npj Computational Materials最新文献_第8页

Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning 使用强化学习解锁材料设计贝叶斯全局优化之外的黑箱

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-22 DOI: 10.1038/s41524-025-01639-w

Yuehui Xian, Xiangdong Ding, Xue Jiang, Yumei Zhou, Jun Sun, Dezhen Xue, Turab Lookman

{"title":"Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning","authors":"Yuehui Xian, Xiangdong Ding, Xue Jiang, Yumei Zhou, Jun Sun, Dezhen Xue, Turab Lookman","doi":"10.1038/s41524-025-01639-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01639-w","url":null,"abstract":"Materials design often becomes an expensive black-box optimization problem due to limitations in balancing exploration-exploitation trade-offs in high-dimensional spaces. We propose a reinforcement learning (RL) framework that effectively navigates the complex design spaces through two complementary approaches: a model-based strategy utilizing surrogate models for sample-efficient exploration, and an on-the-fly strategy when direct experimental feedback is available. This approach demonstrates better performance in high-dimensional spaces (D ≥ 6) compared to Bayesian optimization (BO) with the Expected Improvement (EI) acquisition function through more dispersed sampling patterns and better landscape learning capabilities. Furthermore, we observe a synergistic effect when combining BO’s early-stage exploration with RL’s adaptive learning. Evaluations on both standard benchmark functions (Ackley, Rastrigin) and real-world high-entropy alloy data, demonstrate statistically significant improvements (p < 0.01) over traditional BO with EI, particularly in complex, high-dimensional scenarios. This work addresses limitations of existing methods while providing practical tools for guiding experiments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis 高熵驱动绿色能源：氢、电池、电子和催化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-22 DOI: 10.1038/s41524-025-01594-6

Guotao Qiu, Tianhao Li, Xiao Xu, Yuxiang Liu, Maya Niyogi, Katie Cariaga, Corey Oses

{"title":"High entropy powering green energy: hydrogen, batteries, electronics, and catalysis","authors":"Guotao Qiu, Tianhao Li, Xiao Xu, Yuxiang Liu, Maya Niyogi, Katie Cariaga, Corey Oses","doi":"10.1038/s41524-025-01594-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01594-6","url":null,"abstract":"A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electronic properties of stacking faults in Bernal graphite 石墨层错的电子特性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-21 DOI: 10.1038/s41524-025-01641-2

Patrick Johansen Sarsfield, Sergey Slizovskiy, Mikito Koshino, Vladimir Fal’ko

引用次数: 0

Unexpected density functional dependence of the antipolar $$boldsymbol{Pbcn}$$ phase in HfO2 HfO2中反极性$$boldsymbol{Pbcn}$$相意想不到的密度泛函依赖性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-19 DOI: 10.1038/s41524-025-01647-w

Di Fan, Tianyuan Zhu, Shi Liu

{"title":"Unexpected density functional dependence of the antipolar $$boldsymbol{Pbcn}$$ phase in HfO2","authors":"Di Fan, Tianyuan Zhu, Shi Liu","doi":"10.1038/s41524-025-01647-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01647-w","url":null,"abstract":"The antipolar Pbcn phase of HfO2 has been suggested to play a critical role in the phase transitions and polarization switching mechanisms of ferroelectric hafnia. Here, we benchmark density functional theory (DFT) and deep potential molecular dynamics (DPMD) simulations to investigate the thermodynamic stability and phase transition behavior of hafnia, with a particular focus on the relationship between the Pbcn and ferroelectric Pca21 phases. Significant discrepancies in phase energetics emerge across exchange-correlation functionals: the PBE and hybrid HSE06 functionals exhibit consistent trends, which diverge from the predictions of the PBEsol and SCAN functionals. Quasi-harmonic free energy calculations show good agreement with finite-temperature DPMD simulations using deep potentials trained on the same functional. We further find that, under fixed mechanical boundary conditions based on the Pca21 ground-state structure, all functionals predict consistent relative phase stabilities, polarization switching barriers, and domain wall energies.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"74 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electro-chemo-mechanical modelling of structural battery composite full cells 结构电池复合材料全电池的电化学-力学建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-19 DOI: 10.1038/s41524-025-01646-x

Carl Larsson, Fredrik Larsson, Johanna Xu, Kenneth Runesson, Leif E. Asp

{"title":"Electro-chemo-mechanical modelling of structural battery composite full cells","authors":"Carl Larsson, Fredrik Larsson, Johanna Xu, Kenneth Runesson, Leif E. Asp","doi":"10.1038/s41524-025-01646-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01646-x","url":null,"abstract":"Structural battery composites are multifunctional materials capable of storing electrochemical energy and carry mechanical load at the same time. In this study, we focus on the laminated structural battery design developed by Asp and co-workers, which utilises multifunctional carbon fibres as both active material and mechanical reinforcement in the negative electrode. The positive electrode consists of active lithium iron phosphate particles adhered to an aluminium foil. Building upon previous research, we develop a fully coupled numerical multiphysics model to simulate the charge–discharge processes of the structural battery full cell. The model includes non-linear reaction kinetics, pertinent to the Butler–Volmer relation. Furthermore, we employ a simplified continuum representation of the porous positive electrode, enabling simulations at the battery cell level. Available experimental data for material parameters is utilised when possible, while the remaining parameters are obtained from calibration against experimental charge–discharge voltage profiles at two different rates. Results show that the presented model captures the general trend of the experimental voltage profiles for a range of charge rates. Through this work, we aim to provide insights for future structural battery design efforts.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144097353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational discovery of metallic MBenes for two-dimensional semiconductor contacts approaching the quantum limit 接近量子极限的二维半导体触点金属MBenes的计算发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-19 DOI: 10.1038/s41524-025-01640-3

