D3SC: CDS&E: Collaborative Research: Development and application of accurate, transferable and extensible deep neural network potentials for molecules and reactions

D3SC：CDS

• 批准号: 1802789
• 负责人: Olexandr Isayev
• 金额: $ 35.08万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802789&HistoricalAwards=false
• 关键词: D3SC; CDS& Collaborative; Research; Development

Adrian Roitberg of the University of Florida and Olexandr Isayev of the University of North Carolina at Chapel Hill are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. Empirical potentials---also known as force fields---play an essential role in simulating atomic-scale interactions between molecules. They are used in the computational design of materials and pharmaceuticals. However, current potentials have been designed to be fast or accurate, but rarely both. This presents a critical bottleneck for the next phase of predictive chemical computer models. In this project, Professors Roitberg and Isayev are leveraging state-of-the-art artificial intelligence (AI) to "learn" potentials from ultra-large datasets of molecular energies and chemical reactions. The project is creating a new force field, ANI, that is accurate and fast, while also applicable to a broad range of systems in chemistry. This research has the potential to benefit materials design, renewable energy research, and drug design. The project is first step toward the use of artificial intelligence techniques to create new materials and molecules beyond what the human imagination can do alone. The research team is engaged in outreach through workshops on molecular simulations, "Talk science to me" science for the general public, and the involvement of high school students from the North Carolina School of Science and Math (NCSSM) in the research. The objective of this project is to develop a chemically-accurate, extensible, and universal neural network potential, ANI, for use in "in silico" organic chemistry experimentation. The range of possible applications for ANI is very broad, from conformational searches to chemical reactions and ligand binding. Through intelligent sampling of new regions of chemical space, the researchers are expanding use cases for ANI to include arbitrary systems containing H, C, N O, F, S, P, Cl and Br atoms. The new design strategy is based on the ANAKIN-ME method, used in implementing the earlier ANI-1 potential. To train ANI-1, a database of wB97x/6-31G* DFT energies for 22 million structural conformations from 60,000 distinct organic molecules was computed through exhaustive, stochastic sampling of conformational and chemical space. Through rigorous benchmarks for organic molecules, biomolecules, and peptides, ANI-1 predicted total and relative energies with RMS errors under 1 kcal/mol, when compared to DFT reference values. The enhancements being made to ANAKIN-ME are aimed at improving computational efficiency, expanding the range of systems that can be simulated, and achieving 1 kcal/mol RMS error in comparison to high quality CCSD(T)/CBS quantum chemical energies. These enhancements include reducing the required dataset size for a given set of atom types, to enable inclusion of additional elements and chemistries; expanding training datasets to include data on atomic charges and forces, in addition to energies, and data for charged molecules; and implementing "query-by-committee" active learning approaches to facilitate learning of addition, substitution, and elimination chemical reactions by ANI. The ANI potential is being disseminated through user-friendly open access mechanisms. The implementation of the ANI potential takes advantage of graphics processing unit (GPU) acceleration to run on GPU-enabled workstations and parallel supercomputers. The ANI software library has a simple Python API and is being integrated with popular molecular modeling and simulation packages such as AMBER, OpenMM and Avogadro.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

佛罗里达大学的Adrian Roitberg和查佩尔山的北卡罗来纳州大学的Olexandr Isayev得到了化学系化学理论、模型和计算方法项目的支持。经验势---也被称为力场---在模拟分子间原子尺度的相互作用中起着重要的作用。 它们用于材料和药物的计算设计。 然而，电流电位被设计为快速或准确，但很少两者兼而有之。 这为预测化学计算机模型的下一阶段提出了一个关键瓶颈。 在这个项目中，Roitberg和Isayev教授利用最先进的人工智能（AI）从分子能量和化学反应的超大数据集中“学习”潜力。该项目正在创建一个新的力场，ANI，这是准确和快速的，同时也适用于广泛的化学系统。 这项研究有可能有利于材料设计，可再生能源研究和药物设计。 该项目是使用人工智能技术创造超越人类想象力的新材料和分子的第一步。该研究小组通过分子模拟研讨会、面向公众的“与我谈科学”科学以及北卡罗来纳州科学与数学学院（NCSSM）的高中生参与研究，参与外展活动。本项目的目标是开发一个化学准确的，可扩展的，通用的神经网络潜力，ANI，用于“在硅”有机化学实验。ANI的可能应用范围非常广泛，从构象搜索到化学反应和配体结合。通过对化学空间新区域的智能采样，研究人员正在扩展ANI的用例，以包括包含H，C，N O，F，S，P，Cl和Br原子的任意系统。新的设计策略是基于ANAKIN-ME方法，用于实现早期的ANI-1潜力。为了训练ANI-1，通过构象和化学空间的穷举随机采样，计算了来自60，000个不同有机分子的2200万个结构构象的wB 97 x/6- 31 G * DFT能量数据库。通过对有机分子、生物分子和肽进行严格的基准测试，ANI-1预测的总能量和相对能量与DFT参考值相比，RMS误差低于1 kcal/mol。 对ANAKIN-ME进行的增强旨在提高计算效率，扩大可以模拟的系统范围，并与高质量CCSD（T）/CBS量子化学能相比，实现1 kcal/mol RMS误差。这些增强包括减少给定原子类型集合所需的数据集大小，以允许包含额外的元素和化学物质;扩展训练数据集，以包括原子电荷和力的数据，以及能量和带电分子的数据;以及实施“按委员会查询”主动学习方法，以促进ANI对添加，取代和消除化学反应的学习。 ANI的潜力正在通过用户友好的开放获取机制进行传播。ANI潜力的实现利用图形处理单元（GPU）加速来在支持GPU的工作站和并行超级计算机上运行。 ANI软件库有一个简单的Python API，并正在与流行的分子建模和模拟软件包，如AMBER，OpenMM和Avogadro集成。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

• DOI: 10.1038/s41597-020-0473-z
• 发表时间: 2020-05-01
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Smith, Justin S.;Zubatyuk, Roman;Tretiak, Sergei
• 通讯作者: Tretiak, Sergei

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

• DOI: 10.1038/s41467-019-10827-4
• 发表时间: 2019-07-01
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Smith, Justin S.;Nebgen, Benjamin T.;Roitberg, Adrian E.
• 通讯作者: Roitberg, Adrian E.

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

• DOI: 10.1126/sciadv.aav6490
• 发表时间: 2018-10
• 期刊: Science Advances
• 影响因子: 13.6
• 作者: R. Zubatyuk;Justin S. Smith;J. Leszczynski;O. Isayev

Predicting Thermal Properties of Crystals Using Machine Learning

• DOI: 10.1002/adts.201900208
• 发表时间: 2019-12-17
• 期刊: ADVANCED THEORY AND SIMULATIONS
• 影响因子: 3.3
• 作者: Tawfik, Sherif Abdulkader;Isayev, Olexandr;Winkler, David A.
• 通讯作者: Winkler, David A.