Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.

Themed collection Accelerating Chemistry Symposium Collection

30 items
Open Access Minireview

Machine-enabled inverse design of inorganic solid materials: promises and challenges

The grand challenge of materials science, discovery of novel materials with target properties, can be greatly accelerated by machine-learned inverse design strategies.

Graphical abstract: Machine-enabled inverse design of inorganic solid materials: promises and challenges
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Minireview

Progress and prospects for accelerating materials science with automated and autonomous workflows

Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.

Graphical abstract: Progress and prospects for accelerating materials science with automated and autonomous workflows
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD).

Graphical abstract: SyntaLinker: automatic fragment linking with deep conditional transformer neural networks
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system.

Graphical abstract: Predicting the chemical reactivity of organic materials using a machine-learning approach
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Discovery of a synthesis method for a difluoroglycine derivative based on a path generated by quantum chemical calculations

QCaRA successfully predicted a new synthetic path based on the reaction path network produced by quantum chemical calculation.

Graphical abstract: Discovery of a synthesis method for a difluoroglycine derivative based on a path generated by quantum chemical calculations
From the themed collection: Celebrating 10 years of Chemical Science
Open Access Edge Article

Pushing property limits in materials discovery via boundless objective-free exploration

Our developed algorithm, BLOX (BoundLess Objective-free eXploration), successfully found “out-of-trend” molecules potentially useful for photofunctional materials from a drug database.

Graphical abstract: Pushing property limits in materials discovery via boundless objective-free exploration
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Geometric landscapes for material discovery within energy–structure–function maps

We introduce a representation for the geometric features of the pores of porous molecular crystals. This representation provides a good basis for supervised (predict adsorption properties) and unsupervised (polymorph classification) tasks.

Graphical abstract: Geometric landscapes for material discovery within energy–structure–function maps
From the themed collection: 2020 Chemical Science HOT Article Collection
Open Access Edge Article

Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids

The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression. We devise structural mechanics statistical learning method to reveal their molecular origin in terms of chemical interactions.

Graphical abstract: Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery

Evolutionary optimisation and crystal structure prediction are used to explore chemical space for molecular organic semiconductors.

Graphical abstract: Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery
From the themed collection: 2020 Chemical Science HOT Article Collection
Open Access Edge Article

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

A machine learning exploration of the chemical space surrounding Vaska's complex.

Graphical abstract: Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Spectral deep learning for prediction and prospective validation of functional groups

A new multi-label deep neural network architecture is used to combine Infrared and mass spectra, trained on single compounds to predict functional groups, and experimentally validated on complex mixtures.

Graphical abstract: Spectral deep learning for prediction and prospective validation of functional groups
From the themed collection: 2020 Chemical Science HOT Article Collection
Open Access Edge Article

Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks

A neural network is used to reveal composition-dependent structural evolution under operando conditions in CuZn nanocatalysts for CO2 electroreduction.

Graphical abstract: Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Automatic retrosynthetic route planning using template-free models

Retrosynthetic pathway planning using a template-free model coupled with heuristic Monte Carlo tree search.

Graphical abstract: Automatic retrosynthetic route planning using template-free models
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

UCBShift predicts NMR chemical shifts of proteins that exceeds accuracy of other popular chemical shift predictors on real-world data sets.

Graphical abstract: Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data
From the themed collection: 2020 Chemical Science HOT Article Collection
Open Access Edge Article

Optical monitoring of polymerizations in droplets with high temporal dynamic range

Two complementary measurements, fluorescence polarization anisotropy and aggregation-induced emission, allow for in situ optical monitoring of polymerization reaction progress in droplets across varying temporal regimes of the reaction.

Graphical abstract: Optical monitoring of polymerizations in droplets with high temporal dynamic range
From the themed collection: 2020 Chemical Science HOT Article Collection
Open Access Edge Article

IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy

The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours.

Graphical abstract: IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain

Computer Assisted Synthesis Planning (CASP), datasets and their performance.

Graphical abstract: Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights

New crystal forms of two well-studied organic molecules are identified in a computationally targeted way, by combining structure prediction with a robotic crystallisation screen, including a ‘hidden’ porous polymorph of trimesic acid.

Graphical abstract: Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Accelerated robotic discovery of type II porous liquids

High-throughput automation was used to streamline the synthesis, characterisation, and solubility testing, of new Type II porous liquids, accelerating their discovery.

Graphical abstract: Accelerated robotic discovery of type II porous liquids
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Delfos: deep learning model for prediction of solvation free energies in generic organic solvents

We introduce Delfos, a novel, machine-learning-based QSPR method which predicts solvation free energies for generic organic solutions.

Graphical abstract: Delfos: deep learning model for prediction of solvation free energies in generic organic solvents
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Machine learning enables long time scale molecular photodynamics simulations

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

Graphical abstract: Machine learning enables long time scale molecular photodynamics simulations
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Efficient multi-objective molecular optimization in a continuous latent space

We utilize Particle Swarm Optimization to optimize molecules in a machine-learned continuous chemical representation with respect to multiple objectives such as biological activity, structural constrains or ADMET properties.

Graphical abstract: Efficient multi-objective molecular optimization in a continuous latent space
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Mapping binary copolymer property space with neural networks

We map the property space of binary copolymers to understand how copolymerisation can be used to tune the optoelectronic properties of polymers.

Graphical abstract: Mapping binary copolymer property space with neural networks
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans

A family of network algorithms allows the Chematica retrosynthetic platform to plan both cost-effective and chemically diverse syntheses.

Graphical abstract: Selection of cost-effective yet chemically diverse pathways from the networks of computer-generated retrosynthetic plans
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.

Graphical abstract: How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.

Graphical abstract: Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

A graph-convolutional neural network model for the prediction of chemical reactivity

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

Graphical abstract: A graph-convolutional neural network model for the prediction of chemical reactivity
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

An evolutionary algorithm for the discovery of porous organic cages

An evolutionary algorithm is developed and used to search for shape persistent porous organic cages.

Graphical abstract: An evolutionary algorithm for the discovery of porous organic cages
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories

Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor.

Graphical abstract: Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
From the themed collection: Accelerating Chemistry Symposium Collection
Open Access Edge Article

Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

The standard paradigm in computational materials science is INPUT: STRUCTURE; OUTPUT: PROPERTIES, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace.

Graphical abstract: Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure
From the themed collection: Accelerating Chemistry Symposium Collection
30 items

About this collection

In honour of our second Chemical Science symposium titled ‘How can machine learning and autonomy accelerate chemistry?’, being held virtually in September 2020, we have put together a special collection of some of our favourite articles in this area, including a few by some of the speakers and scientific committee of the symposium.

Spotlight

Advertisements