☆ 4.8 Review

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

CHEMICAL REVIEWS (2022)

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Hydrogen Adsorption on Defective Nitrogen-Doped Carbon Nanotubes Explained via Machine Learning Augmented DFT Calculations and Game-Theoretic Feature Attributions

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Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning

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Machine Learning-Guided Discovery of Underlying Decisive Factors and New Mechanisms for the Design of Nonprecious Metal Electrocatalysts

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Process optimization and enhancement of pesticide adsorption by porous adsorbents by regression analysis and parametric modelling

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Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Qiuling Tao et al.

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Stepwise Evolution of Photocatalytic Spinel-Structured (Co,Cr,Fe,Mn,Ni)3O4 High Entropy Oxides from First-Principles Calculations to Machine Learning

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CRYSTALS (2021)

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Process optimization and kinetics analysis for photocatalytic degradation of emerging contaminant using N-doped TiO2-SiO2 nanoparticle: Artificial Neural Network and Surface Response Methodology approach

Shimelis Kebede Kassahun et al.

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ENVIRONMENTAL TECHNOLOGY & INNOVATION (2021)

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Article Chemistry, Physical

Deep learning for ultra-fast and high precision screening of energy materials

Zhilong Wang et al.

Summary: This study successfully predicted the high-precision band gaps of 1,503 binary metallic oxides, nitrides, and sulfides using a deep learning model; transfer learning was employed to train the model with less than 5% of the dataset and predict the band gaps of the rest of the structures; this work demonstrates the feasibility of transfer learning in band gap prediction, with a computation speed significantly faster than traditional methods.

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Article Multidisciplinary Sciences

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts

Haoxin Mai et al.

Summary: The study introduces a machine learning approach to rapidly screen potential photocatalysts, successfully predicting two promising compounds with confirmed photocatalytic activity. The research highlights the potential of machine learning in accelerating the discovery of next-generation photocatalysts.

ISCIENCE (2021)

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Review Chemistry, Multidisciplinary

Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications

Haoxin Mai et al.

Summary: Solar energy is attractive for being free, renewable, abundant, and sustainable. Photocatalysis using perovskites has gained attention for pollutant degradation and fuel production. The tailored properties of perovskites make them promising for efficient and sustainable photocatalysts.

CHEMICAL SOCIETY REVIEWS (2021)

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Article Automation & Control Systems

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

Marco Fronzi et al.

Summary: The bandgap, a fundamental property of condensed matter, was accurately predicted for a large number of novel 2D heterostructures using active machine learning algorithms. This approach leveraged a limited number of accurate density functional theory calculations to produce a database of approximately 2.2 million bandgap values.

ADVANCED INTELLIGENT SYSTEMS (2021)

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Review Materials Science, Multidisciplinary

Autonomous experimentation systems for materials development: A community perspective

Eric Stach et al.

Summary: Materials research and development are crucial for solving world problems, and the partnership between humans and robots can accelerate technological advancements. The new paradigm brings both challenges and opportunities, requiring collaborative efforts across academia, industry, government, and funding agencies.

MATTER (2021)

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Article Chemistry, Physical

Machine Learning-Based Screening of Highly Stable and Active Ternary Pt Alloys for Oxygen Reduction Reaction

Joohwi Lee et al.

Summary: A novel screening strategy combining machine learning and first-principles calculations is proposed to efficiently find stable and active ternary Pt alloys, significantly reducing the number of candidate structures and achieving efficient predictions.

JOURNAL OF PHYSICAL CHEMISTRY C (2021)

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Article Chemistry, Physical

Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C2N by DFT and machine learning

Yiran Ying et al.

Summary: This study showcases the promising bifunctional OER/ORR catalytic performance of single-atom catalysts supported on the C2N monolayer, with Rh@C2N, Au, and Pd@C2N demonstrating superior activity. The origin of this catalytic activity is revealed through DFT calculations and ML modelling, shedding light on the underlying mechanisms and element-specific activity.

JOURNAL OF MATERIALS CHEMISTRY A (2021)

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Article Engineering, Environmental

Electrocatalytic degradation of sulfamethazine on IrO2-RuO2 composite electrodes: influencing factors, kinetics and modeling

Naichuan Yu et al.

Summary: In this study, IrO2-RuO2 composite electrodes were prepared and used to effectively degrade SMT in wastewater with a removal rate of 49.17%, below China's urban sewage COD discharge standard. Mathematical and artificial neural network models were established to predict and guide the removal of COD from SMT-containing wastewater using IrO2-RuO2 composite electrodes.

