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Machine learning for molecular and materials science

Nature (London), 2018-07, Vol.559 (7715), p.547-555

2018

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Ferroelectric materials for solar energy conversion: photoferroics revisited

Energy & environmental science, 2015-01, Vol.8 (3), p.838-848

2015

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Interplay of Orbital and Relativistic Effects in Bismuth Oxyhalides: BiOF, BiOCl, BiOBr, and BiOI

Chemistry of materials, 2016-04, Vol.28 (7), p.1980-1984

2016

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Atomistic Origins of High-Performance in Hybrid Halide Perovskite Solar Cells

Nano letters, 2014-05, Vol.14 (5), p.2584-2590

2014

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How Strong Is the Hydrogen Bond in Hybrid Perovskites?

The journal of physical chemistry letters, 2017-12, Vol.8 (24), p.6154-6159

2017

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Band Engineering of Carbon Nitride Monolayers by N‑Type, P‑Type, and Isoelectronic Doping for Photocatalytic Applications

ACS applied materials & interfaces, 2018-04, Vol.10 (13), p.11143-11151

2018

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Computational materials design of crystalline solids

Chemical Society reviews, 2016-11, Vol.45 (22), p.6138-6146

2016

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Modeling the dielectric constants of crystals using machine learning

The Journal of chemical physics, 2020-07, Vol.153 (2), p.024503-024503

2020

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Relativistic electronic structure and band alignment of BiSI and BiSeI: candidate photovoltaic materials

Journal of materials chemistry. A, Materials for energy and sustainability, 2016-01, Vol.4 (6), p.2060-2068

2016

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Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals

Chemistry of materials, 2022-01, Vol.34 (2), p.562-573

2022

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Tuning the Negative Thermal Expansion Behavior of the Metal–Organic Framework Cu3BTC2 by Retrofitting

Journal of the American Chemical Society, 2019-07, Vol.141 (26), p.10504-10509

2019

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Electronic Structure Modulation of Metal–Organic Frameworks for Hybrid Devices

ACS applied materials & interfaces, 2014-12, Vol.6 (24), p.22044-22050

2014

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Polymorph Engineering of TiO2: Demonstrating How Absolute Reference Potentials Are Determined by Local Coordination

Chemistry of materials, 2015-06

2015

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Computational Screening of All Stoichiometric Inorganic Materials

Chem, 2016-10, Vol.1 (4), p.617-627

2016

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Metal-free perovskites for non linear optical materials

Chemical science (Cambridge), 2019, Vol.1 (35), p.8187-8194

2019

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

Chemical science (Cambridge), 2018, Vol.9 (4), p.1022-1030

2018

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Designing porous electronic thin-film devices: band offsets and heteroepitaxy

Faraday discussions, 2017, Vol.21, p.27-219

2017

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Role of entropic effects in controlling the polymorphism in formate ABX3 metal-organic frameworks

Chemical communications (Cambridge, England), 2015-11, Vol.51 (85), p.15538-15541

2015

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Quasi-particle electronic band structure and alignment of the V-VI-VII semiconductors SbSI, SbSBr, and SbSeI for solar cells

Applied physics letters, 2016-03, Vol.108 (11)

2016

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Morphological control of band offsets for transparent bipolar heterojunctions: The Bädeker diode

Physica status solidi. A, Applications and materials science, 2015-07, Vol.212 (7), p.1461-1465

Butler, Keith T.

2015

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Screening procedure for structurally and electronically matched contact layers for high-performance solar cells: hybrid perovskites

Journal of materials chemistry. C, Materials for optical and electronic devices, 2016-01, Vol.4 (6), p.1149-1158

2016

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Crystal electron binding energy and surface work function control of tin dioxide

Physical review. B, Condensed matter and materials physics, 2014-03, Vol.89 (11), Article 115320

2014

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Role of Amine–Cavity Interactions in Determining the Structure and Mechanical Properties of the Ferroelectric Hybrid Perovskite [NH3NH2]Zn(HCOO)3

Chemistry of materials, 2016-01, Vol.28 (1), p.312-317

2016

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Distributed representations of atoms and materials for machine learning

npj computational materials, 2022-03, Vol.8 (1), p.1-9, Article 44

2022

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Predicting thermoelectric transport properties from composition with attention-based deep learning

Machine learning: science and technology, 2023-03, Vol.4 (1), p.15037

2023

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Machine learning for molecular and materials science

Ferroelectric materials for solar energy conversion: photoferroics revisited

Interplay of Orbital and Relativistic Effects in Bismuth Oxyhalides: BiOF, BiOCl, BiOBr, and BiOI

Atomistic Origins of High-Performance in Hybrid Halide Perovskite Solar Cells

How Strong Is the Hydrogen Bond in Hybrid Perovskites?

Band Engineering of Carbon Nitride Monolayers by N‑Type, P‑Type, and Isoelectronic Doping for Photocatalytic Applications

Computational materials design of crystalline solids

Modeling the dielectric constants of crystals using machine learning

Relativistic electronic structure and band alignment of BiSI and BiSeI: candidate photovoltaic materials

Breaking the Aristotype: Featurization of Polyhedral Distortions in Perovskite Crystals

Tuning the Negative Thermal Expansion Behavior of the Metal–Organic Framework Cu3BTC2 by Retrofitting

Electronic Structure Modulation of Metal–Organic Frameworks for Hybrid Devices

Polymorph Engineering of TiO2: Demonstrating How Absolute Reference Potentials Are Determined by Local Coordination

Computational Screening of All Stoichiometric Inorganic Materials

Metal-free perovskites for non linear optical materials

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

Designing porous electronic thin-film devices: band offsets and heteroepitaxy

Role of entropic effects in controlling the polymorphism in formate ABX3 metal-organic frameworks

Quasi-particle electronic band structure and alignment of the V-VI-VII semiconductors SbSI, SbSBr, and SbSeI for solar cells

Morphological control of band offsets for transparent bipolar heterojunctions: The Bädeker diode

Screening procedure for structurally and electronically matched contact layers for high-performance solar cells: hybrid perovskites

Crystal electron binding energy and surface work function control of tin dioxide

Role of Amine–Cavity Interactions in Determining the Structure and Mechanical Properties of the Ferroelectric Hybrid Perovskite [NH3NH2]Zn(HCOO)3

Distributed representations of atoms and materials for machine learning

Predicting thermoelectric transport properties from composition with attention-based deep learning

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