jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai Liu | Bingxin Ge | Fangyu Liu | Min Feng | Yong Ji | Yan Li | Lu Wu | Xuefan Jiang | Yongsheng Liu
Source: Computational Materials Science (article, 2025-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted development of Heusler alloys for high magnetic moment Magnetic moment ; Materials science ; Condensed matter physics ; Moment (physics) ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced Welding Techniques Analysis
Authors: Kazuma Ito | Naoki Matsumura | Yasunobu Iwasaki | Yasufumi Sakai | Misaho Yamamura | Tomohiko Omura | Junichiro Yamabe | Hisao Matsunaga
Source: Communications Materials (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

Predicting hydrogen diffusion in nickel–manganese random alloys using machine learning interatomic potentials Abstract To advance carbon neutrality, structural materials for high-pressure hydrogen environments must be designed based on fundamental principles. However, the atomic-scale complexity of random alloys hinders the development of interatomic potentials that can accurately reproduce hydrogen behavior influenced by alloying elements. This study develops a machine-learning interatomic potential (MLIP) for the Ni–Mn–H ternary system by efficiently sampling training data through an active learning strategy that combines atomic-force uncertainty and structural descriptors of diverse atomic environments. Molecular dynamics simulations employing the constructed MLIP quantitatively reproduce the experimentally observed non-monotonic dependence of the hydrogen diffusion coefficient on the Mn content. Two competing Mn-addition effects are found: increased and decreased activation energies from repulsive Mn–H interactions and lattice expansion, respectively, the balance of which shifts with the Mn content and governs the diffusion behavior. This approach enables accurate prediction of hydrogen diffusion in random alloys and provides atomic-level insights into alloying effects. Manganese ; Nickel ; Diffusion ; Materials science ; Hydrogen ; Metallurgy ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Abrar U. Hassan | Cihat Güleryüz | Islam H. El Azab | Ashraf Y. Elnaggar | M.H.H. Mahmoud
Source: Materials Chemistry and Physics (article, 2025-03-18)
Field: Materials Science — Polymers and Plastics

A graph neural network assisted reverse polymers engineering to design low bandgap benzothiophene polymers for light harvesting applications Benzothiophene ; Polymer ; Band gap ; Materials science ; Nanotechnology ; Organic chemistry ; Optoelectronics ; Chemistry ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Wai-Pan Ng | Zili Zhang | Jun Yang
Source: Journal of Chemical Theory and Computation (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales Existing machine learning models attempt to predict the energies of large molecules by training small molecules, but eventually fail to retain high accuracy as the errors increase with system size. Through an orbital pairwise decomposition of the correlation energy, a pretrained neural network model on hundred-scale data containing small molecules is demonstrated to be sufficiently transferable for accurately predicting large systems, including molecules and crystals. Our model introduces a residual connection to explicitly learn the pairwise energy corrections, and employs various low-rank retraining techniques to modestly adjust the learned network parameters. We demonstrate that with as few as only one larger molecule retraining the base model originally trained on only small molecules of (H<sub>2</sub>O)<sub>6</sub>, the MP2 correlation energy of the large liquid water (H<sub>2</sub>O)<sub>64</sub> in a periodic supercell can be predicted at chemical accuracy. Similar performance is observed for large protonated clusters and periodic poly glycine chains. A demonstrative application is presented to predict the energy ordering of symmetrically inequivalent sublattices for distinct hydrogen orientations in the ice XV phase. Our work represents an important step forward in the quest for cost-effective, highly accurate and transferable neural network models in quantum chemistry, bridging the electronic structure patterns between small and large systems. Ab initio ; Artificial neural network ; Computer science ; Energy (signal processing) ; Statistical physics ; Materials science ; Biological system ; Chemical physics ; Artificial intelligence ; Chemistry ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: H.P. Wang | Jikai Sun | Youyong Li | Wei Deng
