Themed collection Editor’s Choice: Machine Learning for Materials Innovation
Journal of Materials Chemistry A and Materials Advances Editor’s choice web collection: “Machine learning for materials innovation”
Zhen Zhou introduces a Journal of Materials Chemistry/Materials Advances Editor’s choice web collection on machine learning for materials innovation (https://rsc.li/MachineLearning).
High entropy alloy electrocatalysts: a critical assessment of fabrication and performance
Critical assessment of the present status of HEA NPs as catalysts, including an in-depth discussion of computational studies, combinatorial screening, or machine-learning studies to find the optimum composition and structure of HEA electrocatalysts.
Machine learning for renewable energy materials
Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accelerate the discovery renewable energy materials.
A water destructible SnS2 QD/PVA film based transient multifunctional sensor and machine learning assisted stimulus identification for non-invasive personal care diagnostics
We demonstrate, for the first time, a transient, flexible multifunctional sensor (strain, pressure, and breath) using a water soluble SnS2-QD/PVA film.
Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning
We built machine learning-based models to predict the performance of filtration membranes, and integrated them into homemade standalone software (polySML).
Rational design of transition metal single-atom electrocatalysts: a simulation-based, machine learning-accelerated study
With maximum atom-utilization efficiency, single atom catalysts (SACs) are surging as a new research frontier in catalysis science.
Computational design of (100) alloy surfaces for the hydrogen evolution reaction
Based on the understandings of alloying effects in bimetallic (100) surfaces, we explored their four-fold active sites for electrocatalytic hydrogen evolution reaction.
Gapped metals as thermoelectric materials revealed by high-throughput screening
Gapped metals present in their band structure a gap near the Fermi level. This key feature makes these metals comparable to degenerate semiconductors and thus suitable as thermoelectrics. The present screening searches them systematically.
A machine learning scheme for the catalytic activity of alloys with intrinsic descriptors
We develop a universal design scheme based on the machine learning method and the intrinsic properties of substrates and adsorbates, allowing accurate prediction and rapid screening through a large phase space of alloys and multiple adsorbates.
Data-driven pilot optimization for electrochemical CO mass production
Pilot plant optimization of CO2RR system to produce CO via Ag electrodes have been performed and the results are intensely studied via correlation analysis.
Formulation of mix design for 3D printing of geopolymers: a machine learning approach
This work evaluates the application of machine learning in the construction automation.
Fast material search of lithium ion conducting oxides using a recommender system
Fast material search using a recommender system is demonstrated to obtain novel lithium ion conducting oxides.
Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning
Electronic and thermal transport in materials originate from various forms of electron and ion interactions.
Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning
The oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal–air batteries.
Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts
Machine learning (ML) methods would significantly reduce the computational burden of catalysts screening for nitrogen reduction reaction (NRR).
Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives
By a two-step computational process, namely Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML), we screened 50 959 hypothetical pure-silica zeolites and identified 230 preeminent zeolites with excellent adsorption performances.
In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling
The advent of machine learning (ML) techniques in solving problems related to materials science and chemical engineering is driving expectations to give faster predictions of material properties.
Large-scale evaluation of cascaded adsorption heat pumps based on metal/covalent–organic frameworks
High-throughput computational screening of millions of cascaded adsorption heat pumps based on metal–organic frameworks and covalent–organic frameworks.
Rationalizing the interphase stability of Li|doped-Li7La3Zr2O12via automated reaction screening and machine learning
Lithium metal batteries are a promising candidate for future high-energy-density energy storage.
First-principles study of alkali-metal intercalation in disordered carbon anode materials
The intercalation of alkali metals in disordered carbon anode materials is studied by a combination of first-principles and machine-learning methods.
Designing promising molecules for organic solar cells via machine learning assisted virtual screening
Rational design of new OPV molecules via virtual screening of candidate materials using high-performing machine learning models.
Rational design of hydrocarbon-based sulfonated copolymers for proton exchange membranes
Developing novel hydrocarbon-based proton exchange membranes is at the Frontier of research on fuel cells, batteries and electrolysis, aiming to reach the demand for advanced performance in proton conductivity, fuel retardation, swelling, mechanical and thermal stability etc.
Enhancing photovoltaic performance by tuning the domain sizes of a small-molecule acceptor by side-chain-engineered polymer donors
This paper reports side-chain-engineered polymer donors and a small-molecule acceptor that are capable of simultaneous charge and energy transfer as the active layer for organic photovoltaics.
Predicting performance limits of methane gas storage in zeolites with an artificial neural network
Crystalline nanoporous materials (i.e. shapes) were generated in the energy space using an artificial neural network.
About this collection
Associate Editor for Journal of Materials Chemistry A and Materials Advances, Zhen Zhou (Nankai University, China), has selected a few outstanding recent manuscripts on Machine Learning for Materials Innovation for this Editor’s Choice collection. In order to highlight developments in Machine Learning for Materials Innovation, this online collection includes recent manuscripts from Journal of Materials Chemistry A and Materials Advances on the topic.