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2021)

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Article Chemistry, Multidisciplinary

Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

Xiaobo Li et al.

Summary: Organic molecules play a crucial role in photocatalysts, but reliable structure-property design rules are difficult to formulate. By integrating experiment, computation, and machine learning, researchers were able to identify common structural features in molecules with high catalytic activity. The study marks a step towards the machine-learning assisted discovery of molecular organic photocatalysts, with potential applications beyond photocatalytic hydrogen evolution.

CHEMICAL SCIENCE (2021)

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Article Materials Science, Multidisciplinary

Machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery

Andrew S. Rosen et al.

Summary: This study introduces a Quantum MOF (QMOF) database, which contains computed quantum-chemical properties for over 14,000 experimentally synthesized MOFs. Machine learning models trained on this database can efficiently discover MOFs with targeted properties, with the successful prediction of MOFs with low band gaps being highlighted.

MATTER (2021)

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Article Computer Science, Artificial Intelligence

Inverse design of nanoporous crystalline reticular materials with deep generative models

Zhenpeng Yao et al.

Summary: Reticular frameworks are crystalline porous materials that have desirable properties for various applications, but the diversity of building blocks presents challenges for materials design. An automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder is proposed for generative design of reticular materials, showing high fidelity in capturing structural features and promising optimization capability for gas separation.

NATURE MACHINE INTELLIGENCE (2021)

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Review Nanoscience & Nanotechnology

Emerging materials intelligence ecosystems propelled by machine learning

Rohit Batra et al.

Summary: The age of cognitive computing and artificial intelligence is emerging, with AI ecosystems flourishing in materials science and engineering. Machine learning algorithms are utilized to create surrogate models of materials properties, transforming the computational and physical laboratory infrastructural landscapes. Integration of various ML landscape parts may lead to materials-savvy digital assistants and human-machine partnerships for efficient materials screening and discovery.

NATURE REVIEWS MATERIALS (2021)

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Review Chemistry, Multidisciplinary

Particulate Photocatalysts for Light-Driven Water Splitting: Mechanisms, Challenges, and Design Strategies

Qian Wang et al.

CHEMICAL REVIEWS (2020)

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Article Engineering, Environmental

Selective photocatalytic oxidation of cyclohexanol to cyclohexanone: A spectroscopic and kinetic study

M. R. Karimi Estahbanati et al.

CHEMICAL ENGINEERING JOURNAL (2020)

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Article Chemistry, Physical

Artificial Intelligence to Accelerate the Discovery of N2 Electroreduction Catalysts

Myungjoon Kim et al.

CHEMISTRY OF MATERIALS (2020)

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Article Chemistry, Physical

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Lei Ge et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Chemistry, Physical

Oxygen Reduction Activities of Strained Platinum Core-Shell Electrocatalysts Predicted by Machine Learning

Marlon Rueck et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Multidisciplinary Sciences

Ultrafast machine vision with 2D material neural network image sensors

Lukas Mennel et al.

NATURE (2020)

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Article Chemistry, Physical

Enhanced photocatalytic hydrogen evolution from organic semiconductor heterojunction nanoparticles

Jan Kosco et al.

NATURE MATERIALS (2020)

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Article Multidisciplinary Sciences

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Sukriti Singh et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

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Article Multidisciplinary Sciences

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Jin-Woong Lee et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Physical

Automated in Silico Design of Homogeneous Catalysts

Marco Foscato et al.

ACS CATALYSIS (2020)

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Article Chemistry, Physical

High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions

Jack K. Pedersen et al.

ACS CATALYSIS (2020)

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Review Chemistry, Physical

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Takashi Toyao et al.

ACS CATALYSIS (2020)

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Article Chemistry, Physical

Role of Electrocatalysis in the Remediation of Water Pollutants

Nirala Singh et al.

ACS CATALYSIS (2020)

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Article Chemistry, Physical

Accelerating Atomic Catalyst Discovery by Theoretical Calculations-Machine Learning Strategy

Mingzi Sun et al.

ADVANCED ENERGY MATERIALS (2020)

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Review Chemistry, Physical

A Critical Review of Machine Learning of Energy Materials

Chi Chen et al.

ADVANCED ENERGY MATERIALS (2020)

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Article Multidisciplinary Sciences

Learning to learn from data: Using deep adversarial learning to construct optimal statistical procedures

Alex Luedtke et al.

SCIENCE ADVANCES (2020)

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Article Multidisciplinary Sciences

Inverse design of porous materials using artificial neural networks

Baekjun Kim et al.

SCIENCE ADVANCES (2020)

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Review Chemistry, Multidisciplinary

Machine Learning in Catalysis, From Proposal to Practicing

Wenhong Yang et al.

ACS OMEGA (2020)

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Review Chemistry, Multidisciplinary

Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation

Geun Ho Gu et al.

ADVANCED MATERIALS (2020)

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Article Chemistry, Physical

Scalable approach to high coverages on oxides via iterative training of a machine-learning algorithm

Fuzhu Liu et al.

CHEMCATCHEM (2020)

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Article Chemistry, Multidisciplinary

Toward On-Demand Materials Synthesis and Scientific Discovery through Intelligent Robots

Jiagen Li et al.

ADVANCED SCIENCE (2020)

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Article Chemistry, Physical

High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

Jingnan Zheng et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2020)

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Article Chemistry, Physical

Machine-Learning-Enabled Exploration of Morphology Influence on Wire-Array Electrodes for Electrochemical Nitrogen Fixation

Benjamin B. Hoar et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Multidisciplinary Sciences

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

Joshua L. Lansford et al.

NATURE COMMUNICATIONS (2020)

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Article Multidisciplinary Sciences

Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

Jiagen Li et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Physical

An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts

Zheng Li et al.

ACS CATALYSIS (2020)

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Article Multidisciplinary Sciences

Accelerated discovery of CO2 electrocatalysts using active machine learning

Miao Zhong et al.

NATURE (2020)

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Article Neurosciences

Machine translation of cortical activity to text with an encoder-decoder framework

Joseph G. Makin et al.

NATURE NEUROSCIENCE (2020)

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Review Chemistry, Multidisciplinary

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka et al.

CHEMICAL REVIEWS (2020)

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Article Environmental Sciences

A generalized predictive model for TiO2-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network

Zhuoying Jiang et al.

ENVIRONMENTAL RESEARCH (2020)

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Article Chemistry, Multidisciplinary

Generative Adversarial Networks for Crystal Structure Prediction

Sungwon Kim et al.

ACS CENTRAL SCIENCE (2020)

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Article Food Science & Technology

Structural characterization and catalytic sterilization performance of a TiO2 nano-photocatalyst

Yue Yang et al.

FOOD SCIENCE & NUTRITION (2020)

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Article Chemistry, Physical

Covalency competition dominates the water oxidation structure-activity relationship on spinel oxides

Yuanmiao Sun et al.

NATURE CATALYSIS (2020)

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Review Chemistry, Physical

Toward Excellence of Transition Metal-Based Catalysts for CO2Electrochemical Reduction: An Overview of Strategies and Rationales

Mengran Li et al.

SMALL METHODS (2020)

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Article Chemistry, Physical

Descriptors for Hydrogen Evolution on Single Atom Catalysts in Nitrogen-Doped Graphene

Victor Fung et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2020)

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Article Chemistry, Physical

Rational Prediction of Single Metal Atom Supported on Two-Dimensional Metal Diborides for Electrocatalytic N2 Reduction Reaction with Integrated Descriptor

Lei Ge et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Chemistry, Physical

Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials

Xiang Sun et al.

APPLIED SURFACE SCIENCE (2020)

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Article Multidisciplinary Sciences

A mobile robotic chemist

Benjamin Burger et al.

NATURE (2020)

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Article Chemistry, Multidisciplinary

Role of Artificial Intelligence and Machine Learning in Nanosafety

David A. Winkler

SMALL (2020)

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Article Multidisciplinary Sciences

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Baicheng Weng et al.

NATURE COMMUNICATIONS (2020)

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Review Chemistry, Physical

Why Do We Use the Materials and Operating Conditions We Use for Heterogeneous (Photo)Electrochemical Water Splitting?

Ananth Govind Rajan et al.

ACS CATALYSIS (2020)

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Article Environmental Sciences

Degradation of sulfonamide antibiotics using ozone-based advanced oxidation process: Experimental, modeling, transformation mechanism and DFT study

Rasool Pelalak et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

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Article Multidisciplinary Sciences

Designing and understanding light-harvesting devices with machine learning

Florian Hase et al.

NATURE COMMUNICATIONS (2020)

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Article Chemistry, Multidisciplinary

Nonequilibrium Dynamics of Proton-Coupled Electron Transfer in Proton Wires: Concerted but Asynchronous Mechanisms

Joshua J. Goings et al.

ACS CENTRAL SCIENCE (2020)

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Article Chemistry, Physical

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Mohammad Rezaul Karim et al.

ACS APPLIED ENERGY MATERIALS (2020)

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Article Chemistry, Physical

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

Rohit Batra et al.

CHEMISTRY OF MATERIALS (2020)

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Article Chemistry, Applied

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters

Marc O. J. Jager et al.

ACS COMBINATORIAL SCIENCE (2020)

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Article Chemistry, Physical

Understanding activity origin for the oxygen reduction reaction on bi-atom catalysts by DFT studies and machine-learning

Chaofang Deng et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Chemistry, Physical

Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy

Xiaoxu Wang et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Multidisciplinary Sciences

Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

Francesco Pellegrino et al.

SCIENTIFIC REPORTS (2020)

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Article Chemistry, Physical

Machine learning property prediction for organic photovoltaic devices

Nastaran Meftahi et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Materials Science, Multidisciplinary

Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects

Zhuole Lu et al.

MATTER (2020)

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Article Chemistry, Physical

First-principles based machine learning study of oxygen evolution reactions of perovskite oxides using a surface center-environment feature model

Xiaomeng Wang et al.

APPLIED SURFACE SCIENCE (2020)

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Article Chemistry, Physical

In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling

Shivam Saxena et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Chemistry, Physical

Quantitative structure-property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning

Jesse S. Dondapati et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)

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Review Chemistry, Multidisciplinary

QSAR without borders

Eugene N. Muratov et al.

CHEMICAL SOCIETY REVIEWS (2020)

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Article Chemistry, Physical

High entropy alloy electrocatalysts: a critical assessment of fabrication and performance

Gracita M. Tomboc et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Chemistry, Multidisciplinary

Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study

Alireza Baghban et al.

RSC ADVANCES (2020)

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Review Chemistry, Multidisciplinary

Metal-free photocatalysts for hydrogen evolution

Mohammad Ziaur Rahman et al.

CHEMICAL SOCIETY REVIEWS (2020)

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Article Chemistry, Physical

Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning

Shiru Lin et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Chemistry, Physical

Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

Mohammad Zafari et al.

JOURNAL OF MATERIALS CHEMISTRY A (2020)

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Article Chemistry, Multidisciplinary

Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics

Marcos F. Calegari Andrade et al.

CHEMICAL SCIENCE (2020)

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Article Chemistry, Multidisciplinary

The development of an artificial neural network - genetic algorithm model (ANN-GA) for the adsorption and photocatalysis of methylene blue on a novel sulfur-nitrogen co-doped Fe2O3 nanostructure surface

Roya Mohammadzadeh Kakhki et al.

RSC ADVANCES (2020)

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Article Chemistry, Physical

Active learning-based framework for optimal reaction mechanism selection from microkinetic modeling: a case study of electrocatalytic oxygen reduction reaction on carbon nanotubes

Aleksandr A. Kurilovich et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)

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Article Chemistry, Physical

Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production

Elif Can et al.

APPLIED CATALYSIS B-ENVIRONMENTAL (2019)

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Review Chemistry, Multidisciplinary

Cocatalysts for Selective Photoreduction of CO2 into Solar Fuels

Xin Li et al.

CHEMICAL REVIEWS (2019)

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Review Computer Science, Information Systems

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Zahangir Alom et al.

ELECTRONICS (2019)

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Article Nanoscience & Nanotechnology

Computational Discovery and Design of MXenes for Energy Applications: Status, Successes, and Opportunities

Cheng Zhan et al.

ACS APPLIED MATERIALS & INTERFACES (2019)

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Article Chemistry, Multidisciplinary

Defect-Tailoring Mediated Electron-Hole Separation in Single-Unit-Cell Bi3O4Br Nanosheets for Boosting Photocatalytic Hydrogen Evolution and Nitrogen Fixation

Jun Di et al.

ADVANCED MATERIALS (2019)

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Article Chemistry, Physical

Simulation and optimization of the post plasma-catalytic system for toluene degradation by a hybrid ANN and NSGA-II method

Tian Chang et al.

APPLIED CATALYSIS B-ENVIRONMENTAL (2019)

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Review Chemistry, Physical

Machine Learning for Computational Heterogeneous Catalysis

Philomena Schlexer Lamoureux et al.

CHEMCATCHEM (2019)

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Article Chemistry, Physical

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chi Chen et al.

CHEMISTRY OF MATERIALS (2019)

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Article Chemistry, Multidisciplinary

Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory

Yang Bai et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2019)

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Article Chemistry, Multidisciplinary

Unravelling mechanistic features of organocatalysis with in situ modifications at the secondary sphere

Vasudevan Dhayalan et al.

NATURE CHEMISTRY (2019)

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Article Physics, Multidisciplinary

Identifying topological order through unsupervised machine learning

Joaquin F. Rodriguez-Nieva et al.

NATURE PHYSICS (2019)

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Review Chemistry, Multidisciplinary

Intermetallic Nanoparticles: Synthetic Control and Their Enhanced Electrocatalysis

Junrui Li et al.

ACCOUNTS OF CHEMICAL RESEARCH (2019)

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Article Chemistry, Physical

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts

Seoin Back et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

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Article Multidisciplinary Sciences

Machine learning in electronic-quantum-matter imaging experiments

Yi Zhang et al.

NATURE (2019)

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Editorial Material Multidisciplinary Sciences

Three pitfalls to avoid in machine learning

Patrick Riley

NATURE (2019)

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Article Multidisciplinary Sciences

Unsupervised word embeddings capture latent knowledge from materials science literature

Vahe Tshitoyan et al.

NATURE (2019)

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Article Multidisciplinary Sciences

Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data

Qian Liu et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Tian Xie et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Physically informed artificial neural networks for atomistic modeling of materials

G. P. Purja Pun et al.

NATURE COMMUNICATIONS (2019)

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Review Nanoscience & Nanotechnology

High-entropy alloys

Easo P. George et al.

NATURE REVIEWS MATERIALS (2019)

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Article Chemistry, Physical

Semi-supervised machine-learning classification of materials synthesis procedures

Haoyan Huo et al.

NPJ COMPUTATIONAL MATERIALS (2019)

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Review Chemistry, Physical

First-principles-based multiscale modelling of heterogeneous catalysis

Albert Bruix et al.

NATURE CATALYSIS (2019)

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Article Chemistry, Multidisciplinary

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer et al.

ADVANCED MATERIALS (2019)

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Article Chemistry, Physical

Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning

Daniel W. Davies et al.

CHEMISTRY OF MATERIALS (2019)

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Article Chemistry, Multidisciplinary

Proton-Coupled Electron Transfer Drives Long-Range Proton Translocation in Bioinspired Systems

Emmanuel Odella et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2019)

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Article Multidisciplinary Sciences

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Xiwen Jia et al.

NATURE (2019)

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Article Chemistry, Physical

Using Artificial Intelligence To Forecast Water Oxidation Catalysts

Regina Palkovits et al.

ACS CATALYSIS (2019)

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Article Chemistry, Physical

Inverting X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors

Janis Timoshenko et al.

ACS CATALYSIS (2019)

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Review Chemistry, Multidisciplinary

Data-Driven Materials Science: Status, Challenges, and Perspectives

Lauri Himanen et al.

ADVANCED SCIENCE (2019)

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Review Energy & Fuels

Current understanding and challenges of solar-driven hydrogen generation using polymeric photocatalysts

Yiou Wang et al.

NATURE ENERGY (2019)

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Review Chemistry, Multidisciplinary

Electronic Circular Dichroism Spectroscopy of Proteins

David M. Rogers et al.

CHEM (2019)

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Article Multidisciplinary Sciences

Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis

James Dean et al.

SCIENCE ADVANCES (2019)

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Article Environmental Sciences

Modeling and optimization of fixed mode dual effect (photocatalysis and photo-Fenton) assisted Metronidazole degradation using ANN coupled with genetic algorithm

Steffi Talwar et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2019)

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Article Multidisciplinary Sciences

Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals

Rodrigo Garcia-Muelas et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Dipendra Jha et al.

NATURE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K. T. Schuett et al.

NATURE COMMUNICATIONS (2019)

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Article Chemistry, Physical

Machine Learning for Accelerated Discovery of Solar Photocatalysts

Hassan Masood et al.

