Optimal experimental design for materials discovery
In this paper, we propose a general experimental design framework for optimally guiding
new experiments or simulations in search of new materials with desired properties. The …
new experiments or simulations in search of new materials with desired properties. The …
[HTML][HTML] Structure prediction of boron-doped graphene by machine learning
Heteroatom doping has endowed graphene with manifold aspects of material properties and
boosted its applications. The atomic structure determination of doped graphene is vital to …
boosted its applications. The atomic structure determination of doped graphene is vital to …
Bayesian optimization for conformer generation
Generating low-energy molecular conformers is a key task for many areas of computational
chemistry, molecular modeling and cheminformatics. Most current conformer generation …
chemistry, molecular modeling and cheminformatics. Most current conformer generation …
Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations
Scandium is the best alloying element to improve the mechanical properties of industrial Al-
Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc …
Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc …
Computational functionality‐driven design of semiconductors for optoelectronic applications
The rapid development of the semiconductor industry has motivated researchers passion for
accelerating the discovery of advanced optoelectronic materials. Computational functionality …
accelerating the discovery of advanced optoelectronic materials. Computational functionality …
Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy
Spectroscopy is indispensable for determining atomic configurations, chemical bondings,
and vibrational behaviours, which are crucial information for materials development. Despite …
and vibrational behaviours, which are crucial information for materials development. Despite …
Advances in kriging-based autonomous x-ray scattering experiments
Autonomous experimentation is an emerging paradigm for scientific discovery, wherein
measurement instruments are augmented with decision-making algorithms, allowing them to …
measurement instruments are augmented with decision-making algorithms, allowing them to …
[PDF][PDF] 人工智能加速聚合物设计的最新进展和未来前景
周天航， 蓝兴英， 徐春明 - 化工学报, 2023 - researchgate.net
广阔的化学空间蕴藏着近乎无限的可能, 高性能聚合物材料的设计至今仍是一项充满挑战的工作
. 利用实验或高通量计算广泛探索大量样本, 选择其中性能较好的候选材料进行深入研究的传统 …
. 利用实验或高通量计算广泛探索大量样本, 选择其中性能较好的候选材料进行深入研究的传统 …
Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization
Parameter optimization is a long-standing challenge in various production processes.
Particularly, powder film forming processes entail multiscale and multiphysical phenomena …
Particularly, powder film forming processes entail multiscale and multiphysical phenomena …
Role of uncertainty estimation in accelerating materials development via active learning
An active learning strategy using sampling based on uncertainties shows the promise of
accelerating the development of new materials. We study the efficiencies of the active …
accelerating the development of new materials. We study the efficiencies of the active …