Source: Scientific Data (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Machine learning and DFT database for C-H dissociation on single-atom alloy surfaces in methane decomposition Methane decomposition using single-atom alloy (SAA) catalysts, known for uniform active sites and high selectivity, significantly enhances hydrogen production efficiency without CO<sub>2</sub> emissions. This study introduces a comprehensive database of C-H dissociation energy barriers on SAA surfaces, generated through machine learning (ML) and density functional theory (DFT). First-principles DFT calculations were utilized to determine dissociation energy barriers for various SAA surfaces, and ML models were trained on these results to predict energy barriers for a wide range of SAA surface compositions. The resulting dataset, comprising 10,950 entries with descriptors and energy barriers, as main predictive outcomes, has been validated against existing DFT calculations confirming the reliability of the ML predictions. This dataset provides valuable insights into the catalytic mechanisms of SAAs and supports the development of efficient, low-emission hydrogen production technologies. All data and computational tools are publicly accessible for further advancements in catalysis and sustainable energy solutions. Dissociation (chemistry) ; Methane ; Alloy ; Decomposition ; Atom (system on chip) ; Chemistry ; Materials science ; Database ; Computer science ; Physical chemistry ; Metallurgy ; Parallel computing ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Don Roosan | Md Rahatul Ashakin | Rubayat Khan | Hasiba Khan | Tiffany Khou | Maria-Isabel Carnasciali | Mohammad Rifat Haider
Source:  (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Variational Quantum Circuits for Molecular Classification Using Graph Neural Network The study explores the integration of quantum-enhanced feature mapping and optimization within Graph Neural Networks (GNNs) for molecular classification tasks. Utilizing IBM's Qiskit platform, we implement Variational Quantum Circuits (VQCs) to transform classical molecular features into quantum states, capturing complex correlations through quantum entanglement. Additionally, the Quantum Approximate Optimization Algorithm (QAOA) is employed for hyperparameter tuning to enhance model convergence and performance. Comparative analyses against traditional machine learning models demonstrate the superiority of the quantum-enhanced GNN, highlighting the potential of quantum computing in advancing molecular property prediction. Computer science ; Artificial neural network ; Quantum ; Electronic circuit ; Graph theory ; Graph ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Anh D. Phan | Ngo T. Que | Nguyen T. T. Duyen | Phan Thanh Viet | Quach K. Quang | Baicheng Mei
Source: Journal of Applied Physics (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Bridging machine learning and glassy dynamics theory for predictive polymer modeling Understanding and predicting the glassy dynamics of polymers remain fundamental challenges in soft matter physics. While the elastically collective nonlinear Langevin equation theory has been successful in describing relaxation dynamics, its practical application to polymers depends on thermal mapping, to connect the theory with the experiment, which, in turn, requires detailed thermodynamic data. Such data may not be available for chemically complex or newly designed polymers. In this work, we propose a simple approach that integrates machine learning-predicted glass transition temperatures (Tg) with a simplified thermal mapping based on an effective thermal expansion coefficient to overcome these limitations. This approach can provide quantitatively accurate predictions of relaxation dynamics across a broad range of polymers. Rather than replacing the original thermal mapping, our method complements it by trading formal rigor for computational efficiency and broader applicability in high-throughput screening and materials with limited available data. Moreover, we introduce a physically motivated modification to the thermal mapping that resolves discrepancies in the description of low-Tg polymers. Our results establish a generalizable approach for predictive modeling of glassy polymer dynamics and point toward new directions for theory-guided materials discovery. Bridging (networking) ; Polymer ; Dynamics (music) ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology ; Composite material ; Physics Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Theoretical and Computational Physics
Authors: Ziduo Yang | Xiaoqing Liu | Xiuying Zhang | Pengru Huang | Kostya S. Novoselov | Lei Shen
Source: npj Computational Materials (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Modeling crystal defects using defect informed neural networks Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configurations in point-defect structures. DefiNet achieves near-DFT-level structural predictions in milliseconds using a single GPU. To validate its accuracy, we perform DFT relaxations using DefiNet-predicted structures as initial configurations and measure the residual ionic steps. For most defect structures, regardless of defect complexity or system size, only 3 ionic steps are required to reach the DFT-level ground state. Finally, comparisons with scanning transmission electron microscopy (STEM) images confirm DefiNet’s scalability and extrapolation beyond point defects, positioning it as a valuable tool for defect-focused materials research. Materials science ; Artificial neural network ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Metallurgical Processes and Thermodynamics
