Themed collection Digital Discovery – Editors Choice Collection 2023

16 items
Open Access Perspective

A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows

The capability to replicate the predictions by machine learning (ML) or artificial intelligence (AI) models and the results in scientific workflows that incorporate such ML/AI predictions is driven by a variety of factors.

Graphical abstract: A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
Open Access Perspective

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.

Graphical abstract: 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Open Access Tutorial Review

Recent advances in the self-referencing embedded strings (SELFIES) library

We describe the current state of the SELFIES library (version 2.1.1), and, in particular, the advances and improvements we have made in its underlying algorithms, design, and API.

Graphical abstract: Recent advances in the self-referencing embedded strings (SELFIES) library
Open Access Communication

The materials experiment knowledge graph

Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.

Graphical abstract: The materials experiment knowledge graph
Open Access Paper

Towards a modular architecture for science factories

Advances in robotic automation, high-performance computing, and artificial intelligence encourage us to propose large, general-purpose science factories with the scale needed to tackle large discovery problems and to support thousands of scientists.

Graphical abstract: Towards a modular architecture for science factories
From the themed collection: Accelerate Conference 2022
Open Access Paper

Autonomous biomimetic solid dispensing using a dual-arm robotic manipulator

An automated solid dispenser was developed using a dual-arm robot and fuzzy logic controller, mimicking the operations of human researchers.

Graphical abstract: Autonomous biomimetic solid dispensing using a dual-arm robotic manipulator
Open Access Paper

Driving school for self-driving labs

Self-driving labs benefit from occasional and asynchronous human interventions. We present a heuristic framework for how self-driving lab operators can interpret progress and make changes during a campaign.

Graphical abstract: Driving school for self-driving labs
From the themed collection: Accelerate Conference 2022
Open Access Paper

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

We used synthetically generated crystals to train ResNet-like models to enhance the prediction of space groups from ICSD powder X-ray diffractograms. The results show improved generalization to unseen structure types compared to previous approaches.

Graphical abstract: Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
From the themed collection: Accelerate Conference 2022
Open Access Paper

A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform

A sonochemical Materials Acceleration Platform was implemented to synthesize CdSe nanocrystals under 625 unique conditions (in triplicate) in less than 6 weeks. The modularity of the workflow is adaptable to a variety of applications.

Graphical abstract: A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform
From the themed collection: Accelerate Conference 2022
Open Access Paper

Chemical design with GPU-based Ising machines

Ising machines are used to create molecules with desired properties. GPU-based Ising machines are shown to outperform qubit-based ones in terms of scalability.

Graphical abstract: Chemical design with GPU-based Ising machines
Open Access Paper

Automated patent extraction powers generative modeling in focused chemical spaces

Automated patent mining creates domain-specific datasets of molecular structures for generative modeling with limited human intervention.

Graphical abstract: Automated patent extraction powers generative modeling in focused chemical spaces
Open Access Paper

Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research

We present ODACell, an automated electrolyte formulation and coin cell assembly system for accelerated battery research.

Graphical abstract: Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research
Open Access Paper

Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions

Variational-autoencoders with an additional predictor neural-network and gradient-based optimization allow us to generate new Suzuki-catalysts and predict the binding energies.

Graphical abstract: Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions
Open Access Paper

Group SELFIES: a robust fragment-based molecular string representation

Group SELFIES is a molecular string representation that incorporates tokens which represent substructures while maintaining robustness, which improves the performance of molecular generative models.

Graphical abstract: Group SELFIES: a robust fragment-based molecular string representation
Open Access Paper

A fully automated platform for photoinitiated RAFT polymerization

The use of robotic instrumentation and Python scripts allows for fully automated and robust combinatorial polymer synthesis.

Graphical abstract: A fully automated platform for photoinitiated RAFT polymerization
From the themed collection: Accelerate Conference 2022
Open Access Paper

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

We present an interpretable uncertainty-aware machine learning model to predict battery degradation trajectories. Using LSTM Recurrent Neural Networks, we reach an RMSE of 106 and MAPE of 10.6%.

Graphical abstract: Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory
16 items

About this collection

This collection highlights some of the very best work published in Digital Discovery in 2023, carefully chosen by our dedicated editorial team and the Editor-in-Chief, Alán Aspuru-Guzik.

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