jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeineb Nefzi | Céline Barreteau | Lucas Levrel | Jean‐Claude Crivello
Source: ACS Applied Energy Materials (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Promising Multicomponent Cubic Alloys for Hydrogen Storage: Insights from First-Principles Calculations and Machine Learning An extensive exploration of the chemical space was conducted to design and identify promising multicomponent cubic alloys with appropriate enthalpy of reaction for hydrogen storage applications. We aim to identify alloys with suitable hydrogen absorption conditions for ambient conditions, according to favorable thermodynamic criteria, while addressing computational challenges in modeling large-scale systems. 18 elements were selected, leading to the systematic investigation of over 8000 quinary alloy systems across four distinct crystal phases (within solid–solution alloys, mono- and dihydrides). This effort resulted in a comprehensive data set of more than 34,000 equimolar MHx structures, where M represents a combination of 5 elements chosen among the 18 selected atoms. To handle the computational demands of density functional theory (DFT) calculations on such a large scale of disordered supercells designed by the special quasirandom structure (SQS) method, a machine learning (ML) approach was introduced to accurately predict the enthalpy of hydride formation. By training the ML model on a strategically chosen subset of the data, high predictive accuracy was achieved while significantly reducing computational costs. By applying filtering parameters constrained by thermodynamic considerations, such as the value of plateau pressure or the presence of a single plateau, the integrated DFT-SQS-ML framework successfully identified 568 quinary alloy systems as ideal candidates for hydrogen storage. The findings establish a solid foundation for experimental validation and further advancements in the field of hydrogen storage materials. Hydrogen storage ; Materials science ; Computer science ; Metallurgy ; Alloy Hydrogen Storage and Materials ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Yingjie Yan | Yifan Zhang | Xiaojia Wu | Qiwei Guo | Daijun Zhang | Chao Li | Yanfeng Liu | Jingyi Zhang | Liwei An | Zhiyang Wang | Junhua Guo | Li Chen
Source: Composites Communications (review, 2025-07-22)
Field: Materials Science — Materials Chemistry

Deep learning paradigm shift in composite materials performance prediction: A review of state-of-the-art applications Paradigm shift ; Deep learning ; Nanotechnology ; Composite number ; Artificial intelligence ; Computer science ; Materials science ; Epistemology ; Philosophy ; Composite material Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Industrial Vision Systems and Defect Detection
Authors: Debasis Saran | Neelam Mishra | Sivaiah Bathula | Κ. K. Sahu
Source: Electrochimica Acta (article, 2025-04-14)
Field: Materials Science — Biomaterials

Machine learning assisted classification and interpretation of EIS data with experimental demonstration for chemical conversion coatings on Mg alloys Interpretation (philosophy) ; Materials science ; Artificial intelligence ; Conversion coating ; Computer science ; Metallurgy ; Machine learning ; Corrosion Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Jingwen Zhou | Yanan Fu | Ling Liu | Chungen Liu
Source: The Journal of Physical Chemistry C (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Constant-Potential Machine Learning Molecular Dynamics Simulations Reveal Potential-Regulated Cu Cluster Formation on MoS<sub>2</sub> Electrochemical processes play a crucial role in energy storage and conversion systems, yet their computational modeling remains a significant challenge. Accurately incorporating the effects of electric potential has been a central focus in theoretical electrochemistry. Although constant-potential ab initio molecular dynamics (CP-AIMD) has provided valuable insights, it is limited by its substantial computational demands. Here, we introduce the explicit electric potential machine learning force field (EEP-MLFF) model. Our model integrates the electric potential as an explicit input parameter along with the atom-centered descriptors in the atomic neural network. This approach enables the evaluation of atomic forces under arbitrary electric potentials, thus facilitating molecular dynamics simulations at a specific potential. By applying the proposed machine learning method to the Cu/1T′-MoS2 system, molecular dynamics simulations reveal that the potential-modulated Cu atom migration and aggregation lead to the formation of small steric Cu clusters (Single Clusters, SCs) at potentials below −0.1 V. The morphological transformations of adsorbed Cu atoms are elucidated through electronic structure analyses, which demonstrates that both Cu–S and Cu–Cu bonding can be effectively tuned by the applied electric potential. Our findings present an opportunity for the convenient manufacture of single metal cluster catalysts through potential modulation. Moreover, the proposed theoretical framework offers an efficient and convenient method for investigating electric potential-related processes, and it is anticipated to apply to electrochemical reactions occurring at electrode/electrolyte interfaces. Molecular dynamics ; Cluster (spacecraft) ; Constant (computer programming) ; Chemical physics ; Dynamics (music) ; Statistical physics ; Physics ; Materials science ; Chemistry ; Nanotechnology ; Computational chemistry ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; 2D Materials and Applications
