A smart charging system for charging a plug-in electric vehicle (PEV) includes an electric vehicle supply equipment (EVSE) configured to supply electrical power to the PEV through a smart charging module coupled to the EVSE. The smart charging module comprises an electronic circuitry which includes a processor. The electronic circuitry includes electronic components structured to receive electrical power from the EVSE, and supply the electrical power to the PEV. The electronic circuitry is configured to measure a charging parameter of the PEV. The electronic circuitry is further structured to emulate a pulse width modulated signal generated by the EVSE. The smart charging module can also include a first coupler structured to be removably couple to the EVSE and a second coupler structured to be removably coupled to the PEV.

A flexible user scripting interface for the definitions of robot-performed experimental procedures

Opportunity & Solution

Autonomous experiments require orchestrating robotic movements and functions of hardware modules to perform a sequence of experimental steps designed and specified by a user who is familiar with the type of experiments (peptide synthesis, nanostructure characterization, etc.), but not necessarily accustomed to robotics hardware or computer programming. Our workflow scripting interface utilizes the Python programming language to simplify the scripting of hardware control sequences, and organizes them into logical entries that represent experimental steps. The entire experimental procedure is encompassed within a single script, allowing easy archival. The design enables sophisticated backend processing of the workflow script to enable seamless integration with machine learning/optimization frameworks and the robotics system control. Since the experimental procedure is presented in a form that resembles the original hardware control sequences, a wide range of experimental workflows and hardware configurations can be programmed with ease due to the readability and flexibility of the Python scripting language.

Benefits

• flexible scripting (no hidden codes)
• experimental steps embedded clearly in control sequences
• minimal programming skills required

A ML-based scheduler for concurrent sample processing in autonomous experiments

Opportunity & Solution

A scheduler is as a software component that orchestrates the execution of steps (e.g., robot arm movement, solution stirring, temperature annealing, etc.) for performing an autonomous experiment. It is challenging to build a scheduler that enables seamless integration of non-vendor specific hardware and can efficiently optimize the execution order of steps in an experiment involving multiple hardware modules and concurrent processing of samples. Our software comprises a new scheduler using the widely used Python programming language. Since it is built using a general language that is increasingly common in the robotics and scientific fields, one can easily couple it with script-based hardware APIs and utilize machine learning to provide automatic data-driven tuning and hardware-aware scheduling of experimental steps. The ease of integration with existing software codes enables faster progress to be made across laboratories and reduces the cost of autonomous systems thus making them available to less well-funded laboratories.

Benefits

• Enables high-throughput material processing and synthesis
• Leverages machine learning for closed-loop experiments
• Fully autonomous

Data structure and data workflow for handling material samples in high-throughput experiments

Opportunity & Solution

High-throughput experiments enabled by robotics generate large volumes of heterogeneous data, coming from multiple hardware modules in different data formats. There needs to be an easy way to standardize data and store them in a universal format such that data and metadata of individual material samples are well organized. This software consists of a material sample data class/object that utilizes JSON file in the backend for data storage. The sample data class is implemented using the Python programming language. It couples the flexibility of JSON files with rigid schema to make data files compatible with database handling. There are also functions for smart creation of unique sample ID based on workflow and input parameters, as well as handling the underlying data writing and conversion. Instead of the traditional approach of specifying file paths within an experimental workflow, we developed an object/data-oriented approach that initiates actions from the sample data class, providing an elegant way of handling the file path related issues in concurrent/parallel processing of material samples.

Benefits

• Designed to handle autonomous workflow
• User-friendly content addition and editing
• Data hierarchy designed for material samples

Inventions, Applications, Industries:

• Autonomous material discovery
• Combinatorial experiments
• High-dimensional processing-property relationships

Lab automation:

• Facilitates integration solutions for new instruments
• Avoids resource deadlocks
• Dynamic workflow adjustments

Methods and systems are provided for synthesis and deposition of chalcogenides (including Cu₂ZnSnS₄). Binary compounds, such as metal sulfides, can be deposited by alternating exposures of the substrate to a metal cation precursor and a chalcogen anion precursor with purge steps between.

The invention provides a method for producing a molecule capable of undergoing reduction-oxidation when subjected to a voltage potential, the method comprising phosphorylating hydroquinone to create a first intermediate; rearranging the first intermediate to an aryl-bis-(phosphonate) thereby creating a second intermediate comprising phosphorous alkoxy groups; alkylating (e.g., methylating) the second intermediate; converting the alkoxy groups to halides; and substituting the halides to alkyl or aryl groups. Also provided is a system for preventing overcharge in a Lithium-ion battery, the method comprising a mixture of a redox shuttle with electrolyte in the battery such that the shuttle comprises between about 10 and about 20 weight percent of the mixture.

Please contact us for additional information.

Please contact us for additional information.

Invention:

Multi-fidelity scale bridging between various flavors of molecular dynamics (i.e. ab-initio, classical and coarse-grained models) has remained a long-standing challenge. BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) is a framework that leverages machine learning principles to address this challenge.

Opportunity and Solution 

BLAST provides users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (i.e., force fields) for molecular simulations. BLAST is designed to address several long-standing problems in the molecular simulation community, such as unintended misuse of existing force fields due to a knowledge gap between developers and users, bottlenecks in traditional force field development approaches, and other issues relating to the accuracy, efficiency, and transferability of force fields. The BLAST architecture consists of a web user-friendly interface, front-end and back-end web services, and machine learning algorithms that run on high-performance computing (HPC) clusters.