Zobrazeno 1 - 10
of 167
pro vyhledávání: '"Nandy, Aditya"'
Autor:
Lamtyugina, Alexandra, Behera, Agnish Kumar, Nandy, Aditya, Floyd, Carlos, Vaikuntanathan, Suriyanarayanan
Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset and synthesizing data by sampling from the approximated distribution. In this work, we explore how the generative performance may be be mo
Externí odkaz:
http://arxiv.org/abs/2411.07233
Autor:
Nandy, Aditya, Yue, Shuwen, Oh, Changhwan, Duan, Chenru, Terrones, Gianmarco G., Chung, Yongchul G., Kulik, Heather J.
High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models
Externí odkaz:
http://arxiv.org/abs/2210.14191
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density funct
Externí odkaz:
http://arxiv.org/abs/2209.08595
Autor:
Arunachalam, Naveen, Gugler, Stefan, Taylor, Michael G., Duan, Chenru, Nandy, Aditya, Janet, Jon Paul, Meyer, Ralf, Oldenstaedt, Jonas, Chu, Daniel B. K., Kulik, Heather J.
To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuclear octahedral transition metal complexes deposited in an experimenta
Externí odkaz:
http://arxiv.org/abs/2209.05412
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously have optim
Externí odkaz:
http://arxiv.org/abs/2208.05444
Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density functional appr
Externí odkaz:
http://arxiv.org/abs/2207.10747
Publikováno v:
Journal of Physical Chemistry Letters, 2021, 12, 19, 4628-4637
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of trai
Externí odkaz:
http://arxiv.org/abs/2205.02967
Despite its widespread use, the predictive accuracy of density functional theory (DFT) is hampered by delocalization errors, especially for correlated systems such as transition-metal complexes. Two complementary tuning strategies have been developed
Externí odkaz:
http://arxiv.org/abs/2204.03810
Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with high calculation failure rate and wasted computati
Externí odkaz:
http://arxiv.org/abs/2203.01276
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS is carried out with approx
Externí odkaz:
http://arxiv.org/abs/2201.04243