jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rina Ibragimova | Mikhail S. Kuklin | Tigany Zarrouk | A. Miguel
Source: Chemistry of Materials (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the Gaussian approximation potential (GAP) framework with an extensive data set of C–H configurations obtained from density functional theory. The data set is constructed through iterative training and structure-search techniques that generate a broad range of configurations to comprehensively sample the potential energy surface. Furthermore, the data set is supplemented with relevant bulk, molecular, and high-pressure structures. Finally, long-range van der Waals interactions are added as a locally parametrized model. The accuracy and generality of the potential are validated through the analysis of different simulations under a wide range of conditions, including weak interactions, high temperature, and high pressure. We show that our CH GAP model describes different problems such as the formation of simple and complex alkanes, aromatic hydrocarbons, hydrogenated amorphous carbon (a-C:H), and CH systems at extreme conditions, while retaining good accuracy for pure carbon materials. We use this model to generate hydrocarbons of different sizes and complexity without prior knowledge of organic chemistry rules, and to highlight intrinsic limitations to the simultaneous description on intra- and intermolecular interactions within a single computational framework. Our general-purpose ML interatomic potential has the capability to significantly advance research in the field of H-containing carbon materials and compounds, particularly in the areas where longer dynamics, reactivity, and large-scale effects may be important. Carbon fibers ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Chemical physics ; Engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mo Wang | Jianfeng Wang | Guoliang Yuan | Dan Cao | Gongxun Bai
Source: Materials Today Communications (review, 2025-02-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of machine learning in magnetocaloric materials: A review Materials science ; Magnetic refrigeration ; Metallurgy ; Magnetization ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Ryo Fujita | Yoshifumi Amamoto | Jun Kikuchi
Source: npj Materials Degradation (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field NMR is one of the candidate techniques because it provides information on the higher-order structure and dynamics of polymers quickly and conveniently. Although machine learning approaches such as Bayesian optimization (BO) and convolutional neural networks (CNNs) are significant, there have been almost no reports on effective material design based on low-field nuclear magnetic resonance (NMR) data. This study proposes a method for optimizing polymer process conditions using CNN-based features extracted from relaxation curves. This approach identified important features related to material properties while reconstructing denoised relaxation curves of polylactic acid. BO of process conditions using these features achieved an optimization rate comparable to using material property values, suggesting that effective material design is possible without directly evaluating a large number of properties. This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data. Field (mathematics) ; Bayesian probability ; Bayesian optimization ; Polymer ; Biodegradable polymer ; Computer science ; Biochemical engineering ; Materials science ; Machine learning ; Artificial intelligence ; Engineering ; Mathematics ; Composite material Machine Learning in Materials Science ; NMR spectroscopy and applications ; Fuel Cells and Related Materials
Authors: Sodiq Abiodun Kareem | Ibrahim Ms | Justus Uchenna Anaele | Olajesu Favor Olanrewaju | Emmanuel Omosegunfunmi Aikulola | Michael Oluwatosin Bodunrin
Source: Next Materials (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications MXene-based materials are characterized by excellent superconductivity, superb ion-holding capacity, large surface area, and rapid electrochemical reactions, making them viable options for applications in high-capacity energy storage and conversion systems (ESCS) such as portable digital devices, electric vehicles, power transportation, modern intelligent networks, and 5 G telecommunications. This review article looks at the latest developments and some of the difficulties in the synthesis and modification of MXene-based materials and highlights the transformative role of machine learning (ML) in advancing MXene research and applications. Applications in energy storage and water purification are discussed alongside the economic and industrial challenges of large-scale production. Recent studies confirm that ML models have been instrumental in improving MXene synthesis processes, enabling higher yields and optimization of properties, better purity, and scalability through real-time process control and reinforcement learning. Techniques such as genetic algorithms, evolutionary algorithms, and Bayesian optimization accelerate the discovery of novel MXene phases tailored for specific uses. The review identifies future directions in MXene research, emphasizing the development of scalable fabrication methods, ML-driven material informatics platforms, and the expansion of MXene applications in electronics and beyond. By integrating ML, MXene research is poised to achieve faster, cost-effective advancements and commercialization for next-generation technologies. Commercialization ; Characterization (materials science) ; Nanotechnology ; Engineering ; Computer science ; Materials science ; Business MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Xiaotong Sun | Ruili Li | Bowen Zhang | Hui Wang | Yujun Cheng | Jianping Guan | Qi Liu | Xiaoqing Chen
