Inverse molecular design from first principles: Tailoring organic chromophore spectra for optoelectronic applications

发色团 有机发光二极管 计算机科学 变形 有机太阳能电池 材料科学 吸收(声学) 纳米技术 光电子学 化学 聚合物 光化学 人工智能 图层(电子) 复合材料
作者
James D. Green,Eric G. Fuemmeler,Timothy J. H. Hele
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:156 (18): 180901-180901 被引量:19
标识
DOI:10.1063/5.0082311
摘要

The discovery of molecules with tailored optoelectronic properties, such as specific frequency and intensity of absorption or emission, is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the following question: How can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning, and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties. In this Perspective, we address both of these challenges from first principles. We first adapt the Thomas–Reiche–Kuhn sum rule to organic chromophores and show how this indicates how easily the absorption and emission of a molecule can be improved. We then show how by combining electronic structure theory and intensity borrowing perturbation theory we can predict whether or not the proposed morphing operations will achieve the desired spectral alteration, and thereby derive widely applicable design rules. We go on to provide proof-of-concept illustrations of this approach to optimizing the visible absorption of acenes and the emission of radical OLEDs. We believe that this approach can be integrated into genetic algorithms by biasing morphing operations in favor of those that are likely to be successful, leading to faster molecular discovery and greener chemistry.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
qhcaywy发布了新的文献求助10
刚刚
浮游应助yy采纳,获得10
1秒前
2秒前
4秒前
hAFMET发布了新的文献求助10
5秒前
脑洞疼应助傲娇的云朵采纳,获得10
10秒前
葡萄架发布了新的文献求助10
11秒前
汉堡包应助hAFMET采纳,获得10
14秒前
rainshake完成签到,获得积分10
19秒前
科研通AI2S应助Roger采纳,获得10
21秒前
Xjx6519发布了新的文献求助10
23秒前
Akim应助柳雷采纳,获得30
23秒前
充电宝应助Roger采纳,获得10
27秒前
科研通AI6应助乘风文月采纳,获得30
27秒前
斯文败类应助Xjx6519采纳,获得10
29秒前
lilili应助彼岸花开采纳,获得50
32秒前
祥小哥完成签到,获得积分10
32秒前
浮游应助Roger采纳,获得10
32秒前
领导范儿应助caoju采纳,获得10
33秒前
我是老大应助Jodie采纳,获得10
33秒前
BowieHuang应助Roger采纳,获得10
40秒前
科目三应助科研通管家采纳,获得10
44秒前
脑洞疼应助科研通管家采纳,获得30
44秒前
44秒前
44秒前
脑洞疼应助科研通管家采纳,获得10
44秒前
爆米花应助科研通管家采纳,获得10
44秒前
SciGPT应助科研通管家采纳,获得10
44秒前
科研通AI6应助科研通管家采纳,获得50
44秒前
共享精神应助科研通管家采纳,获得10
44秒前
44秒前
Zx_1993应助科研通管家采纳,获得20
44秒前
打打应助科研通管家采纳,获得10
44秒前
天天快乐应助科研通管家采纳,获得10
44秒前
wanci应助科研通管家采纳,获得10
44秒前
打打应助科研通管家采纳,获得10
44秒前
斯文败类应助科研通管家采纳,获得10
44秒前
Lucas应助科研通管家采纳,获得10
45秒前
45秒前
45秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1601
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 800
Biology of the Reptilia. Volume 21. Morphology I. The Skull and Appendicular Locomotor Apparatus of Lepidosauria 620
A Guide to Genetic Counseling, 3rd Edition 500
Laryngeal Mask Anesthesia: Principles and Practice. 2nd ed 500
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5557651
求助须知:如何正确求助?哪些是违规求助? 4642720
关于积分的说明 14668939
捐赠科研通 4584158
什么是DOI,文献DOI怎么找? 2514615
邀请新用户注册赠送积分活动 1488842
关于科研通互助平台的介绍 1459533