Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines [0.03%]
基于图神经网络的羧酸和烷基胺类化合物构象依赖型DFT描述符的快速预测方法研究
Brittany C Haas,Melissa A Hardy,Shree Sowndarya S V et al.
Brittany C Haas et al.
Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associ...
Jules Lee,Prajakatta Mulay,Matthew J Tamasi et al.
Jules Lee et al.
Oxygen tolerant polymerizations including Photoinduced Electron/Energy Transfer-Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerization allow for high-throughput synthesis of diverse polymer architectures on the benchtop ...
Substrate prediction for RiPP biosynthetic enzymes via masked language modeling and transfer learning [0.03%]
基于掩码语言模型和迁移学习的RiPP生物合成酶底物预测
Joseph D Clark,Xuenan Mi,Douglas A Mitchell et al.
Joseph D Clark et al.
Ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes often exhibit promiscuous substrate preferences that cannot be reduced to simple rules. Large language models are promising tools for predicting t...
PolyCL: contrastive learning for polymer representation learning via explicit and implicit augmentations [0.03%]
基于显式和隐式增强的聚合物表示学习对比学习(PolyCL)
Jiajun Zhou,Yijie Yang,Austin M Mroz et al.
Jiajun Zhou et al.
Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screenin...
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials [0.03%]
基于化学反应的机器学习相互原子势能训练集增强采样生成的自动化方法
Rolf David,Miguel de la Puente,Axel Gomez et al.
Rolf David et al.
The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs overcome the ...
Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing [0.03%]
纠正：一个微笑就够了：使用自然语言处理从SMILES预测活度系数上限
Benedikt Winter,Clemens Winter,Johannes Schilling et al.
Benedikt Winter et al.
[This corrects the article DOI: 10.1039/D2DD00058J.]. This journal is © The Royal Society of Chemistry.
Published Erratum
Digital discovery. 2024 Oct 14;3(11):2384. DOI:10.1039/d4dd90045f 2024
Alexandre A Schoepfer,Jan Weinreich,Ruben Laplaza et al.
Alexandre A Schoepfer et al.
Bayesian optimization (BO) is an efficient method for solving complex optimization problems, including those in chemical research, where it is gaining significant popularity. Although effective in guiding experimental design, BO does not ac...
Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning [0.03%]
基于主动学习的富钛SrTiO3(110)重构界面的探索研究
Ralf Wanzenböck,Esther Heid,Michele Riva et al.
Ralf Wanzenböck et al.
The investigation of inhomogeneous surfaces, where various local structures coexist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the ...
Stefan Hödl,Tal Kachman,Yoram Bachrach et al.
Stefan Hödl et al.
Language models trained on molecular string representations have shown strong performance in predictive and generative tasks. However, practical applications require not only making accurate predictions, but also explainability - the abilit...
Extracting structured data from organic synthesis procedures using a fine-tuned large language model [0.03%]
使用精细调优的大语言模型从有机合成程序中提取结构化数据
Qianxiang Ai,Fanwang Meng,Jiale Shi et al.
Qianxiang Ai et al.
The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstruct...