Zobrazeno 1 - 10
of 15 088
pro vyhledávání: '"Head, A. P."'
This work constructs an advanced force field, the Completely Multipolar Model (CMM), to quantitatively reproduce each term of an energy decomposition analysis (EDA) for aqueous solvated alkali metal cations and halide anions and their ion pairings. W
Externí odkaz:
http://arxiv.org/abs/2410.08286
Autor:
Head, D. A.
Graph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph neural networks were trained and optimised for two large-s
Externí odkaz:
http://arxiv.org/abs/2411.14159
We have developed a new time propagation method, time-dependent adaptive sampling configuration interaction (TD-ASCI), to describe the dynamics of a strongly correlated system. We employ the short iterative Lanczos (SIL) method as the time-integrator
Externí odkaz:
http://arxiv.org/abs/2411.07615
A wide variety of reactions are reported to be dramatically accelerated in aqueous microdroplets, making them a promising platform for environmentally clean chemical synthesis. However to fully utilize the microdroplets for accelerating chemical reac
Externí odkaz:
http://arxiv.org/abs/2411.01587
Autor:
Wang, Yingze, Sun, Kunyang, Li, Jie, Guan, Xingyi, Zhang, Oufan, Bagni, Dorian, Head-Gordon, Teresa
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data, and often relies on the PDBBind dataset for training and testing their parameters. In this work we show
Externí odkaz:
http://arxiv.org/abs/2411.01223
Autor:
Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung, Salehi, Mohammadreza, Muennighoff, Niklas, Lo, Kyle, Soldaini, Luca, Lu, Jiasen, Anderson, Taira, Bransom, Erin, Ehsani, Kiana, Ngo, Huong, Chen, YenSung, Patel, Ajay, Yatskar, Mark, Callison-Burch, Chris, Head, Andrew, Hendrix, Rose, Bastani, Favyen, VanderBilt, Eli, Lambert, Nathan, Chou, Yvonne, Chheda, Arnavi, Sparks, Jenna, Skjonsberg, Sam, Schmitz, Michael, Sarnat, Aaron, Bischoff, Byron, Walsh, Pete, Newell, Chris, Wolters, Piper, Gupta, Tanmay, Zeng, Kuo-Hao, Borchardt, Jon, Groeneveld, Dirk, Nam, Crystal, Lebrecht, Sophie, Wittlif, Caitlin, Schoenick, Carissa, Michel, Oscar, Krishna, Ranjay, Weihs, Luca, Smith, Noah A., Hajishirzi, Hannaneh, Girshick, Ross, Farhadi, Ali, Kembhavi, Aniruddha
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a r
Externí odkaz:
http://arxiv.org/abs/2409.17146
As AI-generated summaries proliferate, how can we help people understand the veracity of those summaries? In this short paper, we design a simple interaction primitive, traceable text, to support critical examination of generated summaries and the so
Externí odkaz:
http://arxiv.org/abs/2409.13099
In 1999 Wright and Dyson highlighted the fact that large sections of the proteome of all organisms are comprised of protein sequences that lack globular folded structures under physiological conditions. Since then the biophysics community has made si
Externí odkaz:
http://arxiv.org/abs/2409.02240
Autor:
Cavanagh, Joseph M., Sun, Kunyang, Gritsevskiy, Andrew, Bagni, Dorian, Bannister, Thomas D., Head-Gordon, Teresa
Here we show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) which performs at or above the level of CLMs trained solely on chemical SMILES string data. Using supervised fine-tuning (SFT) and dire
Externí odkaz:
http://arxiv.org/abs/2409.02231
Autor:
Kim, Lukas, Head-Gordon, Teresa
Identification of the breaking point for the chemical bond is essential for our understanding of chemical reactivity. The current consensus is that a point of maximal electron delocalization along the bonding axis separates the different bonding regi
Externí odkaz:
http://arxiv.org/abs/2408.14643