ACS CATALYSIS (2019)

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Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning

J. A. Aguiar et al.

SCIENCE ADVANCES (2019)

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Article Materials Science, Multidisciplinary

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

Konstantin Gubaev et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

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Article Chemistry, Multidisciplinary

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning

Janis Timoshenko et al.

NANO LETTERS (2019)

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Electrochemical Energy Conversion on Intermetallic Compounds: A Review

Leonard Roessner et al.

ACS CATALYSIS (2019)

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Article Chemistry, Applied

Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides

Masanori Kaneko et al.

JOURNAL OF ENERGY CHEMISTRY (2019)

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Article Materials Science, Multidisciplinary

Inverse Design of Solid-State Materials via a Continuous Representation

Juhwan Noh et al.

MATTER (2019)

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Article Materials Science, Multidisciplinary

Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning

Song Wang et al.

ACS MATERIALS LETTERS (2019)

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Review Chemistry, Physical

Atomic Arrangement Engineering of Metallic Nanocrystals for Energy-Conversion Electrocatalysis

Jiashun Liang et al.

JOULE (2019)

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Promoting nitrogen electroreduction to ammonia with bismuth nanocrystals and potassium cations in water

Yu-Chen Hao et al.

NATURE CATALYSIS (2019)

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Machine learning for renewable energy materials

Geun Ho Gu et al.

JOURNAL OF MATERIALS CHEMISTRY A (2019)

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Article Chemistry, Physical

High-Entropy Alloys as a Discovery Platform for Electrocatalysis

Thomas A. A. Batchelor et al.

JOULE (2019)

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Review of Deep Learning Algorithms and Architectures

Ajay Shrestha et al.

IEEE ACCESS (2019)

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Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges

Martin A. Mosquera et al.

ACS ENERGY LETTERS (2018)

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Machine Learning in Computer-Aided Synthesis Planning

Connor W. Coley et al.

ACCOUNTS OF CHEMICAL RESEARCH (2018)

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Review Chemistry, Multidisciplinary

Proton-Coupled Electron Transfer in Artificial Photosynthetic Systems

S. Jimena Mora et al.

ACCOUNTS OF CHEMICAL RESEARCH (2018)

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Article Nanoscience & Nanotechnology

Above-Band Gap Photoinduced Stabilization of Engineered Ferroelectric Domains

Haoxin Mai et al.

ACS APPLIED MATERIALS & INTERFACES (2018)

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Random Alloyed versus Intermetallic Nanoparticles: A Comparison of Electrocatalytic Performance

Jocelyn T. L. Gamler et al.

ADVANCED MATERIALS (2018)

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Article Biochemistry & Molecular Biology

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Daniel S. Kermany et al.

CELL (2018)

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Green and Sustainable Solvents in Chemical Processes

Coby J. Clarke et al.

CHEMICAL REVIEWS (2018)

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Recent advances in three-dimensional graphene based materials for catalysis applications

Bocheng Qiu et al.

CHEMICAL SOCIETY REVIEWS (2018)

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Article Chemistry, Physical

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

Arunkumar Chitteth Rajan et al.

CHEMISTRY OF MATERIALS (2018)

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Mechanistic aspects of CO2 reduction catalysis with manganese-based molecular catalysts

David C. Grills et al.

COORDINATION CHEMISTRY REVIEWS (2018)

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Article Chemistry, Medicinal

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies

Lorena Simon-Vidal et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

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GA Landrum et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2005)

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Article Chemistry, Multidisciplinary

The Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics

C Steinbeck et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2003)

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Article Chemistry, Multidisciplinary

Reoptimization of MDL keys for use in drug discovery

JL Durant et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2002)

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Article Automation & Control Systems

Feature selection in principal component analysis of analytical data

Q Guo et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2002)

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Article Biochemical Research Methods

Beware of q(2)!

A Golbraikh et al.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2002)

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Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)

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Article Computer Science, Artificial Intelligence

Soft margins for AdaBoost

G Rätsch et al.

MACHINE LEARNING (2001)

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Article Multidisciplinary Sciences

Nonlinear dimensionality reduction by locally linear embedding

ST Roweis et al.

SCIENCE (2000)

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Article Multidisciplinary Sciences

A global geometric framework for nonlinear dimensionality reduction

JB Tenenbaum et al.

SCIENCE (2000)

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Article Computer Science, Artificial Intelligence

An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization

TG Dietterich

MACHINE LEARNING (2000)

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