Large-scale semidefinite programs in electronic structure calculation. - Mituhiro Fukuda; Bastiaan J. Braams; Maho Nakata; Michael L. Overton; Jerome K. Percus; Makoto Yamashita; Zhengji Zhao (Math. Program., 2007)

A numerical evaluation of highly accurate multiple-precision arithmetic version of semidefinite programming solver: SDPA-GMP, -QD and -DD. - Maho Nakata (CACSD, 2010)

All about RICC: RIKEN Integrated Cluster of Clusters. - Maho Nakata (ICNC, 2011)

A Fast Implementation of Matrix-matrix Product in Double-double Precision on NVIDIA C2050 and Application to Semidefinite Programming. - Maho Nakata; Yasuyoshi Takao; Shigeho Noda; Ryutaro Himeno (ICNC, 2012)

High-performance general solver for extremely large-scale semidefinite programming problems. - Katsuki Fujisawa; Hitoshi Sato; Satoshi Matsuoka; Toshio Endo; Makoto Yamashita; Maho Nakata (SC, 2012)

Poster: MPACK 0.7.0: Multiple Precision Version of BLAS and LAPACK. - Maho Nakata (SC Companion, 2012)

Strange behaviors of interior-point methods for solving semidefinite programming problems in polynomial optimization. - Hayato Waki; Maho Nakata; Masakazu Muramatsu (Comput. Optim. Appl., 2012)

All about RIKEN Integrated Cluster of Clusters (RICC). - Maho Nakata (Int. J. Netw. Comput., 2012)

Algorithm 925: Parallel Solver for Semidefinite Programming Problem having Sparse Schur Complement Matrix. - Makoto Yamashita; Katsuki Fujisawa; Mituhiro Fukuda; Kazuhide Nakata; Maho Nakata (ACM Trans. Math. Softw., 2012)

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry. - Maho Nakata; Tomomi Shimazaki (J. Chem. Inf. Model., 2017)

Basics and Practice of Linear Algebra Calculation Library BLAS and LAPACK. - Maho Nakata (The Art of High Performance Computing for Computational Science, Vol. 1, 2019)

Techniques Concerning Computation Accuracy. - Shin'ichi Oishi; Yusuke Morikura; Kouta Sekine; Hisayasu Kuroda; Maho Nakata (The Art of High Performance Computing for Computational Science, Vol. 1, 2019)

White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing. - Roman Iakymchuk; Daichi Mukunoki; Artur Podobas; Fabienne Jézéquel; Toshiyuki Imamura; Norihisa Fujita; Jens Huthmann; Shuhei Kudo; Yiyu Tan; Jens Domke; Kai Torben Ohlhus; Takeshi Fukaya; et al. (CoRR, 2020)

PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties. - Maho Nakata; Tomomi Shimazaki; Masatomo Hashimoto; Toshiyuki Maeda (J. Chem. Inf. Model., 2020)

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. - Weihua Hu; Matthias Fey; Hongyu Ren; Maho Nakata; Yuxiao Dong; Jure Leskovec (NeurIPS Datasets and Benchmarks, 2021)

MPLAPACK version 1.0.0 user manual. - Maho Nakata (CoRR, 2021)

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs. - Zhao Xu 005; Youzhi Luo; Xuan Zhang; Xinyi Xu; Yaochen Xie; Meng Liu 015; Kaleb Dickerson; Cheng Deng 002; Maho Nakata; Shuiwang Ji (CoRR, 2021)

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs. - Fumiya Kono; Naohito Nakasato; Maho Nakata (FCCM, 2023)

PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations. - Maho Nakata; Toshiyuki Maeda (CoRR, 2023)

PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations. - Maho Nakata; Toshiyuki Maeda (J. Chem. Inf. Model., 2023)

Evaluation of POSIT Arithmetic with Accelerators. - Naohito Nakasato; Yuki Murakami; Fumiya Kono; Maho Nakata (HPC Asia, 2024)

3D Molecular Geometry Analysis with 2D Graphs. - Zhao Xu 005; Yaochen Xie; Youzhi Luo; Xuan Zhang; Xinyi Xu; Meng Liu 015; Kaleb Dickerson; Cheng Deng 002; Maho Nakata; Shuiwang Ji (SDM, 2024)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials. - Cong Fu 003; Yuchao Lin; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations. - Yuchao Lin; Cong Fu 003; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)