Authors: Qionghua Zhou | Xinyu Chen | Jinlan Wang
Source: Accounts of Materials Research (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Material Discovery: A Small Data Approach ConspectusThe data-driven paradigm, represented by the famous machine learning paradigm, is revolutionizing the way materials are discovered. The inductive nature of the data-driven approach gives it great speed of prediction but also brings with it a heavy reliance on material data. However, unlike its success with text and images, which are supported by big data, materials data tend to be small data. Building a large database of materials is a good solution but not a permanent one. The cost of materials data is much higher than that of text or images, and the size of the materials database at this stage is far from sufficient. We will continue to face a shortage of materials data for a long time to come, making small data approaches necessary for machine learning based materials discovery.In this Account, we focus on small data strategies developed over the past few years and the scenarios in which they are used. In the first part, we discuss two general strategies, active learning and transfer learning, which are ways of adding new data efficiently and using existing data, respectively. The key to active learning is the sampling strategy, which determines the speed of convergence and the predictive range of the machine learning model. For transfer learning, adversarial training is introduced to extend the scope of this strategy, allowing for knowledge transfer across materials and properties. We also discuss other small data approaches for special cases, such as material search with zero initial data and model training on multisource experimental data. In the second part, we focus on the construction of material descriptors and reduction of their dimensionality. We have developed a crystal-graph-based descriptor specifically for two-dimensional materials. It can encode both structural and atomic information and also has a flexible multilayer format for different target properties. Since the dimensionality of the material descriptor is limited by the amount of data, specially designed dimensionality reduction strategies are also discussed. In the third part, we discuss model interpretability. Several examples are given to illustrate how model-based and data-based interpretation strategies can be used to help us understand the machine learning model and its prediction results.The Account concludes with our perspectives on the latest developments in generative AI (in particular, large language model and diffusion model) and explainable AI, which could be powerful tools in the future of machine learning assisted material discovery. Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shang Xiang | Shaowen Lu | Jiawei Li | Kai Xie | Rui Zhu | Huanan Wang | Kai Huang | Chaoen Li | Jiang Wu | Shibo Chen | Ying Shen | Yih‐Fan Chen | Zhaoyin Wen
Source: ACS Applied Energy Materials (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Ionic Conductivity Study of Antiperovskite Solid-State Electrolytes Based on Interpretable Machine Learning The development of high-performance all-solid-state ion batteries necessitates the design of solid-state electrolytes (SSEs) with high ionic conductivity and excellent electrochemical stability. Antiperovskite (AP) X3BA, as the electronically inverted derivative of perovskite ABX3, has garnered significant attention in the field of energy storage batteries due to its superior ionic conductivity. However, the relationship between their structure and ion diffusion behavior warrants further investigation. In this work, we constructed a machine learning (ML) framework for predicting and analyzing the ionic conductivity of the AP SSE, which encompasses data collection, feature selection, and training of various ML models. The optimal ML model demonstrated an exceptional classification performance, achieving an accuracy rate as high as 94%. Furthermore, we employed the ion substitution method to expand the sample size from 168 to 150,000 orders of magnitude. Based on this expanded data set, we examined and analyzed the mechanisms underlying high ionic conductivity from a big data perspective. The findings reveal a strong correlation between the ionic conductivity and atomic-scale characteristics at the A-site. The electronegativity, density, and