Source: ACS Sustainable Chemistry & Engineering (article, 2025-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Function–Structure–Synthesis: Machine Learning Enabled Closed-Loop Design of Biomass-Derived Porous Carbon Materials Rational development of biomass-derived porous carbons (BDPCs) is vital for advancing fundamental research and addressing clean energy challenges such as hydrogen purification and supercapacitor energy storage. Current experimental designs are limited by empirical methods, and computational models struggle with the complex function–structure–synthesis relationships of biomass feedstocks. In this study, we introduce a data-driven closed-loop machine learning (ML) workflow integrated with literature-derived BDPC synthesis data sets, enabling inverse design from desired functionalities to optimal structures and synthesis conditions. Our ML framework predicts seven critical synthesis parameters─including precarbonation, physical state, type and amount of activator, activation temperature, time, and heating rate─based on targeted microstructural features. Experimental validations with various biofeedstocks confirm the efficacy of our approach. Notably, AI-S, a BDPC synthesized from sugarcane bagasse under ML guidance, achieves a CO2 working capacity of 12.3 mmol/g at 273 K and 10 bar and a CO2/H2 selectivity of 47.8─the highest reported for BDPCs to date. This ML-driven workflow bridges BDPC properties, microstructure, and synthesis, demonstrating a reliable and predictive method for functionally oriented porous material design and synthesis, thereby accelerating material discovery in energy and environmental applications. Biomass (ecology) ; Function (biology) ; Carbon fibers ; Porosity ; Closed loop ; Porous medium ; Loop (graph theory) ; Biomimetics ; Chemical engineering ; Materials science ; Process engineering ; Nanotechnology ; Environmental science ; Engineering ; Control engineering ; Composite material ; Mathematics ; Ecology ; Biology Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Yuchao Tang | Bin Xiao | Manabu Ihara | Sergei Manzhos | Yi Liu
Source: Journal of Materials Informatics (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features Prediction of materials properties from descriptors of chemical composition and structure with machine learning (ML) methods has been emerging as a viable approach to materials design and is a major component of the materials informatics field. However, as both experimental and computed data may be costly, one often has to work with limited data, which increases the risk of overfitting. Combining various datasets to improve sampling on the one hand and designing optimal ML models from small datasets on the other, can be used to address this issue. Center-environment (CE) features were recently introduced and showed promise in predicting formation energies, structural parameters, band gaps, and adsorption properties of various materials. Here, we consider the prediction of formation energies of Nb and Nb-Nb&lt;sub&gt;5&lt;/sub&gt;Si&lt;sub&gt;3&lt;/sub&gt; eutectic alloys substituted with various alloying elements in the Nb and Nb&lt;sub&gt;5&lt;/sub&gt;Si&lt;sub&gt;3&lt;/sub&gt; phases using CE features - a typical alloy system where the data can be naturally divided into subsets based on the types of substitutional sites. We explore effects of dataset combination and of the functional form of the dependence of the target property on the features. We show that combining the subsets, despite the increased amount of data, can complicate rather than facilitate ML, as different subsets do not increase the density of sampling but sample different parts of space with different distribution patterns, and also have different optimal hyperparameters. The Gaussian process regression-neural network hybrid ML method was used to separate the effects of nonlinearity and inter-feature coupling and show that while for Nb alloys nonlinearity is unimportant, it is critical to Nb-Nb&lt;sub&gt;5&lt;/sub&gt;Si&lt;sub&gt;3&lt;/sub&gt; alloys. We find that inter-feature coupling terms are unimportant or non-recoverable, demonstrating the utility of more robust and interpretable additive models. Overfitting ; Materials science ; Eutectic system ; Coupling (piping) ; Artificial intelligence ; Feature (linguistics) ; Sampling (signal processing) ; Machine learning ; Computer science ; Alloy ; Algorithm ; Biological system ; Artificial neural network ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yuhang Wang | Zelin Wu | Yingfang Jiang | Di Zhang | Qiang Wang | Congwei Wang | Hui‐Hui Li | Xue Jia | Jun Fan | Hao Li
Source: Advanced Functional Materials (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Bridging Theory and Experiment: Machine Learning Potential‐Driven Insights into pH‐Dependent CO₂ Reduction on Sn‐Based Catalysts Abstract Sn‐based materials are among the most promising catalysts for CO 2 reduction reaction (CO 2 RR) to formic acid. However, the complex electrochemistry‐induced surface reconstruction under negative potentials has hindered the precise elucidation of the structure‐performance relationship. Herein, machine learning potential (MLP) is employed to accelerate molecular dynamics (MD) simulations, and pH‐field coupled microkinetic modelling is perfromed to unravel the pH dependence of CO 2 RR at the reversible hydrogen electrode (RHE) scale. Encouragingly, the developed MLP reveals that SnO 2 adopts a nanorod‐like morphology, accurately reproducing experimentally observed reconstruction phenomena. Additionally, SnS 2 prefers to form a rougher surface. Leveraging the precisely determined reconstructed surface, the exciting pH‐dependent behavior of Sn‐based catalysts is highlighted: the increase of pH will cause a left‐shift in the CO 2 RR volcano and ultimately enhance the catalyst's activity. Most importantly, the excellent agreement between the theoretical simulations and our subsequent experimental measurements validates the accuracy of the simulations in terms of turnover frequencies, providing a clear benchmarking analysis between experiments and the MLP‐MD‐assisted pH‐field coupled microkinetic modelling. This work not only offers a valuable MLP‐based approach for studying surface reconstructions, but also provides new guidance for the design of high‐performance complex catalysts for CO 2 RR. Materials science ; Bridging (networking) ; Catalysis ; Nanotechnology ; Reduction (mathematics) ; Chemical engineering ; Computer science ; Organic chemistry ; Chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Pengcheng Xu | Yingying Ma | Wencong Lu | Minjie Li | Wenyue Zhao | Zhilong Dai
Source: Journal of Materials Informatics (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Multi-objective optimization in machine learning assisted materials design and discovery Over the past decades, machine learning has kept playing an important role in materials design and discovery. In practical applications, materials usually need to fulfill the requirements of multiple target properties. Therefore, multi-objective optimization of materials based on machine learning has become one of the most promising directions. This review aims to provide a detailed discussion on machine learning-assisted multi-objective optimization in materials design and discovery combined with the recent research progress. First, we briefly introduce the workflow of materials machine learning. Then, the Pareto fronts in multi-objective optimization and the corresponding algorithms are summarized. Next, multi-objective optimization strategies are demonstrated, including Pareto front-based strategy, scalarization function, and constraint method. Subsequently, the research progress of multi-objective optimization in materials machine learning is summarized and different Pareto front-based strategies are discussed. Finally, we propose future directions for machine learning-based multi-objective optimization of materials. Computer science ; Artificial intelligence ; Engineering ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Chuhong Lin | Bryan Lee | Uzma Anjum | Asmee Prabhu | Neeru Chaudhary | Rong Xu | Tej S. Choksi
Source: Applied Catalysis B: Environmental (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Harnessing physics-inspired machine learning to design nanocluster catalysts for dehydrogenating liquid organic hydrogen carriers Catalysis ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Hybrid Renewable Energy Systems
Authors: Shuxin Li | Yifei Xue | Zhining Sun | Huan Wei | Fei Wu | Lanqun Mao
Source: Journal of the American Chemical Society (article, 2025-05-13)
Field: Chemistry — Electrochemistry

A Chemistry-Informed Generative Deep Learning Approach for Enhancing Voltammetric Neurochemical Sensing in Living Mouse Brain Exploring the time-resolved dynamics of neurochemicals is essential for deciphering neuronal functions, intercellular communication, and neurophysiological or pathological mechanisms. However, the complex interplay among neurochemicals between neurocytes, coupled with extensive chemical signal crosstalk, puts simultaneous sensing of multiple neurochemicals into a longstanding challenge. Herein, we report a chemistry-informed generative neural network (CIGNN) model to separate the Faradaic and the non-Faradaic components from voltammetric currents, minimizing their mutual interference and enhancing quantitative accuracy. With the assistance of generative deep learning, we successfully establish a new platform for <i>in vivo</i> neurochemical sensing, which is validated by simultaneously monitoring the dynamics of dopamine (DA), ascorbic acid (AA), and ionic strength in a neuroinflammation mouse model. We observe that the stimulation with KCl solution triggers a significant enhancement of AA efflux on the model mice (300 ± 50 μM) compared with that from the control mice (170 ± 20 μM), as well as a significant decrease of ion influx (55 ± 7 mM) compared with that from the control mice (120 ± 16 mM), while not evoking a significant change in the DA release from the model mice (2.8 ± 0.3 μM) versus that from the control mice (3.0 ± 0.5 μM). This work provides a robust tool for studying multineurochemical signaling and elucidating the molecular mechanisms underlying various brain activities. Neurochemical ; Chemistry ; Generative grammar ; Neuroscience ; Cognitive science ; Psychology ; Artificial intelligence Electrochemical Analysis and Applications ; Neuroscience and Neuropharmacology Research ; Electrochemical sensors and biosensors
