Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The first ML model (classification model), accurately classifies samples with a conductivity >~25 to 100 S/cm, achieving a maximum of 100% accuracy rate. For the subset of highly conductive samples, we employed a second ML model (regression model), to predict their conductivities, yielding an impressive test R2 value of 0.984. To validate the approach, we showed that the models, neither trained on the samples with the two highest conductivities of 498 and 506 S/cm, were able to, in an extrapolative manner, correctly classify and predict them at satisfactory levels of errors. The proposed ML workflow results in an improvement in the efficiency of the conductivity measurements by 89% of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.Comment: 33 Pages, 17 figure

Automated Electrokinetic Stretcher for Manipulating Nanomaterials

In this work, we present an automated platform for trapping and stretching individual micro- and nanoscale objects in solution using electrokinetic forces. The platform can trap objects at the stagnation point of a planar elongational electrokinetic field for long time scales, as demonstrated by the trapping of ~100 nanometer polystyrene beads and DNA molecules for minutes, with a standard deviation in displacement from the trap center < 1 micrometer. This capability enables the stretching of deformable nanoscale objects in a high-throughput fashion, as illustrated by the stretching of more than 400 DNA molecules within ~4 hours. The flexibility of the electrokinetic stretcher opens up numerous possibilities for contact-free manipulation, with size-based sorting of DNA molecules performed as an example. The platform described provides an automated, high-throughput method to track and manipulate objects for real-time studies of micro- and nanoscale systems.Comment: 9 pages, 7 figure

Constructing Custom Thermodynamics Using Deep Learning

One of the most exciting applications of AI is automated scientific discovery based on previously amassed data, coupled with restrictions provided by the known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Of particular importance are complex dynamic systems where their time evolution is strongly influenced by varying external parameters. In this paper we develop a platform based on a generalised Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. We focus on systems whose complexity and sheer sizes render complete microscopic description impractical, and constructing theoretical macroscopic models requires extensive domain knowledge or trial-and-error. Our machine learning approach addresses this by simultaneously constructing reduced thermodynamic coordinates and interpreting the dynamics on these coordinates. We demonstrate our method by studying theoretically and validating experimentally, the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including (1) the identification of stable and transition states and (2) the control of the stretching rate. We further demonstrate the universality of our approach by applying it to an unrelated problem in a different domain: constructing macroscopic dynamics for spatial epidemics, showing that our method addresses wide scientific and technological applications

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.Comment: 22 pages, 3 figure