ionic radius at the A-site are identified as the three most critical features influencing ionic conductivity. The interpretable ML model constructed in this study enables high-precision prediction of the ionic conductivity of AP materials, provides insightful design principles, and significantly accelerates the development and application of AP SSEs. Antiperovskite ; Electrolyte ; Ionic conductivity ; Conductivity ; Solid-state ; Materials science ; Ionic bonding ; State (computer science) ; Computer science ; Ion ; Chemistry ; Nanotechnology ; Engineering physics ; Physics ; Physical chemistry ; Algorithm ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Hongyu Yang | C. Gao | Denghui Jiang | Diansheng Zhong | Yuxuan Ma | Yang Li | Linzhuang Xing | Heng Zhao | Zhimin Li | Yue Hao
Source: Journal of Advanced Ceramics (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction for the coefficient of thermal expansion of binary crystals It is challenging to theoretically predict the coefficient of thermal expansion (CTE) for binary crystals A<sub>m</sub>B<sub>n</sub> owing to the complexities of crystal structures and computational procedures. Herein, the Pearson feature selection method is utilized to identify nine key features associated closely with crystal structures, and a back-propagation neural network model with two hidden layers containing 24 and 15 neurons is adopted to achieve the optimal matching effect of CTE, which is specially optimized by the pelican optimization algorithm. Moreover, the black-box nature of the model is well elucidated by interpretability techniques of SHapley Additive exPlanations and Accumulated Local Effects, including the specific impact rules of each feature and the interaction effects between features on CTE. It is found that the feature of average bond length delivers the contribution of up to 27%, while low influence features serve an important function in increasing prediction accuracy. The findings demonstrate high efficiency and accuracy of the developed model for predicting CTE of binary crystals. Thermal expansion ; Binary number ; Materials science ; Structural material ; Thermal ; Thermodynamics ; Composite material ; Mathematics ; Physics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Fouzia Mashkoor | Mohd Shoeb | Sayed Mohammed Adnan | Jahangeer Ahmed | Changyoon Jeong
Source: International Journal of Biological Macromolecules (article, 2025-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Biomacromolecular engineering of redox-active chitosan–polypyrrole–clay hybrid materials for machine learning-assisted desulfurization and supercapacitor application Polypyrrole ; Supercapacitor ; Flue-gas desulfurization ; Chitosan ; Redox ; Chemical engineering ; Chemistry ; Materials science ; Nanotechnology ; Inorganic chemistry ; Electrochemistry ; Electrode ; Engineering ; Organic chemistry Supercapacitor Materials and Fabrication ; Catalysis and Hydrodesulfurization Studies ; Polyoxometalates: Synthesis and Applications
Authors: Farhan Zafar | Salah M. El‐Bahy | Abdul Sami | Sadaf Ul Hassan | Naeem Akhtar | Adel Alkhedaide | Hailing Ma | Yao Tong | Shuaifei Zhao
Source: ACS Applied Materials & Interfaces (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Synthesis of Bimetallic Metal–Organic Frameworks for the Optimized Oxygen Evolution Reaction Although a wide range of bimetallic metal-organic frameworks (MOFs) have been reported as electrocatalysts for the oxygen evolution reaction (OER), there is still a need for precise tuning of the metal precursors and composite ratios to minimize the overpotential and design highly efficient electrocatalysts. To achieve this, we applied machine learning (ML) algorithms to optimize the metal precursor and composite ratios, identifying the key factors that govern the OER performance. We first synthesized a bimetallic FeCo squarate-based MOF (FeCo-Sq MOF) using a solvothermal method and then optimized the metal precursor ratios using ML algorithms to achieve a low overpotential. To further enhance the OER efficacy, the ML-optimized FeCo-Sq MOF was coated with S-doped graphitic carbon nitride (SCN) and wrapped with polydopamine (PDA). The PDA wrapping not only increased the number of binding/adsorption sites for -OH but also enhanced the stability, charge/electron transfer kinetics, and effective anchoring of SCN on the MOF surface. To obtain optimal OER catalysts, the SCN loading was further fine-tuned through ML. The ML-optimized PDA-SCN@FeCo-Sq MOF exhibited high electrocatalytic performance, achieving a low overpotential of 310 mV and a Tafel slope of 56 mV/dec at a current density of 10 mA cm<sup>-2</sup> in 1 M KOH. This study presents a promising ML-assisted strategy for designing high-performance PDA-SCN@FeCo-Sq MOF electrocatalysts for efficient water splitting. Materials science ; Bimetallic strip ; Oxygen evolution ; Metal ; Oxygen ; Metal-organic framework ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Metallurgy ; Physical chemistry ; Adsorption ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ming‐Yu Guo | Yun‐Fan Yan | Pin Chen | Wei‐Xiong Zhang
