Benjamin Heckscher Sjølin,William Sandholt Hansen,Armando Antonio Morin-Martinez et al.
Benjamin Heckscher Sjølin et al.
Workflow managers play a critical role in the efficient planning and execution of complex workloads. A handful of these already exist within the world of computational materials discovery, but their dynamic capabilities are somewhat lacking...
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange [0.03%]
用于材料发现、设计和数据交换的OPTIMADE API的发展与应用
Matthew L Evans,Johan Bergsma,Andrius Merkys et al.
Matthew L Evans et al.
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials an...
Automated prediction of ground state spin for transition metal complexes [0.03%]
过渡金属配合物地面态自旋的自动化预测方法研究
Yuri Cho,Ruben Laplaza,Sergi Vela et al.
Yuri Cho et al.
Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop ...
Maxime van der Heijden,Gabor Szendrei,Victor de Haas et al.
Maxime van der Heijden et al.
Porous electrodes are performance-defining components in electrochemical devices, such as redox flow batteries, as they govern the electrochemical performance and pumping demands of the reactor. Yet, conventional porous electrodes used in r...
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning [0.03%]
ProtAgents：通过大型语言模型物理和机器学习多智能体协作进行蛋白质发现
Alireza Ghafarollahi,Markus J Buehler
Alireza Ghafarollahi
Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate mo...
Deep learning-based recommendation system for metal-organic frameworks (MOFs) [0.03%]
基于深度学习的金属有机框架（MOF）推荐系统
Xiaoqi Zhang,Kevin Maik Jablonka,Berend Smit
Xiaoqi Zhang
This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds...
Mining patents with large language models elucidates the chemical function landscape [0.03%]
使用大型语言模型挖掘专利以阐明化学功能景观
Clayton W Kosonocky,Claus O Wilke,Edward M Marcotte et al.
Clayton W Kosonocky et al.
The fundamental goal of small molecule discovery is to generate chemicals with target functionality. While this often proceeds through structure-based methods, we set out to investigate the practicality of methods that leverage the extensiv...
Kenneth López-Pérez,Taewon D Kim,Ramón Alain Miranda-Quintana
Kenneth López-Pérez
The quantification of molecular similarity has been present since the beginning of cheminformatics. Although several similarity indices and molecular representations have been reported, all of them ultimately reduce to the calculation of mo...
Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks [0.03%]
Correction: 使用深度集成神经网络在终端设备上预测小分子溶解度
Mayk Caldas Ramos,Andrew D White
Mayk Caldas Ramos
[This corrects the article DOI: 10.1039/D3DD00217A.]. This journal is © The Royal Society of Chemistry.
Published Erratum
Digital discovery. 2024 May 3;3(5):1069-1070. DOI:10.1039/d4dd90020k 2024
MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions [0.03%]
基于X射线对分布函数的机器学习结构鉴定工具
Emil T S Kjær,Andy S Anker,Andrea Kirsch et al.
Emil T S Kjær et al.
Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed...