Výsledky vyhledávání - "Mathew D. Halls"

Akademický článek

Advancing material property prediction: using physics-informed machine learning models for viscosity

Autor: Alex K. Chew, Matthew Sender, Zachary Kaplan, Anand Chandrasekaran, Jackson Chief Elk, Andrea R. Browning, H. Shaun Kwak, Mathew D. Halls, Mohammad Atif Faiz Afzal

Publikováno v: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-14 (2024)

Abstract In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructin

Externí odkaz: https://doaj.org/article/d431eabc630d4e9b849181689578969e

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Akademický článek

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin

Autor: Paulo G. M. Mileo, Caroline M. Krauter, Jeffrey M. Sanders, Andrea R. Browning, Mathew D. Halls

Publikováno v: ACS Omega, Vol 8, Iss 45, Pp 42417-42428 (2023)

Externí odkaz: https://doaj.org/article/1545eb3be8524cc38ffb2b405c7cbdb7

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Akademický článek

Development of scalable and generalizable machine learned force field for polymers

Autor: Shaswat Mohanty, James Stevenson, Andrea R. Browning, Leif Jacobson, Karl Leswing, Mathew D. Halls, Mohammad Atif Faiz Afzal

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)

Abstract Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain p

Externí odkaz: https://doaj.org/article/2dbe2c1c2a9b4039905d9d1df98e14c1

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Akademický článek

Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods

Autor: Paul Winget, Mathew D. Halls, Rafael Gómez-Bombarelli, Chihaya Adachi

Publikováno v: Frontiers in Chemistry, Vol 10 (2022)

Externí odkaz: https://doaj.org/article/5d1f6faacd0d48aa9f5a4cb55533df3b

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Akademický článek

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics

Autor: Hadi Abroshan, H. Shaun Kwak, Yuling An, Christopher Brown, Anand Chandrasekaran, Paul Winget, Mathew D. Halls

Publikováno v: Frontiers in Chemistry, Vol 9 (2022)

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidat

Externí odkaz: https://doaj.org/article/d4c36d13675c4d7dadb86e2818ce9771

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Akademický článek

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations

Autor: H. Shaun Kwak, Yuling An, David J. Giesen, Thomas F. Hughes, Christopher T. Brown, Karl Leswing, Hadi Abroshan, Mathew D. Halls

Publikováno v: Frontiers in Chemistry, Vol 9 (2022)

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials

Externí odkaz: https://doaj.org/article/9d052ffe22fc48adb56256115e599767

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Akademický článek

Molecular Design Based on Donor-Weak Donor Scaffold for Blue Thermally-Activated Delayed Fluorescence Designed by Combinatorial DFT Calculations

Autor: Youichi Tsuchiya, Keita Tsuji, Ko Inada, Fatima Bencheikh, Yan Geng, H. Shaun Kwak, Thomas J. L. Mustard, Mathew D. Halls, Hajime Nakanotani, Chihaya Adachi

Publikováno v: Frontiers in Chemistry, Vol 8 (2020)

Quantum chemical calculations are necessary to develop advanced emitter materials showing thermally-activated delayed fluorescence (TADF) for organic light-emitting diodes (OLEDs). However, calculation costs become problematic when more accurate func

Externí odkaz: https://doaj.org/article/3b51b5322d2442619cbbc9ebf8e40d80

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