Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling [0.03%]
基于机器学习的原子模拟揭示过饱和非晶氧化铝中的氢化学态
Simon Gramatte,Olivier Politano,Noel Jakse et al.
Simon Gramatte et al.
Advancing hydrogen-based technologies requires detailed characterization of hydrogen chemical states in amorphous materials. As experimental probing of hydrogen is challenging, interpretation in amorphous systems demands accurate structural...
Parameter efficient multi-model vision assistant for polymer solvation behaviour inference [0.03%]
参数高效的多模型视觉助手在聚合物溶剂化行为推理中的应用
Zheng Jie Liew,Ziad Elkhaiary,Alexei A Lapkin
Zheng Jie Liew
Polymer-solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid sc...
Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy [0.03%]
基于第一性原理的光诱导有序无序相变的机器学习方法
Andrea Corradini,Giovanni Marini,Matteo Calandra
Andrea Corradini
While machine learning excels in simulating material thermal properties, its application to order-disorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfa...
Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning [0.03%]
通过高效且稳健的主动学习绘制分子中的多电子态及间隙驱动的动力学图表
Mikołaj Martyka,Lina Zhang,Fuchun Ge et al.
Mikołaj Martyka et al.
We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in exci...
Jiyu Chen,Philipp Werner
Jiyu Chen
Multidimensional coherent spectroscopy (MDCS) has been established in quantum chemistry as a powerful tool for studying the nonlinear response and nonequilibrium dynamics of molecular systems. More recently, the technique has also been appl...
Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals [0.03%]
基于时间依赖密度泛函微扰理论和非经验Hubbard泛函的 magnon计算
Luca Binci,Nicola Marzari,Iurii Timrov
Luca Binci
Spin excitations play a fundamental role in understanding magnetic properties of materials, and have significant technological implications for magnonic devices. However, accurately modeling these in transition-metal and rare-earth compound...
Francesco Libbi,Anders Johansson,Lorenzo Monacelli et al.
Francesco Libbi et al.
The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these experiments is extremely challenging due to the complex nonli...
Development of an atomic cluster expansion potential for iron and its oxides [0.03%]
铁及其氧化物原子团簇扩展势的开发
Baptiste Bienvenu,Mira Todorova,Jörg Neugebauer et al.
Baptiste Bienvenu et al.
The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time scales, one requires a physically justified interatomic potenti...
Constructing multicomponent cluster expansions with machine-learning and chemical embedding [0.03%]
用机器学习和化学嵌入构建多组分团簇展开式
Yann L Müller,Anirudh Raju Natarajan
Yann L Müller
Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to...
A machine-learning framework for accelerating spin-lattice relaxation simulations [0.03%]
加速自旋晶格弛豫模拟的机器学习框架
Valerio Briganti,Alessandro Lunghi
Valerio Briganti
Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progres...