4.6 Article

Inverse Design of Nanoparticles Using Multi-Target Machine Learning

Related references

Note: Only part of the references are listed.
Article Materials Science, Multidisciplinary

Interfacial informatics

Julia M. Fischer et al.

Summary: Utilizing machine learning methods for analyzing and predicting events at interfaces is more complex than for individual entities, with feature selection being crucial for understanding potential energy surfaces.

JOURNAL OF PHYSICS-MATERIALS (2021)

Article Chemistry, Physical

Machine learning reveals multiple classes of diamond nanoparticles

Amanda J. Parker et al.

NANOSCALE HORIZONS (2020)

Article Chemistry, Physical

Predicting Chemical Reaction Barriers with a Machine Learning Model

Aayush R. Singh et al.

CATALYSIS LETTERS (2019)

Editorial Material Multidisciplinary Sciences

Beware plausible predictions of fantasy materials

Alex Zunger

NATURE (2019)

Article Chemistry, Physical

Does Twinning Impact Structure/Property Relationships in Diamond Nanoparticles?

Amanda S. Barnard et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2019)

Article Chemistry, Physical

Semi-supervised machine-learning classification of materials synthesis procedures

Haoyan Huo et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Review Chemistry, Physical

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Review Materials Science, Multidisciplinary

From DFT to machine learning: recent approaches to materials science-a review

Gabriel R. Schleder et al.

JOURNAL OF PHYSICS-MATERIALS (2019)

Review Chemistry, Multidisciplinary

Nanoinformatics, and the big challenges for the science of small things

A. S. Barnard et al.

NANOSCALE (2019)

Review Chemistry, Medicinal

Deep Generative Models for Molecular Science

Peter B. Jorgensen et al.

MOLECULAR INFORMATICS (2018)

Article Chemistry, Multidisciplinary

The Matter Simulation (R)evolution

Alan Aspuru-Guzik et al.

ACS CENTRAL SCIENCE (2018)

Review Chemistry, Multidisciplinary

Inverse design in search of materials with target functionalities

Alex Zunger

NATURE REVIEWS CHEMISTRY (2018)

Review Multidisciplinary Sciences

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamin Sanchez-Lengeling et al.

SCIENCE (2018)

Article Physics, Multidisciplinary

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning

Paul Z. Hanakata et al.

PHYSICAL REVIEW LETTERS (2018)

Article Operations Research & Management Science

A framework for sensitivity analysis of decision trees

Bogumil Kaminski et al.

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH (2018)

Article Chemistry, Medicinal

Is Multitask Deep Learning Practical for Pharma?

Bharath Ramsundar et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Article Chemistry, Medicinal

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction

Baichuan Sun et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Review Physics, Condensed Matter

Challenges in modelling nanoparticles for drug delivery

Amanda S. Barnard

JOURNAL OF PHYSICS-CONDENSED MATTER (2016)

Article Automation & Control Systems

Be aware of error measures. Further studies on validation of predictive QSAR models

Kunal Roy et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2016)

Article Materials Science, Multidisciplinary

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Anubhav Jain et al.

JOURNAL OF MATERIALS RESEARCH (2016)

Article Materials Science, Multidisciplinary

Theory-guided Machine learning in Materials science

Nicholas Wagner et al.

FRONTIERS IN MATERIALS (2016)

Review Materials Science, Multidisciplinary

What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Edward O. Pyzer-Knapp et al.

ANNUAL REVIEW OF MATERIALS RESEARCH, VOL 45 (2015)

Article Materials Science, Multidisciplinary

Inverse design of materials by multi-objective differential evolution

Yue-Yu Zhang et al.

COMPUTATIONAL MATERIALS SCIENCE (2015)

Article Physics, Multidisciplinary

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli et al.

PHYSICAL REVIEW LETTERS (2015)

Review Computer Science, Artificial Intelligence

A survey on multi-output regression

Hanen Borchani et al.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2015)

Article Chemistry, Physical

Inverse Design of High Absorption Thin-Film Photovoltaic Materials

Liping Yu et al.

ADVANCED ENERGY MATERIALS (2013)

Article Engineering, Electrical & Electronic

Differential Evolution as Applied to Electromagnetics

P. Rocca et al.

IEEE ANTENNAS AND PROPAGATION MAGAZINE (2011)

Article Computer Science, Artificial Intelligence

Multivariate random forests

Mark Segal et al.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2011)

Article Materials Science, Multidisciplinary

Nanodiamond Particles: Properties and Perspectives for Bioapplications

Amanda M. Schrand et al.

CRITICAL REVIEWS IN SOLID STATE AND MATERIALS SCIENCES (2009)

Article Computer Science, Interdisciplinary Applications

Unbiased split selection for classification trees based on the Gini Index

Carohn Strobl et al.

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2007)

Article Computer Science, Artificial Intelligence

A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

MA Razi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2005)

Article Chemistry, Multidisciplinary

Random forest: A classification and regression tool for compound classification and QSAR modeling

V Svetnik et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2003)

Article Computer Science, Artificial Intelligence

The problem of bias in training data in regression problems in medical decision support

B Mac Namee et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Environmental Sciences

Designing a competent simple genetic algorithm for search and optimization

P Reed et al.

WATER RESOURCES RESEARCH (2000)