5,607 research outputs found
Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
Compounds of low lattice thermal conductivity (LTC) are essential for seeking
thermoelectric materials with high conversion efficiency. Some strategies have
been used to decrease LTC. However, such trials have yielded successes only
within a limited exploration space. Here we report the virtual screening of a
library containing 54,779 compounds. Our strategy is to search the library
through Bayesian optimization using for the initial data the LTC obtained from
first-principles anharmonic lattice dynamics calculations for a set of 101
compounds. We discovered 221 materials with very low LTC. Two of them have even
an electronic band gap < 1 eV, what makes them exceptional candidates for
thermoelectric applications. In addition to those newly discovered
thermoelectric materials, the present strategy is believed to be powerful for
many other applications in which chemistry of materials are required to be
optimized.Comment: 6 pages, 4 figure
Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery
Molecular discovery is a multi-objective optimization problem that requires
identifying a molecule or set of molecules that balance multiple, often
competing, properties. Multi-objective molecular design is commonly addressed
by combining properties of interest into a single objective function using
scalarization, which imposes assumptions about relative importance and uncovers
little about the trade-offs between objectives. In contrast to scalarization,
Pareto optimization does not require knowledge of relative importance and
reveals the trade-offs between objectives. However, it introduces additional
considerations in algorithm design. In this review, we describe pool-based and
de novo generative approaches to multi-objective molecular discovery with a
focus on Pareto optimization algorithms. We show how pool-based molecular
discovery is a relatively direct extension of multi-objective Bayesian
optimization and how the plethora of different generative models extend from
single-objective to multi-objective optimization in similar ways using
non-dominated sorting in the reward function (reinforcement learning) or to
select molecules for retraining (distribution learning) or propagation (genetic
algorithms). Finally, we discuss some remaining challenges and opportunities in
the field, emphasizing the opportunity to adopt Bayesian optimization
techniques into multi-objective de novo design
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Uncertainty quantification (UQ) is an important component of molecular
property prediction, particularly for drug discovery applications where model
predictions direct experimental design and where unanticipated imprecision
wastes valuable time and resources. The need for UQ is especially acute for
neural models, which are becoming increasingly standard yet are challenging to
interpret. While several approaches to UQ have been proposed in the literature,
there is no clear consensus on the comparative performance of these models. In
this paper, we study this question in the context of regression tasks. We
systematically evaluate several methods on five benchmark datasets using
multiple complementary performance metrics. Our experiments show that none of
the methods we tested is unequivocally superior to all others, and none
produces a particularly reliable ranking of errors across multiple datasets.
While we believe these results show that existing UQ methods are not sufficient
for all common use-cases and demonstrate the benefits of further research, we
conclude with a practical recommendation as to which existing techniques seem
to perform well relative to others
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