Authors: İrem Gülçin Zırhlıoğlu | Ramazan Yıldırım
Source: Materials Research Bulletin (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Machine learning analysis of photocatalytic CO2 reduction on perovskite materials Photocatalysis ; Perovskite (structure) ; Materials science ; Reduction (mathematics) ; Chemical engineering ; Mineralogy ; Nanotechnology ; Chemistry ; Mathematics ; Engineering ; Catalysis Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Hengquan Guo | Seung Geol Lee
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-guided discovery of thermodynamically stable single-atom catalysts on functionalized MXenes for enhanced oxygen reduction and evolution reactions DFT and machine learning are combined to screen transition-metal-doped MXene (Ti 3 C 2 T 2 ) catalysts for oxygen electrocatalysis. Ni–Ti 3 C 2 S 2 and Cu–Ti 3 C 2 S 2 show low ORR/OER overpotentials, highlighting the power of ML-accelerated catalyst design. MXenes ; Electrocatalyst ; Catalysis ; Oxygen reduction ; Oxygen evolution ; Oxygen reduction reaction ; Oxygen ; Transition metal ; Atom (system on chip) ; Materials science ; Oxygen atom ; Chemical engineering ; Chemistry ; Nanotechnology ; Physical chemistry ; Computer science ; Electrochemistry ; Molecule ; Organic chemistry ; Engineering MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Yawen Dong | Yutong Liu | Fei‐Fei Mao | Hua Wu
Source: Applied Energy (article, 2025-05-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy storage in supercapacitor researches: Interdisciplinary applications from molecular simulations to machine learning Supercapacitor ; Energy storage ; Energy (signal processing) ; Process engineering ; Computer science ; Materials science ; Engineering ; Chemistry ; Thermodynamics ; Physics ; Electrochemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Yu Xiong | Yaqin Zhang | Yuhang Wang | Ninggui Ma | Qianqian Wang | Deshuai Yang | Jun Zhao | Shuang Luo | Jun Fan
Source: Surfaces and Interfaces (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

First principles and interpretable machine learning aided the exploration of defective MXene for nitrogen reduction reaction Electrocatalytic nitrogen reduction to ammonia is an efficient and green energy conversion technology, emerging as a promising alternative to the Haber-Bosch process. However, the design and exploration of highly efficient catalysts continues to pose a formidable challenge. Herein, the catalytic performance including stability, activity, and selectivity for the nitrogen reduction reaction of 132 defective 2D MXenes are systematically discussed using first-principles calculations and machine learning models. Several high-performance catalysts with ultralow limiting potential are identified such as Ti 2 CSe 2 (0.30 eV), Ti 2 NSe 2 (0.32 eV), Nb 2 CO 2 (0.41 eV), and Zr 2 CSe 2 (0.43 eV). Furthermore, our findings highlight the exceptional predictive accuracy of the Ridge Regression (RDG) and Support Vector Regressor (SVR) models for ∆ G 1 (*N 2 →*NNH) and ∆ G 2 (*NH 2 →*NH 3 ), respectively, with coefficients of determination (R 2 ) scores of 0.969 and 0.933. Importantly, the Shapley Additive exPlanation analysis has elucidated the significance of descriptors influencing the free energy changes, thereby revealing the underlying origins of the catalytic activity in defective MXenes. The current work offers deeper insights by intricately correlating structure and performance, which could serve as a valuable guide for the design and application of high-performance catalysts. Materials science ; Reduction (mathematics) ; Nitrogen ; Nanotechnology ; Artificial intelligence ; Computer science ; Organic chemistry ; Mathematics MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Jianjian Hu | Jun-Xuan Jin | Xiao‐Jing Hou | C. R. Rao | Yuchen He | Ke‐Jun Wu
Source: Industrial & Engineering Chemistry Research (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

Assisted Energetic Material Property Prediction through Advanced Transfer Learning with Graph Neural Networks In this study, we explore the use of transfer learning to predict the properties of energetic materials using a force-field-inspired transformer graph neural network (FFiTrNet). We began by pretraining the model on a large data set of CHNOF compounds and then fine-tuning it on a smaller data set of experimental enthalpy of formation data for energetic materials. Our results show that transfer learning significantly enhances the accuracy of predicting the enthalpy of formation, with a reduction in mean absolute error and root-mean-square error compared to direct training on the smaller data set. Furthermore, we demonstrate the effectiveness of transfer learning in predicting other properties of energetic materials, highlighting its potential to improve the predictive capabilities of machine learning models for a range of energetic materials properties. The result is the most accurate among the state-of-the-art models for predicting energetic material properties. The data set used in the fine-tuning enriches the database of energetic materials’ properties, making this valuable data publicly available for future research. Artificial neural network ; Transfer of learning ; Property (philosophy) ; Computer science ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Nuclear Materials and Properties ; Welding Techniques and Residual Stresses