Source: npj Computational Materials (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate and efficient simulations. Applying DeepEMs‑25 to an isostructural ABX3 molecular perovskites series, with A-site organic cations, B-site alkali or ammonium cations, and X-site perchlorate anions, we probe the effect of cation size on reactivity. Arrhenius analysis of 100-ps trajectories reveals that increasing B‑site ionic radius simultaneously decreases X–A collision’s activation energy (enhancing reaction rates) and decreases X–A collision’s pre‑exponential factor (reducing collision frequency), producing opposing kinetic effects. Such “kinetic tug‑of‑war” explains why an intermediate‑sized cation yields maximal thermal stability by optimally balancing reactivity and collision dissipation. A similarly sized reactive cation promotes additional hydrogen-transfer pathways causing accelerating decomposition. Our findings link atomistic kinetics to macroscopic stability, informing next-generation EMs design. Tug of war ; DECIPHER ; Kinetic energy ; Stability (learning theory) ; Computer science ; Biology ; Physics ; Bioinformatics ; Machine learning Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Nuclear Materials and Properties
Authors: Sheng-Rui Wang | Fang Qiu | Xiang‐Yang Liu | Wei‐Hai Fang | Ganglong Cui
Source: The Journal of Chemical Physics (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

Machine learning accelerated nonadiabatic dynamics simulations of materials with excitonic effects This study presents an efficient methodology for simulating nonadiabatic dynamics of complex materials with excitonic effects by integrating machine learning (ML) models with simplified Tamm-Dancoff approximation (sTDA) calculations. By leveraging ML models, we accurately predict ground-state wavefunctions using unconverged Kohn-Sham (KS) Hamiltonians. These ML-predicted KS Hamiltonians are then employed for sTDA-based excited-state calculations (sTDA/ML). The results demonstrate that excited-state energies, time-derivative nonadiabatic couplings, and absorption spectra from sTDA/ML calculations are accurate enough compared with those from conventional density functional theory based sTDA (sTDA/DFT) calculations. Furthermore, sTDA/ML-based nonadiabatic molecular dynamics simulations on two different materials systems, namely chloro-substituted silicon quantum dot and monolayer black phosphorus, achieve more than 100 times speedup than the conventional linear response time-dependent DFT simulations. This work highlights the potential of ML-accelerated nonadiabatic dynamics simulations for studying the complicated photoinduced dynamics of large materials systems, offering significant computational savings without compromising accuracy. Excited state ; Density functional theory ; Molecular dynamics ; Wave function ; Speedup ; Work (physics) ; Statistical physics ; Physics ; Chemistry ; Molecular physics ; Computational chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum and electron transport phenomena
Authors: Farhan Zafar | Muhammad Ali Khan | M.M. El-Toony | Naeem Akhtar | Sadaf Ul Hassan | Abdul Shakoor | Yu Cong
Source: Advanced Sustainable Systems (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Machine Learning Optimized FeCoMn‐Trimetallic MOF‐Decorated Nanofibers for Enhanced OER Catalysis Abstract Despite significant advancements in noble metal‐free trimetallic MOF‐based electrocatalysts for efficient oxygen evolution reaction (OER), limited attention is given to identify which metal will play most significant role in controlling OER performance. Thus, to address this gap, herein ternary metallic (FeCoMn) squarate‐based MOF via a solvothermal approach is synthesized. Additionally, machine learning (ML) algorithms are employed on experimental datasets during synthesis strategy to optimize metal concentrations more swiftly and efficiently to design highly efficient ternary metallic (FeCoMn) squarate MOF‐based electrocatalysts. Interestingly, ML optimization has identified Fe as a key element significantly influencing OER efficacy. To further boost OER efficacy, ML‐optimized FeCoMn MOF is drop‐casted onto highly conductive electrospun polycaprolactone (PC) nanofibers, facilitating smooth, uniform flow of ions and electrons across the entire surface, maximizing exposed active sites, all anchored on a sponge‐like conductive Ni foam (NF) substrate. Results reveal that ML‐optimized FeCoMn/PC displays high electrocatalytic activity with lower overpotential (170 mV at a current density of 10 mA cm −2 ), Tafel slope of 66.6.8 mV dec −1 , as compared to FeCoMn (overpotential 180 mV, Tafel slope 89.3 mV dec −1 ). To the best of knowledge, the first time ML optimized FeCoMn/PC‐based electrocatalyst for OER is reported. Nanofiber ; Materials science ; Catalysis ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science