Authors: Xin Zhou | Haoli Qin | Zhibo Zhang | Mengzhen Zhu | Hao Yan | Xiang Feng | Lianying Wu | Chaohe Yang | De Chen
Source: AIChE Journal (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Machine learning knowledge‐driven Pt‐based catalyst design library development for selective oxidation of polyalcohol Abstract Laborious first‐principles calculations and trial‐and‐error experimentation often fail to meet the demands of rational and efficient catalyst development. This paper introduces an approach that integrates costly labeled data with process reaction mechanisms for catalyst formulation in the high‐value conversion of glycerol. We developed an innovative system framework, POCOM, which simultaneously generates the optimal process superstructure and operating conditions to achieve peak conversion rates and desired product specifications. We synergistically combined reaction mechanisms, machine learning, process optimization, and data generation techniques, encapsulating them into a cutting‐edge software system specifically designed for catalyst formulation in glycerol selective oxidation. In this process, we identified a previously unreported Pt‐ZnO catalyst formulation. The catalyst, with 1.8 wt% Pt and 0.4 wt% ZnO, demonstrated exceptional performance, achieving a glycerol conversion rate of 88% and a glyceric acid selectivity of 80%. This study offers groundbreaking insights and robust data support for the rational design of glycerol oxidation catalysts. Catalysis ; Glycerol ; Process engineering ; Process (computing) ; Glyceric acid ; Computer science ; Selectivity ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Evan Walter Clark Spotte‐Smith
Source: Machine Learning Science and Technology (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Considering the ethics of large machine learning models in the chemical sciences Abstract Foundation models, including large language models, vision-language models, and similar large-scale machine learning tools, are quickly becoming ubiquitous in society and in the professional world. Chemical practitioners are not immune to the appeal of foundation models, nor are they immune to the many risks and harms that these models introduce. In this work, I present the first analysis of foundation models using the combined lens of scientific ethics and chemical professional ethics. I find that general-purpose generative foundation models are in many ways incompatible with the moral practice of chemistry, though there are fewer ethical problems with chemistry-specific foundation models. My discussion concludes with an examination of how the harm associated with foundation models can be minimized and further poses a set of serious lingering questions for chemical practitioners and scientific ethicists. Artificial intelligence ; Engineering ethics ; Psychology ; Computer science ; Machine learning ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Yuandong Lin | Ji Ma | Yong‐Guang Jia | Chongchong Yu | Jun‐Hu Cheng
Source: Coordination Chemistry Reviews (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Deep learning-assisted methods for accelerating the intelligent screening of novel 2D materials: New perspectives focusing on data collection and description Chemistry ; Nanotechnology ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Moritz Bensberg | Marco Eckhoff | Raphael T. Husistein | Matthew S. Teynor | Valentina Sora | William Bro‐Jørgensen | F. Emil Thomasen | Anders Krogh | Kresten Lindorff‐Larsen | Gemma C. Solomon | Thomas Weymuth | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and nonequilibrium switching simulations. Computer science ; Quantum ; Embedding ; Quantum machine learning ; Electronic structure ; Context (archaeology) ; Statistical physics ; Quantum computer ; Theoretical computer science ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Tathagata Biswas | Adway Gupta | Arunima K. Singh
