1 code implementation • 13 Jan 2025 • Bangchen Yin, Jiaao Wang, Weitao Du, Pengbo Wang, Penghua Ying, Haojun Jia, Zisheng Zhang, Yuanqi Du, Carla P. Gomes, Chenru Duan, Hai Xiao, Graeme Henkelman
We present AlphaNet, a local frame-based equivariant model designed to achieve both accurate and efficient simulations for atomistic systems.
no code implementations • 21 Oct 2024 • Jieyu Lu, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia, Chenru Duan
We integrate large language models (LLMs) into the evolutionary optimization framework (LLM-EO) and apply it in both single- and multi-objective optimization for TMCs.
1 code implementation • 10 Oct 2024 • Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large space of trajectories.
1 code implementation • 23 Jun 2024 • Haorui Wang, Marta Skreta, Cher-Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable.
1 code implementation • 7 May 2024 • Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du
In this paper, we propose a new framework, ChemFlow, to traverse chemical space through navigating the latent space learned by molecule generative models through flows.
no code implementations • 20 Apr 2024 • Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik
The RMSD and barrier height error is further improved by roughly 25\% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB.
1 code implementation • NeurIPS 2023 • Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, ZhiMing Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.
1 code implementation • 12 Apr 2023 • Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik
Provided reactant and product, this model generates a TS structure in seconds instead of hours required when performing quantum chemistry-based optimizations.
no code implementations • 25 Oct 2022 • Aditya Nandy, Shuwen Yue, Changhwan Oh, Chenru Duan, Gianmarco G. Terrones, Yongchul G. Chung, Heather J. Kulik
We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50, 000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases.
no code implementations • 18 Sep 2022 • Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis.
no code implementations • 10 Aug 2022 • Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik
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 optimal target absorption energies in the visible region as well as well-defined ground states.
no code implementations • 21 Jul 2022 • Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik
With electron density fitting and transfer learning, we build a DFA recommender that selects the DFA with the lowest expected error with respect to gold standard but cost-prohibitive coupled cluster theory in a system-specific manner.
no code implementations • 6 May 2022 • Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design.
no code implementations • 5 May 2022 • Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik
We compute MR diagnostics for over 5, 000 ligands present in previously synthesized transition metal complexes in the Cambridge Structural Database (CSD).
no code implementations • 2 Mar 2022 • Chenru Duan, Aditya Nandy, Husain Adamji, Yuriy Roman-Leshkov, Heather J. Kulik
Combined with model uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.
no code implementations • 11 Jan 2022 • Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik
Differences in MR character are more important than the total degree of MR character in predicting MR effect in property prediction.
no code implementations • 2 Nov 2021 • Aditya Nandy, Chenru Duan, Heather J. Kulik
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships.
no code implementations • 24 Jun 2021 • Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik
Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery.
no code implementations • 24 Jun 2021 • Aditya Nandy, Chenru Duan, Heather J. Kulik
Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice.
no code implementations • 20 Jun 2021 • Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik
To address the common challenge of discovery in a new space where data is limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row.