics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions