Self-Driving Materials Laboratories have greatly advanced the automation of material design and discovery. They require the integration of diverse fields and consist of three primary components, which intersect with many AI-related research topics:
- AI-Guided Design. This component intersects heavily with algorithmic research at NeurIPS, including (but not limited to) various topic areas such as: Reinforcement Learning and data-driven modeling of physical phenomena using Neural Networks (e.g. Graph Neural Networks and Machine Learning For Physics).
- Automated Chemical Synthesis. This component intersects significantly with robotics research represented at NeurIPS, and includes several parts of real-world robotic systems such as: managing control systems (e.g. Reinforcement Learning) and different sensor modalities (e.g. Computer Vision), as well as predictive models for various phenomena (e.g. Data-Based Prediction of Chemical Reactions).
- Automated Material Characterization. This component intersects heavily with a diverse set of supervised learning techniques that are well-represented at NeurIPS such as: computer vision for microscopy images and automated machine learning based analysis of data generated from different kinds of instruments (e.g. X-Ray based diffraction data for determining material structure).
Rapid and in-depth exploration of the chemical space of high molecular weight synthetic polypeptides via the ring-opening polymerization (ROP) of N-carboxyanhydride (NCA) is a viable approach towards protein mimics and functional biomaterials. Here, we develop an efficient chemistry for the high throughput diversification of polypeptides based on a click-like reaction between selenolate and various electrophiles in aqueous solutions. With the assistance of automation and machine learning, iterative exploration of the random heteropolypeptides (RHPs) library efficiently and effectively identifies hit materials from a model system of which we have little prior knowledge. This automated and high-throughput platform provides a useful interface between wet and dry experiment, which would accelerate the discovery of new polypeptide materials for unmet challenges such as de novo design of artificial enzyme, biomacromolecule delivery, and understanding of intrinsically disordered proteins.
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In many applications of machine learning, like drug discovery and material design, the goal is to generate candidates that simultaneously maximize a set of objectives. As these objectives are often conflicting, there is no single candidate that simultaneously maximizes all objectives, but rather a set of Pareto-optimal candidates where one objective cannot be improved without worsening another. Moreover, these objectives, when considered in practice are often under-specified, making diversity of candidates a key consideration. The existing multi-objective optimization methods focus predominantly on covering the Pareto front, failing to capture diversity in the space of candidates. Motivated by the success of GFlowNets for generation of diverse candidates in a single objective setting, in this paper we consider Multi-Objective GFlowNets (MOGFNs). MOGFNs consist of a Conditional GFlowNet which models a family of single-objective sub-problems derived by decomposing the multi-objective optimization problem. Our work is the first to empirically demonstrate conditional GFlowNets. Through a series of experiments on synthetic as well as practically relevant material design and drug discovery tasks, we empirically demonstrate that MOGFNs outperform existing methods in terms of hypervolume, R2-distance and candidate diversity. We also demonstrate the effectiveness of MOGFNs over existing methods in active learning settings.
Bayesian optimization (BO) has proven to be effective approach for guiding sample-efficient exploration of materials domains and is increasingly being used in automated materials optimization set-ups. However, when exploring novel materials, sample quality may vary unexpectedly, which can even invalidate the optimization procedure if it remains undetected. This issue limits the use of highly-automated optimization loops, especially in high-dimensional materials spaces with a lot of samples. Sample quality may be hard to define unequivocally for a machine but human scientists are usually good at judging sample quality, at least on a cursory yet often sufficient level. In this work, we demonstrate that humans can be added into the BO loop as experts to comment on the sample quality, which results in more trustworthy BO results. We implemented human-in-the-loop BO via a data fusion approach and applied virtual BO cycles on experimental perovskite film stability data from literature. The human-in-the-loop approach facilitates automated materials design and characterization by reducing the occurrence of invalid optimization results.
One of the most important problems in rational design of batteries is predicting the properties of the Solid Electrolyte Interphase, which (for a metallic anode) is the part of the battery where metallic and non-metallic components come into contact. However, there is a fundamental problem with predicting the properties of such a mixed material: the two components are best simulated with incompatible levels of density functional theory. Pure functionals perform well for metallic properties, while hybrid or long-range-corrected density functionals perform better for molecular properties and reaction barriers. We demonstrate a simple method to obviate this conflict by training a machine learning potential energy surface using both levels of theory via transfer learning. We further show that the resulting model is more accurate than models trained individually to these levels of theory, allowing more accurate property prediction and potentially faster materials discovery.
Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on multiple correlated model outputs as objectives and/or constraints. To better solve these problems, we propose a recalibrated bootstrap method to generate multivariate prediction intervals for bagged models and show that it is well-calibrated. We apply the recalibrated bootstrap to a simulated sequential learning problem with multiple objectives and show that it leads to a marked decrease in the number of iterations required to find a satisfactory candidate. This indicates that the recalibrated bootstrap could be a valuable tool for practitioners using machine learning to optimize systems with multiple competing targets.
The design and development of nanomanufacturing processes and equipment; application of nanoscale science in engineering; engineering of thin films, nanotubes and nanowires; manufacturing and assembly of nanostructured materials; design of micro/nanoscale machine elements for mechanical sensors and energy systems
This study presents the prediction of the ultimate load carrying capacity of cold formed steel (CFS) built-up back-to-back channel columns having fixed boundary conditions under axial compressive load. There were 60 non-linear finite element models developed in ABAQUS, 12 of which were validated using experimental data while the remaining 48 models were validated based on AISI specification design standards. The finite element analysis and experimental results were also compared to the ultimate strength from the AISI specification. A parametric study was carried out using the validated finite element model in addition to the use of machine learning models to predict the ultimate load of CFS sections. Here, the machine learning models such as Artificial Neural Network (ANN), Gradient Tree Boosting (GTB) and Multivariate Adaptive Regression Splines (MARS) were developed for comparative evaluation of model predictions. Based on the performance evaluation using several statistical indices, MARS and GTB models were found to provide relatively accuratepredictions of the ultimate load of CFS sections. e24fc04721
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