.png){kind=icon}
返回
Data-efficient machine learning for molecular crystal structure prediction
.png){kind=icon}
OA
AI
DOI：10.1039/d0sc05765g.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Delta-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level-for which an efficient implementation is available-with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP.
Keyword:
GAUSSIAN APPROXIMATION POTENTIALS
QUANTUM-MECHANICS
LATTICE ENERGY
ACCURACY
MP2
CHEMISTRY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.4
论文数:
1.7W
被引数:
9.3W
机构
引用论文
Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
PREAI
分享收藏Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning通过迁移学习接近具有通用神经网络潜力的耦合聚类精度.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Long-range correlation energy calculated from coupled atomic response functions由耦合原子响应函数计算的长程相关能OAAI
OAAI分享收藏Mapping Thermal Expansion Coefficients in Freestanding 2D Materials at the Nanometer Scale在纳米级的独立式2D材料中映射热膨胀系数OAAI
OAAI分享收藏
查看更多内容