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M

Mohammed Al-Fahdi

university of south carolina

H指数:12

被引数:447

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Unlocking Lithium Superionic Conduction via Phonon Softness Descriptors: A High-Throughput Machine Learning Paradigm

通过声子软化描述符解锁锂超离子传导：一种高通量机器学习范式

Chemistry of Materials

IF7

2026-02-100

PRE

Crystal Structures; Optical Behavior; and Magnetic Properties in Hydrated Lanthanide Iron Sulfates

Inorganic Chemistry

IF4.7

2026-01-220

OAAI

Accelerated discovery of extreme lattice thermal conductivity by crystal graph attention networks and chemical bonding

通过晶体图注意力网络和化学键加速发现极端晶格热导率

npj Computational Materials

IF11.9

2025-12-100

OAAI

Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors

机器学习辅助发现钠超离子导体的晶格动力学特征

IF10.7

2025-09-180

OA

Syntheses, crystal structures, and electronic structures of new barium fluoromolybdates

新钡氟钼酸盐的合成、晶体结构和电子结构

Journal of Solid State Chemistry

IF3.5

2025-06-260

PRE

Syntheses and crystal structures of four new d0 transition metal tellurites

JOURNAL OF SOLID STATE CHEMISTRY

IF3.5

2025-03-010

PRE

Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling(vol 50, 101632, 2025)

基于晶体注意力图神经网络的声子态密度快速预测及用于宽带隙电子制冷的候选衬底高通量筛选（卷50，101632，2025）

MATERIALS TODAY PHYSICS

IF9.7

2025-02-010

PRE

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors

APPLIED PHYSICS REVIEWS

IF11.6

2024-04-016

OA

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

JOURNAL OF MATERIALS CHEMISTRY A

IF9.5

2024-01-015

OA

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage (vol 12, pg 8502, 2024)

JOURNAL OF MATERIALS CHEMISTRY A

IF9.5

2024-01-010

OA

Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach

COMMUNICATIONS MATERIALS

IF9.6

2023-08-1512

OA

Physics guided deep learning for generative design of crystal materials with symmetry constraints (vol 9, 38, 2023)

NPJ COMPUTATIONAL MATERIALS

IF11.9

2023-06-170

OA

Physics guided deep learning for generative design of crystal materials with symmetry constraints

NPJ COMPUTATIONAL MATERIALS

IF11.9

2023-03-1836

OA

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

NPJ COMPUTATIONAL MATERIALS

IF11.9

2023-02-0616

OA

Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

NPJ COMPUTATIONAL MATERIALS

IF11.9

2022-07-048

OA

Experimental and computational characterization of glass microsphere-cementitious composites

CEMENT AND CONCRETE RESEARCH

IF13.1

2022-02-0113

OA

Phonon transport anomaly in metavalent bonded materials: contradictory to the conventional theory

JOURNAL OF MATERIALS SCIENCE

IF3.9

2021-09-2919

PRE

High-Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

ADVANCED SCIENCE

IF14.1

2021-08-0569

OAAI

High-throughput computation of novel ternary B-C-N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning

JOURNAL OF MATERIALS CHEMISTRY A

IF9.5

2021-01-0127

PRE