Meng Li, Dan Cao, Dabao Xie, Meiying Gong, Congmin Zhang, Tao You, Jing Zhou, Xiaoshuang Chen, Haibo Shu

{"title":"Computational discovery of metallic MBenes for two-dimensional semiconductor contacts approaching the quantum limit","authors":"Meng Li, Dan Cao, Dabao Xie, Meiying Gong, Congmin Zhang, Tao You, Jing Zhou, Xiaoshuang Chen, Haibo Shu","doi":"10.1038/s41524-025-01640-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01640-3","url":null,"abstract":"The realization of ultralow-resistance contacts in two-dimensional semiconductors such as transition metal dichalcogenides (TMDs) is pivotal for advancing transistor scaling toward the end of technology roadmap. In this work, by means of high-throughput first-principles calculations, we identify that highly stable two-dimensional metallic MBenes with large abundance of density of states are potential for achieving low-resistance MBene-TMD contacts at the quantum limit. We reveal that local built-in electric field at MBene-MoS2 interfaces driven by interfacial polarization enables tunable band shift of MoS2 channel, which allows for obtaining p-type Ohmic contact. The strong van der Waals interactions between MBenes and MoS2 induces a delicate balance between the Fermi-level pinning and carrier tunneling efficiency, resulting in ultralow contact resistance down to 41.6 Ω μm. The contact performance of screened Nb2BO2-MoS2 and Nb2B(OH)2-MoS2 junctions can be competed with previous records using semimetals Sb and Bi as the contacts of MoS2 devices.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials 量子材料中原子尺度磁各向异性的可解释机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-18 DOI: 10.1038/s41524-025-01637-y

Jan Navrátil, Rafał Topolnicki, Michal Otyepka, Piotr Błoński

引用次数: 0

Crystal-like thermal transport in amorphous carbon 非晶碳的晶体样热输运

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-17 DOI: 10.1038/s41524-025-01625-2

Jaeyun Moon, Zhiting Tian

{"title":"Crystal-like thermal transport in amorphous carbon","authors":"Jaeyun Moon, Zhiting Tian","doi":"10.1038/s41524-025-01625-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01625-2","url":null,"abstract":"Thermal transport in amorphous carbon has attracted immense attention due to its extreme thermal properties: It has been reported to have among the highest thermal conductivity for bulk amorphous solids up to ~37 W m−1 K−1, comparable to crystalline sapphire (α-Al2O3). However, mechanism behind the high thermal conductivity remains elusive due to many variables at play. In this work, we perform large-scale (~105 atoms) molecular dynamics simulations utilizing a machine learning potential based on neural networks with first-principles accuracy. Through spectral decomposition of thermal conductivity which enables a quantum correction to classical heat capacity, we find that propagating vibrational excitations govern thermal transport in amorphous carbon (~100 % of thermal conductivity) in sharp contrast to the convention that diffusive vibrational excitations dominate thermal transport in amorphous solids. This remarkable behavior resembles thermal transport in simple crystals. Our work, therefore, provides a perspective that deepens our understanding of intermediate thermal transport mechanisms between the two ends of spectrum of solids: crystalline and amorphous solids.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites AUGUR，一种灵活高效的最佳吸附位点识别优化算法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-17 DOI: 10.1038/s41524-025-01630-5

Ioannis Kouroudis, Poonam, Neel Misciasci, Felix Mayr, Leon Müller, Zhaosu Gu, Alessio Gagliardi

引用次数: 0

Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models 通过计算氢电极和可解释的机器学习模型预测电解质溶剂的实际还原电位

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-16 DOI: 10.1038/s41524-025-01582-w

Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen

{"title":"Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models","authors":"Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen","doi":"10.1038/s41524-025-01582-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01582-w","url":null,"abstract":"Accurate prediction of practical reduction electrode potentials (Ered) of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction. However, the emergence of new electrolyte solvents and additives leaves most of the reaction mechanisms unveiled. Here, we provide a machine-learning-assisted workflow of thermodynamically quantified Ered prediction for electrolyte solvents. A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps. Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms. Validation of the best-scoring model is conducted by experimental validation of 6 additional solvents. Multiple thermodynamics features are found impactful on Ered through different chemical bonding with reaction intermediates. This workflow enables accurate Ered prediction for electrolyte solvents without identified reduction mechanisms, and is widely applicable in the electrochemical energy storage area.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0