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和北卡罗来纳大学教堂山分校的Olexandr Isayev得到了化学理论，模型和计算方法计划的奖项。经验潜能---也称为力场---在模拟分子之间的原子级相互作用中起着至关重要的作用。 它们用于材料和药品的计算设计中。 但是，当前的电位已被设计为快速或准确，但很少两者兼而有之。 这为下一阶段的预测化学计算机模型提供了关键的瓶颈。 在这个项目中，罗伊特伯格和伊莎耶夫教授利用最先进的人工智能（AI）从分子能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，该场既准确又快速，同时也适用于化学中的广泛系统。 这项研究有可能使材料设计，可再生能源研究和药物设计受益。 该项目是迈向使用人工智能技术来创建新材料和分子超出人类想象力的第一步。研究小组通过有关分子模拟的研讨会，公众的“与我谈论科学”的科学以及北卡罗来纳州科学与数学学院（NCSSSM）的高中生参与。该项目的目的是开发化学精确，可扩展和通用的神经网络电位ANI，以用于“在硅”有机化学实验中。从构象搜索到化学反应和配体结合，ANI可能应用的范围非常广泛。通过对新的化学空间区域的智能采样，研究人员正在扩大用例，以包括包含H，C，N O，F，S，P，CL和BR原子的任意系统。新的设计策略基于Anakin-Me方法，用于实施早期的ANI-1潜力。要训​​练ANI-1，通过详尽的，构象和化学空间的随机抽样计算了来自60,000个不同有机分子的2200万个结构构象的WB97X/6-31G* DFT能量的数据库。通过对有机分子，生物分子和肽的严格基准测试，与DFT参考值相比，ANI-1预测了1 kcal/mol的RMS误差的总和相对能。 与高质量的CCSD（T）/CBS量子量子化学能相比，对Anakin-Me的增强旨在提高计算效率，扩大可以模拟的系统范围，并达到1 kcal/mol RMS的误差。这些增强功能包括减少给定的原子类型的所需数据集大小，以便纳入其他元素和化学元素；扩展训练数据集以包括原子电荷和力的数据，除了能量以及带电分子的数据；并实施“逐一委员会”的主动学习方法，以促进对ANI的添加，替代和消除化学反应的学习。 ANI电位通过用户友好的开放访问机制传播。 ANI电位的实现利用了图形处理单元（GPU）加速度在启用GPU的工作站和并行超级计算机上运行。 ANI软件库具有一个简单的Python API，并与流行的分子建模和仿真软件包（例如Amber，OpenMM和Avogadro）集成在一起。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

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.

Transforming Computational Drug Discovery with Machine Learning and AI

• DOI: 10.1021/acsmedchemlett.8b00437
• 发表时间: 2018-11-01
• 期刊: ACS MEDICINAL CHEMISTRY LETTERS
• 影响因子: 4.2
• 作者: Smith, Justin S.;Roitberg, Adrian E.;Isayev, Olexandr
• 通讯作者: Isayev, Olexandr

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.

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