School of Physical and Mathematical Sciences (SPMS)

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3,801 to 3,810 of 8,234 Results

readme.docx Dec 14, 2022 - Replication Data for: Anti-Hong–Ou–Mandel interference by coherent perfect absorption of entangled photons MS Word - 20.5 KB - MD5: ae549a523d1e2771994d40e675a5ca96 Documentation Instructions
Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska Dec 14, 2022 - TONG Ping Tong, Ping, 2022, "Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska", https://doi.org/10.21979/N9/MQG8HN, DR-NTU (Data), V1 The final P-wave velocity model of Alaska revealed by double-difference adjoint tomography.
Alaska_DD_Vp.txt Dec 14, 2022 - Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska Plain Text - 225.6 MB - MD5: 0060875d4e973940715a4a7af333b982
Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Dec 1, 2022 - XU Qiang Anand, D Vijay; Xu, Qiang; Wee, Junjie; Xia, Kelin; Sum, Tze Chien, 2022, "Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design", https://doi.org/10.21979/N9/CVJWZ9, DR-NTU (Data), V1 Accelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materia...
dfpt.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 343.2 MB - MD5: 0bcf23fc88dbd16fbdc0d5d323992e6a Data raw data of dielectric constant of perovskites
fig.1.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 1.1 MB - MD5: 5326d4c30ecf79a1bfc717450d1eabdc Data Data for replication and high-resolution image for Figure 1.
fig.2.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 10.8 MB - MD5: 527b3b2e7d6b7addd214b58b187ed108 Data Data for replication and high-resolution image for Figure 2.
fig.3.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 145.0 MB - MD5: ac66dbfec0a934904112ca2a8ab2f7a9 Data Data for replication and high-resolution image for Figure 3.
fig.4.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 5.9 MB - MD5: a8a4cf15137ba22549cc29b6d4fd434c Data Data for replication and high-resolution image for Figure 4.
fig.5.tar.gz Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design Gzip Archive - 13.5 MB - MD5: 30e88c05b25b61718131da0218aa774c Data Data for replication and high-resolution image for Figure 5.

readme.docx

Dec 14, 2022 - Replication Data for: Anti-Hong–Ou–Mandel interference by coherent perfect absorption of entangled photons

MS Word - 20.5 KB -

Documentation

Instructions

Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska

Dec 14, 2022 - TONG Ping

Tong, Ping, 2022, "Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska", https://doi.org/10.21979/N9/MQG8HN, DR-NTU (Data), V1

The final P-wave velocity model of Alaska revealed by double-difference adjoint tomography.

Alaska_DD_Vp.txt

Dec 14, 2022 - Replication Data for: Double-difference adjoint tomography of the crust and uppermost mantle beneath Alaska

Plain Text - 225.6 MB -

Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design

Dec 1, 2022 - XU Qiang

Anand, D Vijay; Xu, Qiang; Wee, Junjie; Xia, Kelin; Sum, Tze Chien, 2022, "Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design", https://doi.org/10.21979/N9/CVJWZ9, DR-NTU (Data), V1

Accelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materia...

dfpt.tar.gz

Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design

Gzip Archive - 343.2 MB -

Data

raw data of dielectric constant of perovskites

fig.1.tar.gz

Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design

Gzip Archive - 1.1 MB -

Data

Data for replication and high-resolution image for Figure 1.

fig.2.tar.gz

Dec 1, 2022 - Replication data for: Topological Feature Engineering for Machine Learning Based Halide Perovskite Materials Design