S. Tian

发表

Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

Brian L. DeCost, N. T. P. Hartono, Janak Thapa, 2018, Joule.

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Qianxiao Li, C. Brabec, Felipe Oviedo, 2019, npj Computational Materials.

Machine Learning and High-Throughput Robust Design of P3HT-CNT Composite Thin Films for High Electrical Conductivity

Qianxiao Li, T. Buonassisi, S. Tian, 2020, 2011.10382.

Inverse design of crystals using generalized invertible crystallographic representation pdf

Yi Liu, Siyu Tian, Qianxiao Li, 2020, ArXiv.

What Information is Necessary and Sufficient to Predict Materials Properties using Machine Learning?

Qianxiao Li, T. Buonassisi, Zekun Ren, 2022, 2206.04968.

Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis

Brian L. DeCost, N. T. P. Hartono, Janak Thapa, 2018, 1812.01025.

Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films

F. G. L. Parlane, Qianxiao Li, Felipe Oviedo, 2022, ArXiv.

Extracting film thickness and optical constants from spectrophotometric data by evolutionary optimization

Zhe Liu, T. Buonassisi, S. Tian, 2022, PloS one.

Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films

F. G. L. Parlane, Qianxiao Li, Felipe Oviedo, 2022, Digital Discovery.

An Invertible Crystallographic Representation for General Inverse Design of Inorganic Crystals with Targeted Properties

Tonio Buonassisi, Qianxiao Li, Qiaohao Liang, 2020, SSRN Electronic Journal.

Inverse design of crystals using generalized invertible crystallographic representation

Qianxiao Li, Juhwan Noh, Yousung Jung, 2020, ArXiv.

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Savitha Ramasamy, Giuseppe Romano, Tonio Buonassisi, 2018, npj Computational Materials.

Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization

Christoph J. Brabec, Ning Li, Ian Marius Peters, 2020, 2004.13599.

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

T. Buonassisi, Zekun Ren, S. Tian, 2023, ACS omega.

Extracting Thin-film Optical Parameters from Spectrophotometric Data by Evolutionary Optimization

Zhe Liu, T. Buonassisi, S. Tian, 2021 .

Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites

T. Buonassisi, Zekun Ren, S. Tian, 2021, Advanced Functional Materials.

Physics-guided characterization and optimization of solar cells using surrogate machine learning model

Christoph J. Brabec, Ian Marius Peters, Tonio Buonassisi, 2019, 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC).

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks pdf

Giuseppe Romano, Tonio Buonassisi, Brian L. DeCost, 2018, 1811.08425.