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Many-body physics and machine learning enabled discovery of promising solar materials In recent years, GW-BSE has been proven to be extremely successful in studying the quasiparticle (QP) bandstructures and excitonic effects in the optical properties of materials. However, the massive computational cost associated with such calculations restricts their applicability in high-throughput material discovery studies. Recently, we developed a Python workflow package, <i>py</i>GWBSE, to perform high-throughput GW-BSE simulations. In this work, using <i>py</i>GWBSE we create a database of various QP properties and excitonic properties of over 350 chemically and structurally diverse materials. Despite the relatively small size of the dataset, we obtain highly accurate supervised machine learning (ML) models <i>via</i> the dataset. The models predict the quasiparticle gap with an RMSE of 0.36 eV, exciton binding energies of materials with an RMSE of 0.29 eV, and classify materials as high or low excitonic binding energy materials with classification accuracy of 90%. We exemplify the application of these ML models in the discovery of 159 visible-light and 203 ultraviolet-light photoabsorber materials utilizing the Materials Project database. Quasiparticle ; Exciton ; Computer science ; Physics ; Nanotechnology ; Materials science ; Condensed matter physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhenyu Lin | Yuhan Cui | Yunjie Wang | Ye Wu | Bing He | Dong Liu
Source: International Journal of Hydrogen Energy (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine learning reveals structure-performance relationships of dry reforming of methane catalysts and the potential influencing mechanisms Catalyst design is crucial for optimizing dry reforming of methane (DRM), but traditional experimental design and computational methods are time and resource costly. Machine learning (ML) can help develop effective catalysts to some extent, we propose an interpretable ML model that achieves an R 2 of 0.91, and use tools to analyse the importance and interactions of the parameters involved in the reaction process. In addition to the well-known temperature and GHSV, the study reveals the potential effect of the calcination temperature on methane conversion, i.e., it affects methane conversion by influencing the surface structure of the catalyst. To further illustrate the model's ability to predict unknown variables, we chose variables that were not included in the dataset for experimental validation and were within 10 % of error. The reaction conditions were optimized for particular scenarios in the extended research. This method was successful in limiting the ideal reaction conditions to a particular range, which yielded fresh catalyst design concepts. • A machine learning model that can explain the intrinsic decision-making mechanisms was proposed. • The model predicts the effect of the interaction between intrinsic and extrinsic reaction conditions on the CH 4 %. • The variation of CH 4 % with reaction temp, calcination temp and Ni loading under specific conditions were investigated. • The credibility of the model was verified by experimentally comparing the prediction results. Methane ; Carbon dioxide reforming ; Catalysis ; Chemical engineering ; Materials science ; Environmental science ; Chemistry ; Engineering ; Syngas Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Qiyue Zhao | Yupeng Cui | Chuanlei Liu | Haiyang Wen | Yuxiang Chen | Peicheng Li | Yousheng Zhou | Yifan Wang | Hao Jiang | Qiumin Wu | Benxian Shen | Hui Sun
Source: Chemical Engineering Science (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Machine learning model based on parallel reaction mechanisms for predicting CO2 capacity of amine solvents Amine gas treating ; Chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Xinke Qi | A. Q. Liu | Yuanyuan Li | Lanxue Dang | Jinglai Zhang | Li Wang
Source: Materials Today Communications (article, 2025-02-13)
Field: Materials Science — Biomaterials

Deep learning and finite element simulation of spatiotemporal multiscale fusion: Real time prediction of magnesium alloys corrosion covered with MAO coatings Materials science ; Corrosion ; Fusion ; Finite element method ; Magnesium ; Metallurgy ; Thermodynamics Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Huafeng Liu | T. Nakata | Chao Xu | Deli Zhao | Lin Zhu | Nan Qu | Haoyang Ding | Kun-kun Deng | Kai-bo Nie | Tao Liu | Guangze Tang | Xiaojun Wang | S. Kamado | Lin Geng
Source: Metallurgical and Materials Transactions A (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Design of High Thermal Conductivity and High Strength Mg Alloys Materials science ; Thermal conductivity ; High strength steel ; Composite material ; Metallurgy MXene and MAX Phase Materials ; Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics
Authors: Kaiming Qiao | Zhe Cui | Xiaowen Hao | Qian Zhao | Yuanxiang Xu | Dekun Wang | Jinyi Liu | Doudou Wang | Yuanguang Xia | Wen Yin | Jiazheng Hao | Lunhua He | Carlos Romero‐Muñiz | Jia Yan Law | V. Franco | Qingyong Ren | Hu Zhang
Source: Acta Materialia (article, 2025-07-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Giant room-temperature magnetocaloric effect MM′X alloys explored by machine learning Materials science ; Magnetic refrigeration ; Metallurgy ; Thermodynamics ; Magnetization ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Shape Memory Alloy Transformations ; Ferroelectric and Piezoelectric Materials