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公开(公告)号：US20240257921A1
公开(公告)日：2024-08-01
申请号：US18635717
申请日：2024-04-15
申请人： SRI International
摘要： Retrosynthetic methods are described for determining one or more optimal synthetic routes to generate a target compound.
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公开(公告)号：US20240249800A1
公开(公告)日：2024-07-25
申请号：US18179621
申请日：2023-03-07
发明人： Leon Immanuel KLEIN , Yue Kwang FOONG , Tor Erlend FJELDE , Bruno Kacper MLODOZENIEC , Marc Manuel Johannes BROCKSCHMIDT , Reinhard Sebastian Bernhard NOWOZIN , Frank NOE , Ryota TOMIOKA
摘要： A computerized method for forecasting a future conformation of a molecular system based on a current conformation of the molecular system comprises (a) receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed; (b) mapping the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain; and (c) returning the proposed conformation as the future conformation.
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公开(公告)号：US20240177809A1
公开(公告)日：2024-05-30
申请号：US18087779
申请日：2022-12-22
发明人： Román ORÚS , Saeed JAHROMI
摘要： A computer-implemented method includes processing a predetermined machine learning routine of a tensor network that defines layers of tensors in the routine, which is adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor. A sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network. Thus, implementing anticommutation fermionic operator; inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction; and outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem. At least one parameter is inferred by the processed predetermined machine learning routine.
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4.发明公开公开(公告)号：US20240153595A1
公开(公告)日：2024-05-09
申请号：US18502852
申请日：2023-11-06
摘要： The embodiments herein provide a system and method for performing accelerated molecular dynamics computer simulations with uncertainty-aware neural networks. The embodiments herein utilize a computational method to simulate the dynamics of atoms in a multi-element system using accelerated molecular dynamics using neural networks (NN) without compromising the accuracy. The formulated method involves simulating the system using ab initio molecular dynamics (AIMD) for a certain number of steps, which are utilized, to train the NN. Further, the trained NN can infer the further steps of the simulation. Here, the uncertainty of the prediction is closely monitored by incorporating uncertainty quantification into NN models. Uncertainty over the threshold indicates the need for more training and hence the usage of AIMD for a few more steps. Therefore, the embodiments herein help in delivering an accurate simulation results at an accelerated speed.
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公开(公告)号：US20240120035A1
公开(公告)日：2024-04-11
申请号：US18221394
申请日：2023-07-13
申请人： Fujitsu Limited
发明人： Eiji OHTA
摘要： A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes in repeat calculation of electron density of a substance by a self-consistent field method that uses a specific number of wave functions according to cutoff energy, executing a second electron density calculation at an (N+1)-th time (N is an integer greater than or equal to 1) by applying second cutoff energy of a value smaller than first cutoff energy applied to an first electron density calculation at an N-th time, determining whether the electron density obtained by the second electron density calculation at the (N+1)-th time satisfies a predetermined condition, and outputting the value of the second cutoff energy in a case where the condition is not satisfied.
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公开(公告)号：US20240038325A1
公开(公告)日：2024-02-01
申请号：US18122251
申请日：2023-03-16
申请人： ZHEJIANG LAB
发明人： Shang YU , Zhipeng ZHONG , Liang XU , Jianshun TANG , Yitao WANG
摘要： The application discloses a reverse virtual screening platform and method based on programmable quantum computing, the method includes the following steps: S1, for a given micromolecule and a target protein molecule, calculating a binding interaction graph of the given micromolecule and the target protein molecule on a computer according to different distances between pharmacophores; S2, encoding, according to an adjacency matrix of the binding interaction graph, the binding interaction graph into a quantum reverse virtual screening platform by decomposing the adjacency matrix; and S3, performing Gaussian boson sampling by the quantum reverse virtual screening platform. The reverse virtual screening platform and method based on programmable quantum computing provided by the present application are implemented by an optical quantum computer system based on a time domain.
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公开(公告)号：US20230392184A1
公开(公告)日：2023-12-07
申请号：US18446048
申请日：2023-08-08
申请人： PHAST CORP.
发明人： Kwangmin SON , Roman STOCKER
CPC分类号： C12Q1/04 , G02B21/0084 , G02B21/365 , C40B30/06 , C40B40/02 , G01N33/9446 , G16C10/00
摘要： Among other things, motility of at least one individual microorganism or a change in motility of at least one individual microorganism or both is or are characterized. The characterized motility or change in motility is used to detect the presence or count of the at least one individual microorganism, or determine the identity of a species or strain of the at least one individual microorganism, or determine a susceptibility of the at least one individual microorganism to one or more antibiotics or other antimicrobials.
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公开(公告)号：US20230093063A1
公开(公告)日：2023-03-23
申请号：US17909276
申请日：2021-03-03
申请人： The Regents of the University of Colorado, a body corporate , University of Utah Research Foundation
发明人： Noel A. Clark , Xi Chen , Matthew A. Glaser , Joseph E. Maclennan , Dengpan Dong , Dimitry Bedrov
摘要： Devices including nematic liquid crystal-forming molecules are disclosed. The molecules include one or more dipoles and exist in a ferroelectric nematic state. Exemplary devices can further include an electrode for applying an electric field in, for example, and in-plane direction.
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公开(公告)号：US20230073816A1
公开(公告)日：2023-03-09
申请号：US18047424
申请日：2022-10-18
发明人： Hangzhou Wang , Yixin Liu , Ye Ji , Wei Duan , Lu Wang
摘要： An optimization method and system for a whole process of molecular-level oil refinery processing and a storage medium are described. According to an embodiment, for mixed products obtained by prediction from simulation of a molecular-level crude oil processing process, when physical properties of any mixed product do not meet any preset standard, or when a target parameter of the mixed products does not meet a preset condition, the proportion of different fractions entering respective petroleum processing device, an operating parameter in a product prediction model, and a mixing rule for mixing predicted products are adjusted, and the mixed products are re-obtained, until the product properties meet any preset standard and the target parameter meets the preset condition. Final predicted products are predicted by adjusting the proportion of fractions for secondary processing, and the production efficiency is improved by means of the simulation optimization of a production process.
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10.发明申请公开(公告)号：US20220406416A1
公开(公告)日：2022-12-22
申请号：US17843341
申请日：2022-06-17
摘要： A method for selecting a material having a desired molecular property comprises generating a combinatorial library of molecule structures derived from a core molecular structure, splitting the library into a training set configured to train a graph neural network (GNN) machine learning (ML) model, a test set configured to test the validity of and assess accuracy of the GNN model, and a prediction set where predictions are made using the GNN model, optimizing geometries of the molecular structures, computing excited state energies of the optimized geometries, encoding molecular structure information into a matrix, determining three mutually orthogonal principal axes, transforming spatial coordinates into mutually orthogonal coordinates, constructing a molecular graph with n nodes, feeding the molecular graph into the GNN model as an input, and selecting a material having a suitable desired molecular property based on the output of the GNN model.
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